Latest blogs
Created by - Skillzam Team
Monolithic vs. Micro-services: An Overview
In the world of software development, there are two main architectures that are commonly used to build applications: monolithic and micro-services. Both have their strengths and weaknesses, and choosing the right architecture for a particular project depends on a variety of factors, including the size and complexity of the application, the development team's expertise, and the organization's goals and priorities. In this article, we'll take a closer look at the differences between monolithic and micro-services architectures, their respective strengths and advantages, and how to choose between them for your next project.Monolithic Architecture Monolithic architecture is the traditional approach to building software applications, where all the components of the application are tightly coupled and deployed as a single unit. This means that the entire application, including the user interface, business logic, and data access layer, is packaged and deployed as a single executable or binary file. When changes are made to any part of the application, the entire application must be rebuilt, tested, and deployed.Advantages: Simplicity: Monolithic architecture is simple to develop and maintain as the entire application is housed in a single executable file. Performance: As all components of an application are present in a single file, there is no communication overhead between different components, which make the application fast and efficient. Simple deployment: Monolithic technology is not as complex as micro-service technology. Monolithic applications have fewer moving parts, so there are fewer components to manage and fix together. All in all, the self-contained nature of a monolithic app makes it easier to deploy, manage, and maintain than a micro-services solution. Security: Security is easier to implement in monolithic applications as the entire application is running on a single machine, making it easier to control access to different components. Disadvantages: Scalability: Because monolithic architecture software is tightly coupled, it can be hard to scale. As your codebase grows and/or you want to add new features, you need to drag the entire architecture up with you. Even if you only want to boost or alter a single function, the entire application needs changing. This isn’t just time and resource-consuming but can also disrupt your continuous delivery. Limited Flexibility: Monolithic architecture can be limiting in terms of the flexibility it offers, as all components are tightly integrated, making it difficult to make changes to one component without impacting the others. Large Codebase: As all components of an application are present in a single file, the codebase of a monolithic application can become large and difficult to manage. Single Point of Failure: As the entire application is running on a single machine, any failure in one component can bring down the entire application. Development and Deployment Time: Monolithic architecture can be slow to develop and deploy, especially when the application is large, as changes made to one component can require recompiling the entire application. Micro-services ArchitectureMicro-services architecture, on the other hand, is a newer approach to building software applications that involves breaking down the application into small, independent services that communicate with each other over a network. Each service is designed to perform a specific function and can be developed, tested, and deployed independently of the other services. Advantages: Scalability: Micro-services architecture allows for easy scaling of individual services, making it easy to handle high traffic and large-scale applications. Flexibility: Micro-services architecture allows for greater flexibility in development and deployment, as each service can be developed and deployed independently of the others. Resilience: In micro-services architecture, if one service fails, it does not bring down the entire application, as other services continue to run. Technology Diversity: Micro-services architecture allows for the use of different technologies and programming languages for each service, enabling developers to choose the best tool for each task. Disadvantages: Complexity: Micro-services architecture can be complex to design, develop, test, and maintain as it involves multiple independent services communicating with each other. Overhead: Micro-services architecture requires additional overhead in terms of communication between services and API management. · High infrastructure costs: Each new micro-service can have its own cost for test suite, deployment playbooks, hosting infrastructure, monitoring tools, and more. Distributed System: Micro-services architecture creates a distributed system, which can make it challenging to manage and monitor, especially when dealing with failures and debugging. Integration Testing: Integration testing can be challenging in micro-services architecture as it involves multiple services interacting with each other, making it difficult to isolate problems. Technologies involved in implementing Monolithic and Micro-services ArchitectureThe underlying technology stack for implementing monolithic and micro-services architecture can vary, depending on the specific needs and goals of the organization. However, there are some common technologies and tools that are typically used for each architecture.For monolithic architecture, the technology stack typically includes a single codebase or repository, a web application framework, and a relational database management system. The web application framework is used to handle HTTP requests and responses, and the relational database is used to store data. Examples of popular web application frameworks for monolithic architecture include Ruby on Rails, Django, and Laravel.For micro-services architecture, the technology stack typically includes multiple independent services that communicate with each other through APIs. Each service may have its own technology stack, depending on its specific requirements. However, some common technologies and tools used in micro-services architecture include containerization platforms such as Docker and Kubernetes, service discovery tools such as Consul or Etcd, and message brokers like RabbitMQ or Kafka. Additionally, micro-services architecture often relies on lightweight and fast web frameworks like Node.js, Flask, or Dropwizard, and NoSQL databases like MongoDB, Cassandra, or DynamoDB.Regardless of the specific technology stack used, implementing both monolithic and micro-services architecture requires a good understanding of software design principles, distributed systems, and scalable infrastructure. Differences Summary Here is a table outlining the main differences between monolithic and micro-services architecture: Monolithic Architecture Micro-services Architecture Deployment Deployed as a single unit Deployed as independent services Scalability Horizontal scaling is difficult due to tight coupling Horizontal scaling is easy as services are independent Complexity Low complexity High complexity Development Simple and easy to develop, test, and deploy More complex development and deployment processes due to independent services Maintenance Small changes can have cascading effects Services can be updated independently Expertise Required Lower expertise required in distributed systems Higher expertise required in distributed systems Flexibility Low flexibility High flexibility Communication Tight coupling of components Loose coupling through APIs Overall, monolithic architecture is simpler and easier to develop, but it becomes more difficult to scale and maintain as an application grows in size and complexity. Micro-services architecture offers greater scalability and flexibility, but it requires a higher degree of expertise in distributed systems and can be more complex to develop and deploy. Choosing Between Monolithic and Micro-services ArchitectureChoosing between monolithic and micro-services architecture depends on a variety of factors, including the size and complexity of the application, the development team's expertise, and the organization's goals and priorities.For small to medium-sized applications or development teams that are just starting out, monolithic architecture may be the best choice. Monolithic architecture is simple and easy to develop, test, and deploy, making it a good choice for applications that don't require a high degree of scalability or flexibility.However, as an application grows in size and complexity, or as the development team gains more expertise in building distributed systems, micro-services architecture may become a better choice. Micro-services architecture offers greater scalability and flexibility, allowing organizations to respond more quickly to changing demands and user needs. However, it also requires a higher degree of expertise and knowledge of distributed systems, making it a better choice for development teams that have experience with building and managing distributed systems. Ultimately, the choice between monolithic and micro-services architecture depends on the specific needs and goals of the organization. Both architectures have their strengths and weaknesses, and choosing the right one requires careful consideration of the tradeoffs between simplicity and complexity, scalability and flexibility, and ease of development and maintenance. The choice between the two architectures ultimately depends on the specific needs and goals of the organization.
More detailsPublished - Tue, 21 Mar 2023
Created by - Skillzam Team
ChatGPT: The Future of Chatbots
Chatbots have been around for decades, but their capabilities have grown significantly in recent years. With advancements in artificial intelligence and natural language processing, chatbots are now able to understand and respond to human language in a more natural and nuanced way. One of the most advanced chatbots in existence today is ChatGPT, a large language model developed by OpenAI.What is ChatGPT?ChatGPT is an AI-powered chatbot that is designed to engage in natural language conversations with humans. It is based on the GPT-3.5 architecture, which is a variant of the GPT-3 language model developed by OpenAI. GPT-3 is currently one of the most advanced natural language processing models in existence, and ChatGPT builds on this technology to create a chatbot that is capable of understanding and responding to a wide range of human queries.One of the key features of ChatGPT is its ability to generate human-like responses. Unlike traditional chatbots, which rely on pre-programmed responses to specific queries, ChatGPT uses machine learning algorithms to generate responses on the fly. This means that the chatbot is able to adapt its responses to match the tone and style of the user, creating a more natural and engaging conversation.How does ChatGPT work?ChatGPT is based on a deep learning neural network that is trained on a massive corpus of text data. This includes everything from books and articles to social media posts and online forums. By analyzing this data, the model is able to learn how humans use language and develop an understanding of common patterns and structures.When a user interacts with ChatGPT, the chatbot analyzes the text input and uses its neural network to generate a response. This response is based on the user's input, as well as any context or information that the chatbot has gathered from previous interactions. The model is able to generate responses that are grammatically correct and semantically relevant, while also taking into account the user's intent and any relevant information that they have provided.One of the key advantages of ChatGPT is its ability to generate responses that are more personalized and contextually relevant than traditional chatbots. By analyzing previous interactions and understanding the user's intent, the chatbot is able to provide more accurate and useful responses. This makes it a valuable tool for businesses and organizations that want to provide a more engaging and personalized customer experience.Applications of ChatGPTChatGPT has a wide range of potential applications, from customer service and support to language learning and education. Some of the key areas where ChatGPT can be used include: Customer service and support: ChatGPT can be used to provide personalized customer support and answer common queries in real-time. This can help businesses to reduce the workload on their support teams and improve the overall customer experience. Language learning: ChatGPT can be used to help people learn new languages by engaging in conversational practice. This can be particularly useful for people who are learning a new language but don't have access to a native speaker. Mental health support: ChatGPT can be used to provide mental health support and counseling to people who may be struggling with anxiety, depression, or other mental health issues. The chatbot can provide a non-judgmental listening ear and offer practical advice and resources. Personalized shopping assistance: ChatGPT can be used to provide personalized shopping assistance to customers, helping them to find products that meet their needs and preferences. Education: ChatGPT can be used to provide personalized learning experiences to students, offering tailored feedback and guidance based on their individual strengths and weaknesses. Challenges and Limitations of ChatGPTDespite its many advantages, ChatGPT still faces several challenges and limitations that must be addressed in order for it to reach its full potential. Bias: Like any machine learning model, ChatGPT is only as unbiased as the data it is trained on. If the data contains biases or stereotypes, the model is likely to replicate them in its responses. This can be a particular concern in areas like mental health support, where bias and stereotype can be harmful. Addressing this challenge will require ongoing efforts to improve the diversity and quality of the data used to train the model. Limited domain knowledge: While ChatGPT is capable of generating responses on a wide range of topics, its domain knowledge is limited to what it has learned from the data it has been trained on. This means that the chatbot may struggle to respond to queries outside of its domain, or provide inaccurate or incomplete information. Addressing this challenge will require ongoing efforts to improve the depth and breadth of the data used to train the model. Safety and security: ChatGPT is designed to engage in natural language conversations with humans, which means that it may be vulnerable to attacks or abuse. For example, it may be used to spread misinformation or engage in phishing scams. Addressing this challenge will require ongoing efforts to improve the safety and security of the chatbot, including measures like content moderation and user verification. Limited emotional intelligence: While ChatGPT is capable of generating responses that are grammatically correct and semantically relevant, it is still limited in its emotional intelligence. This means that it may struggle to understand and respond to emotional cues in the same way that a human would. Addressing this challenge will require ongoing efforts to improve the emotional intelligence of the chatbot, such as incorporating sentiment analysis and other emotional recognition techniques. Limited memory: ChatGPT is designed to analyze each query in isolation, which means that it may struggle to maintain a coherent conversation over time. This can be particularly challenging for longer or more complex conversations, where context and history are important. Addressing this challenge will require ongoing efforts to improve the memory and long-term learning capabilities of the chatbot.Energy consumption of ChatGPT queryThe energy consumption of each query on ChatGPT can vary widely depending on several factors such as the complexity of the query, the size of the model, and the computational resources available to process the query. However, it is generally known that large language models like GPT-3, which is the architecture that ChatGPT is based on, are known to be energy-intensive and require significant computational resources to operate.According to a study conducted by researchers at the University of Massachusetts Amherst, the energy consumption of a single inference pass on a GPT-3 model with 175 billion parameters can range from 3.2 kWh to 13 kWh depending on the hardware used to perform the inference. This energy consumption is significant when compared to other common everyday devices like smartphones or laptops, which typically consume less than 1 kWh per day.It's worth noting that ChatGPT is a cloud-based service that is hosted by OpenAI, which means that the energy consumption of each query will also depend on the energy efficiency of the data centers used to host the service. OpenAI has publicly committed to using renewable energy sources to power its data centers and has also implemented energy-efficient hardware and cooling systems to minimize the environmental impact of its operations. Overall, while the energy consumption of each query on ChatGPT may not be easily quantifiable, it is clear that large language models like GPT-3 are energy-intensive and require significant computational resources to operate. As such, it is important to consider the environmental impact of these models and work towards developing more energy-efficient and sustainable technologies for the future. Conclusion ChatGPT is a powerful and versatile chatbot that has the potential to revolutionize the way we interact with machines. Its ability to generate human-like responses and adapt to user intent and context make it a valuable tool for businesses and organizations across a wide range of industries. However, as with any emerging technology, ChatGPT also faces several challenges and limitations that must be addressed in order for it to reach its full potential. By working to improve the quality and diversity of the data used to train the model, as well as its emotional intelligence and memory capabilities, we can ensure that ChatGPT continues to push the boundaries of what is possible in the world of chatbots.
More detailsPublished - Tue, 21 Mar 2023
Created by - Skillzam Team
React - A JavaScript Library : Highly in-demand skill
From conception of an idea or business case to testing, a top-notch React developer can help you build interactive components for your website or web applications using the React development concepts.What is React.js? What does a React developer do? These are a few of the most common questions we see on the web. We’ll answer these questions and more in this blog.Let’s first explore the interesting world of React. Alternately spelled as React.js or ReactJS; React is a declarative, efficient, and flexible JavaScript library for building user interfaces. It lets you compose complex User Interfaces (UIs) from small and isolated pieces of code called “components”. Simply put, UI is a term that describes any element that allows users to interact and communicate with the information saved in a device (laptop, tablet, smartphone, etc.). The user interface is the primary point of contact between humans and modern computers. Keyboards, mouse, computer texts, and desktop layout are some of the best examples of UI.React was created by Jordan Walke, a software engineer at Facebook, who released an early prototype of React called "FaxJS". He was influenced by XHP, an HTML component library for PHP. It was first deployed on Facebook's News Feed in 2011 and later on Instagram in 2012. It was open-sourced at JSConf US in May 2013.Before ReactJS was introduced, developers back then were stuck building UIs using raw JavaScript or with jQuery (a React predecessor that focused less on UI). This meant that the development process took longer with more room for errors and bugs. To solve this problem, Facebook engineer Jordan Wilke created FaxJS (first React prototype) in 2011.React makes it simple and easy to build websites and apps, create UI test cases, reuse existing codes on the website to its mobile counterpart, and improve the UI and performance of web applications. It’s versatile enough to use where JavaScript is required.Today, more and more enterprises benefit from ReactJS, a testament to its burgeoning popularity in software development. Some of the high-traffic websites that use React are Facebook, Netflix, Airbnb, Instagram, Uber, Reddit, Salesforce and so on.Along with the upswing in React usage, is the surge in demand for a React.js developer. A highly skilled React developer is involved in the entire stages of the development and maintenance of user interface components. What does a React Developer do?In a nutshell, a React developer designs and implements user-facing features for websites and applications using React.js.They are known as front-end developers, a sub-group of developers that manage everything that users see on their web browsers or applications. In other words, they are responsible for the look and feel of a website or application to ensure a seamless experience for users.The menu buttons, online forms, the graphic icons and images, voice recognition (Siri and Cortana), and gesture-driven interface (example: pinching the smartphone screen to zoom in and out) are just a few of the exciting things that a React developer can do.React developers use their knowledge in JavaScript, CSS, HTML, and other programming languages and incorporate them into the ReactJS open-source library ecosystem.Generally, they work with software testing specialists to test the codes before the web application or website goes live. React developers may also work with a team of developers, programmers, web designers, and project managers to work closely on a client’s software project. React Developer SkillsIndeed, React is a powerful tool for front-end development. Many organizations agree that having a solid background in React is essential to dominating this technology-driven industry.To level up in this competitive field, a React.js candidate should have the essential skills in these React developer tools: Web technologies – An in-depth knowledge and experience working with web technologies such as JavaScript, HTML, CSS, TypeScript, C#, SASS, Stylus, Stylus, Python, and LESS. React tools – Strong proficiency in Enzyme, Webpack, Redux, and Flux. Web frameworks – Proven expertise in different frameworks like React Native, AngularJS, Angular2, VueJS, SocketIO, Backbone, KnockoutJS, Svelte, and Bootstrap. Open-source Libraries – Demonstrable experience with JQuery, MomentJS, html5sql.js, RequireJS, and Loads. Software testing tools – A good working knowledge in testing codes using Mocha, Jest, Unit-tests, TDD, Karma, VCS, and Selenium (base). Databases – A familiarity working with different databases such as MySQL, NoSQL(e.g., MongoDB), PostgreSQL, Redis is recommended. Code repositories – Working experience using GitHub, Bitbucket, RhodeCode, and devZing Subversion Cloud management – A strong background in different cloud platforms like Amazon AWS, Microsoft Azure, OpenStack Apache, and Google Cloud Platform Project management – It’s a great advantage if they know how to manage projects using JIRA, Basecamp, Trello, Asana, ClickUp, or Zoho Projects. React Developer SalaryNow for the fun part. Let’s get an estimate on the cost of hiring a React developer. Overall, React developers are one of the highest-paid among JavaScript developers. Their salary depends on: years of experience different roles held over time level of expertise special skills training and certifications geographic location The average ReactJS developer salary in the USA is $119,997 per year or $61.54 per hour. Entry level positions start at $99,890 per year while most experienced workers make up to $150,000 per year.
More detailsPublished - Fri, 13 May 2022
Created by - Skillzam Team
How to compose a powerful & impressive job seeking resume
There are many positions for professional developers, Tester, Analysts etc today. However, to secure an interview and get hired for your dream job, a strong resume is critical. Writing the perfect one is not easy. Many candidates wonder what their resume should look like and what it should include, and this can vary because it represents different things to different people. To some, it’s a document that lists your work experience, to others, it’s a document that lists everything you’ve ever done. In the end, a technology skill based resume is one of the keys to success, despite variations in appearance.“Every corporate job opening on average attracts 250 resumes,” says Glassdoor. Only 4-6 of those applicants will receive an interview. That’s why the art of crafting a resume must not be approached lightly. We’ve outlined some key pointers to ensure that your resume stands out, helping you impress recruiters and find your dream job. Some key pointer to build impressive resume:1) Determine Your ObjectiveBefore you start looking for an entry level developer, tester, Analyst job, you should first determine what kind of expert you want to be. This will help you identify your goal. Take the time to ask and answer this question, and then determine the area of technology, you want to work in—for instance, you may want to showcase that you’re skilled in time management and have expertise in JavaScript or Python or Selenium. Once you’ve been able to identify your career goals, you’ll have a direction for your resume and job search. 2) List Your Best AchievementsNot all of your achievements should be included in your resume, but you should mention those that are applicable to the job you’re applying for. If you worked in retail super-store or in a non-profit as a teenager, this may not be an achievement that is essential to include in a Developer’s or Tester’s resume. However, you may have transferable skills such as prioritization, communication, and leadership. Each of these will make you an excellent candidate for a wide range of Technology roles. 3) Choose a Good Format and OrderWhat will carry the most weight for the role you’re applying to? What should be front and center on your resume? What’s going to persuade your future employer to choose you over other candidates? These are critical questions to ask yourself, when crafting a resume. Always begin with your elevator pitch, or objective statement, which contains a short overview of who you are, while convincing the reader that you are the best candidate for the role.Next, list your abilities. For developer jobs, this includes the programming languages you’re well versed in, or for Tester, this includes the tools, processes or frameworks but it doesn’t mean you need to list each skill. For instance, proficiency in Microsoft Office is a skill that most employers expect you to have, so it can be left out. Instead, attempt to concentrate on your top five or six technical strengths. Then, list your experience, but as we mentioned, only the experience that is directly linked to the work you’re applying for. Next, you’ll want to highlight projects that you’ve accomplished while learning to code or test or analyze. And lastly, list your education, beginning with your latest achievement and working backward. 4) Craft a Solid Opening StatementLike any other document you read, when a hiring manager first looks at a candidate’s resume, they begin at the top and work their way down. That’s why we suggest developing a powerful opening statement that will leave a good impression with the reader. This section should showcase your strengths and passions, for example: “An ambitious problem-solver with demonstrated expertise in front-end development. Passionate about generating work that exceeds expectations and speaks to the mission of the client.”As mentioned in Business Insider, “the skills segment of the CV is, according to most employers, the most important.” Why? Because many candidates may lack experience, but their skills and abilities match what the employer is looking for. That’s why the “skills” that you have mastered through Skillzam courses, section should be positioned at the top of the resume—right under your objective statement—to grab the reader’s attention. 5) Tailor Your Resume Each TimeDuring your search, you’ll likely be applying to hundreds of jobs. It is essential to read through each job description and tailor your resume to reflect the position and the needs of the company you’re applying to. Make use of all your knowledge and never sell yourself short.In addition, use keywords that are vital to your work. Many recruiters use an applicant tracking system (ATS) that filters keywords through resumes, eventually determining which ones are the best match. When selecting which keywords to use, refer to the job description and business website for each position. You may need to change your resume for every job you apply to depending on what keywords are used.In the experience section, it’s not necessary to list every responsibility of each job you’ve held, but rather the responsibilities that are most relevant to the jobs you are applying for. Also, be sure to tailor your resume to the culture of the company itself. Once you’ve completed research on work related sites like Workzam or Glassdoor, you will start to notice phrases like “collaboration” and “communication.” Use these keywords and provide examples of how you fit into the company’s workplace culture.Once you master the art of constructing a strong resume, you’ll get through the pre-interview gatekeepers and into the interview stage, bringing you one step closer to your dream career or better job.
More detailsPublished - Tue, 10 May 2022
Created by - Skillzam Team
Top 10 popular programming languages for 2022
The top 10 most in-demand programming languages for 2022You don’t want to waste your time. If you’re going to put aside the time and energy needed to learn new programming languages, you want to make sure, without a doubt, that the ones you choose are the most in-demand programming languages on the market. After all, if you’re trying to start (or advance) a career in software development, you’ll need to know which programming languages are popular, which are useful and which are up-and-coming on the market — and then set yourself up to learn them. Not sure where to start? Don’t worry; we’ve already done the heavy lifting for you, and looked through developer and market data from the past several years to identify trends and draw conclusions that will help you plan for your programming education. In this article, we’ll walk you through the Top 10 most in-demand programming .But before we get started, let’s recap how we built this list in the first place. When we sat down to figure out what the most in-demand programming languages for 2022 might be, we had two primary research questions:Which languages are in demand right now? Which ones are growing in popularity and will be in demand soon?You can probably guess the rationale behind our first question. While following trends isn’t always the best way to figure out which languages to prioritize, a language’s popularity can indicate its prominence in the market. Here’s a general rule of thumb for you — the more used or common a language is, the more likely it is that a developer (i.e., you!) will need to deal with it at work.Next, we considered which skills are poised to become the most sought-after programming languages on the market. To do so, we dug into Stack Overflow’s developer surveys from the last two years and focused on: The languages that developers report they use and want to continue using The languages that developers aren’t using yet, but are interested in usingBy looking at these trends, we aimed to gain a better understanding of which languages will become popular in the years ahead. We found that Rust, Python and Typescript all topped Stack Overflow’s list for the most-loved languages in 2019 and 2020, with only slight shuffling across years. Similarly, Python, JavaScript and Go ruled the top three of Stack’s Most Wanted lists for both years. Given their prevalence in developers’ minds, the odds of these five languages increasing in popularity is high — and are therefore worth including on our list. However, where we ranked each language depended on other factors. For example, because Python has consistently held a top-five spot on Stack Overflow’s most commonly-used, -loved and -wanted lists, we chose to rank it higher than Rust, which, while ranking #1 on Stack’s most-loved list, languishes at #19 on the organization’s common-languages list. Now that we’ve covered our process, let’s start answering the question at hand: What are the most in-demand coding languages for 2022? The Most In-Demand Programming Languages for 20221. JavaScriptWhat this language is used for: Web developmentGame developmentMobile apps Building web serversAccording to Stack Overflow’s 2020 Developer Survey, JavaScript currently stands as the most commonly-used language in the world (69.7%), followed by HTML/CSS (62.4%), SQL (56.9%), Python (41.6%) and Java (38.4%). JavaScript is used to manage the behavior of web pages. With it, coders can create dynamic web elements such as animated graphics, interactive maps, clickable buttons and more. Programmers who use HTML, CSS and JavaScript in tandem obtain a higher level of website control and can provide a better user experience in terms of navigation and readability. JavaScript is the most common coding language in use today around the world. This is for a good reason: most web browsers utilize it and it’s one of the easiest languages to learn. JavaScript requires almost no prior coding knowledge — once you start learning, you can practice and play with it immediately. Moreover, because the language is so ubiquitous, there are countless communities, courses and avenues of professional support available online. This support, in addition to the language’s top-notch usability, makes JavaScript number one on our list of the most in-demand programming languages. 2. PythonWhat this language is used for:Back end development Data science App developmentPython is a general-purpose programming language that empowers developers to use several different programming styles (i.e., functional, object-oriented, reflective, etc.) when creating programs. Several popular digital tools and platforms were developed with Python, including YouTube, Google Search and iRobot machines. It is also, according to TIOBE Programming Community index, the most popular programming language of 2022.As one of the more easy-to-learn and -use languages, Python is ideal for beginners and experienced coders alike. The language comes with an extensive library that supports common commands and tasks. Its interactive qualities allow programmers to test code as they go, reducing the amount of time wasted on creating and testing long sections of code. That said, even advanced users would benefit from adding Python to their mental catalog of programming languages, Python is easily one of the most marketable and in-demand programming languages of 2022. 3. HTMLWhat this language is used for: Web documents Website development Website maintenanceHTML stands for HyperText Markup Language. Don’t let the complicated-sounding name fool you, though; HTML is one of the most accessible stepping stones into the world of programming. Technically, HTML is a markup language, which means that it is responsible for formatting the appearance of information on a website. Essentially, HTML is used to describe web pages with ordinary text. It doesn’t have the same functionality as other programming languages in this list and is limited to creating and structuring text on a site. Sections, headings, links and paragraphs are all part of the HTML domain. As of 2020, HTML shares its #2 spot on Stack Overflow’s list of the most commonly used languages in the world with CSS. 4. CSSWhat this language is used for: Web documents Website development Website designCSS, or cascading style sheets, is usually applied in conjunction with HTML and governs the site’s appearance. While HTML organizes site text into chunks, CSS is responsible for determining the size, color and position of all page elements. CSS is convenient, too; the cascading part of the name means that an applied style will cascade down from parent elements to all children elements across the site. This feature means that once users determine aesthetics for the main parent, they won’t have to manually repeat their code across a website. Moreover, the delegation of site organization to HTML and aesthetics to CSS means that users don’t have to completely rewrite a web page just to change a color. CSS is an approachable language that allows beginning programmers to dip their toes in the metaphorical coding pool. If you’re new to coding, there’s no reason not to learn CSS before tackling more complex languages! 5. JavaWhat this language is used for: E-commerce FinanceApp developmentJava is a general use and object-oriented programming language. In object-oriented programming, developers create objects that encompass functions and data, which can then be used to provide structure for programs and applications. Java currently ranks as third most on TIOBE Programming Community index, the most popular programming language of 2022 and has held the #5 spot on Stack Overflow’s list of the most commonly used languages for two years. Java’s popularity is for good reason; this language is relatively easy to learn and use, boasts incredible security and can handle massive amounts of data. These features make Java an ideal language for the online finance sector, and it is often applied in industries such as banking, billing and the stock market. The versatility of the language, however, is what learners find really appealing. Touted as a “write-once, run-anywhere” language, Java can effectively run on any operating system, regardless of which OS was used to write the original code. It is thus ideal for writing apps not only for mobile phones and computers, but also remote processors, sensors and a variety of other consumer products. 6. SQLWhat this language is used for: Database management Sales reports Business managementSQL, or Structured Query Language, is a language that allows programmers to query and manipulate databases. As a domain-specific language, it is designed mainly for managing data within an RDBMS (relational database management system). Put simply, SQL can locate and retrieve data from a database, as well as update, add or remove records. While SQL is highly functional, it tends to work better with small databases and doesn’t always lend itself to managing expansive ones. That said, SQL still ranks as the third-most-used language in the programming industry, with over half (54.7%) of surveyed developers reporting that they use it. 7. NoSQLWhat this language is used for: Database management Sales reports Business managementNoSQL, or Non-relational SQL, was created to improve SQL’s scalability while retaining the other language’s ease of use. Remember, SQL utilizes relational database/stream management systems that keep data in tables and allows users to manipulate and extract data. NoSQL databases, on the other hand, don’t use tables and can be more useful than their predecessors for specific applications, such as storing data in a hierarchical network or supporting large-scale, cloud-based applications. Because these languages are both so versatile, they rank high on our list of the most in-demand coding languages. 8. C#What this language is used for: Game developmentDesktop/web/mobile apps VRAlso stylized as C Sharp, this language belongs to the object-oriented family of programming languages. C# was released in 2002 by Microsoft and stands today as a much-loved improvement on the C++ coding language. As a general-purpose language, C# is growing in popularity for developing web and desktop applications; according to TIOBE Programming Community index, it is the fifth most popular language. There’s no denying that it is one of the most in-demand coding languages for the upcoming year; however, there are other reasons to have this skill on your radar.As with other popular languages, an enormous community works with C# and offers support to new learners. Because of this, learning C# may be easier than attempting to learn some of the newer and less-documented languages. Plus, C# is ideal for building ever-more-popular mobile apps and games. There’s little doubt that this language will continue to be useful in the coming years. 9. C C is a general-purpose programming language that is designed to work efficiently on games, graphics, and enterprise applications. It offers amazing features like:· Presence of inbuilt functions and operators.· It is extendable.· Enhanced speed and efficiency.· The top companies using C are GitHub, Telegram messenger, MasterCard, etc.It is highly in demand despite the emergence of new programming languages. C provides the opportunities of building a successful career. 10. GoWhat this language is used for:System/network programming Audio/video editing Big DataDeveloped at Google in 2007, Go is a top-tier programming language. What makes Go really shine is its efficiency; it is capable of executing several processes concurrently. And as far as programming languages go, it has an extensive “vocabulary,” meaning it can display more information than other languages. Though it uses a similar syntax to C, Go is a standout language that provides top-notch memory safety and management features. Additionally, the language’s structural typing capabilities allow for a great deal of functionality and dynamism. Final ThoughtsWhether you are an established coder or just starting to look into the industry, learning a new language is one of the best ways to advance your programming career. But how can you start your educational journey? Your first step depends on you, your schedule and the resources you have at hand. A college program, for example, will provide you with a comprehensive education in the theory and practice of programming — however, the typical undergraduate program also demands four years of full-time study and tens of thousands of dollars in tuition funding. If you want a faster and less expensive educational experience that focuses on skills-based learning, you may want to consider a coding boot camp of SKILLZAM. Coding boot camps equip you with the hands-on skills and language proficiencies you’ll need to land an entry-level job in the field — all within three to six months. If you’re looking for the cheapest educational course and don’t mind taking on the responsibility of self-teaching, you may want to consider learning necessary coding skills via SKILLZAM online coursesWe hope that this list of the most in-demand programming languages for 2022 will help you begin your journey!
More detailsPublished - Sat, 30 Apr 2022
Created by - Skillzam Team
What does it takes to excel in the profession of Data Science
Today’s data science professionals are in high demand across a number of fields ranging from business operations and financial services to healthcare, science and more. A data scientist is an expert who uses data to extract valuable business insights. These professionals should have extensive knowledge related to computer science, data visualization and data mining along with statistics and machine learning. However, it all starts with learning the fundamentals. What You Should Learn: 1) Coding A data scientist should learn to code and create programs. They should have a solid understanding of basic coding languages, advanced analytical platforms and front end web visualization. For example:PythonPython is becoming an increasingly popular programming language. The platform is useful for a variety of processes performed by data scientists. Python’s versatility enables users to accomplish a variety of tasks that might include creating data sets or importing SQL tables. The platform is also recognized as being easy to pick up, making it a good choice for new data professionals because it helps at every stage of your career. New data analysts can learn Python quickly, but Python still has exceptional value for experienced professionals. While programmers rely on Python in many established fields, data analysts can also use Python for emerging processes.Python can even help to prepare you to learn other skills and languages down the line. Taken together, those factors make Python a great choice if you’re looking to learn a programming language. The Python platform is also open-source, making it free to install, so you don’t have to pay anything to start honing your Python skills. It is also known for its strong online community. The Python online community provides support, education, interaction and projects. There is also a great deal of potential for Python to become a common language for producing web-based analytics products and data science. SQLSQL is often a required skill for data scientists and is used to accomplish various functions that include adding, deleting or extracting information from databases. SQL also has the capability to perform analytical tasks. By using the platform’s precise commands, users are able to perform inquiries more quickly. Because of the prevalence of databases in today’s world, data professionals should at least have a basic familiarity with SQL. There is a growing demand for those who are experts in databases, so you can even specialize in SQL developing. Whether you want to turn SQL into a career or just want to supplement your programming knowledge, you’ll appreciate a quick introduction to the highlights of SQL.Before getting started with SQL, you should understand what exactly it is. As mentioned, it’s a language that lets us connect with databases. This is key since data is a crucial component of mobile and web applications, from profile information to those we follow on social media to cookies. Applications and websites use databases to hold the data. Professionals use SQL to interact with that data via a programming language. JavaScriptJavaScript is considered by many to be the scripting language of the web. This is a programming language or scripting that makes it possible to implement complex actions on a web page. JavaScript is part of nearly everything a website does that is more advanced than showing static information. JavaScript is incredibly versatile and can be used for both server-side and client-side language. Although not always the case, many Data Analytics boot camps include JavaScript in their curriculum.JavaScript builds on the web technologies of CSS and HTML, which are the other standards. As a quick reminder, HTML is a markup language that defines paragraphs and provides structure. CSS incorporates style rules, applying them to HTML. JavaScript allows for many other things, such as animating images and controlling multimedia. HTMLHTML stands for HyperText Markup Language and is the code that structures content on a website. It is important to note that HTML is not a type of coding language. Instead, it is a markup language. As a markup language, HTML outlines the structure for content. HTML includes various elements that you use to wrap or enclose portions of the content, with the goal of having it act or appear the way you want. The tags can do things such as add hyperlinks, change font size and italicize words, among others.Having the knowledge to build your own website with HTML gives you the chance to stand out from the crowd with an authentic, hand-crafted representation of your business — or any business for that matter. This powerful coding language is not only helpful to web developers, and you just might find yourself in a position where you may need them in a professional data setting. 2) Data VisualizationThe amount of data that businesses and industries produce today is greater than ever before. However, in order to be useful, the data must be converted into a format that is easily comprehended. A data scientist uses D3.js, ggplot, Matplotlib, Tableau and other tools for this purpose. By organizing and transforming data into usable formats, companies are able to make informed decisions based on the results. 3) Working With Unstructured DataUnstructured data refers to audio or visual feeds, blog posts, customer reviews and social media posts. The data included within multimedia formats often requires an ability to identify, analyze and manipulate the data in order to obtain critical information that may be beneficial to a company or industry. 4) Artificial Intelligence and Machine LearningData scientists who can create programs with artificial intelligence may find a benefit from advancing the program’s ability to learn independently. The program can use decision trees, logistic regression and other algorithms to analyze data sets, make predictions or solve problems once the platform receives a sufficient amount of data.Machine learning is a powerful tool. When you teach a machine how to use an algorithm to identify patterns, it can use those patterns to predict outcomes without using any preconceived notions or pre-programmed rules. A machine can only improve its own learning by using the information it has been given, so machine learning isn’t successful unless users provide a diverse and large enough range of data. 5) MathematicsCalculus, linear algebra and statistics are areas of math that data scientists should know in order to create their own data analysis platforms. A background in statistics is particularly helpful for understanding statistical distributions, estimators and tests. The results of statistical findings are commonly required by companies in order to make informed decisions. What You Should Already Have: 1) Natural CuriosityA data scientist needs to possess an innate desire to obtain more knowledge or information. This drive motivates them to begin the educational process and learn the field of data science in order to find answers and insights contained within data sets. Curiosity drives the best scientists forward despite obstacles to achieve the end result. 2) Effective CommunicationThe diagnoses, predictions or other findings that data scientists are able to formulate mean nothing to a company if they cannot comprehend the results. While presenting illustrated data, a data scientist must be able to explain how the results impact the business. As such, data scientists must be able to clearly translate their findings in order to make them useful to a company. 3) Commitment to LearningThe most successful data professionals will have a strong understanding of the core technical data analyst skills needed to succeed in the field. For example, it is important to develop a solid grasp in today’s most in-demand data languages such as SQL, NoSQL, Postgres/pgAdmin and MongoDB. It is also beneficial to learn advanced specialties like statistical modeling, forecasting and prediction, pivot tables and VBA scripting.Having a solid understanding of today’s critical programming languages can help a data professional stand out in the job field. Those new to the field should gain a thorough understanding of core data analytics tools like NumPy, Pandas and Matplotlib. You might also consider learning specific libraries for interacting with web data such as Requests and BeautifulSoup.Finally, it is helpful for data professionals to learn the inner workings of web visualization. Building visualizations is of little benefit without an effective way to communicate the message. Consider exploring the core technologies of front-end web visualization such as Bootstrap, Dashboarding and Geomapping in addition to the coding specialties above. Learning to use these tools will help any analysts create new, interactive data visualizations that can be shared with everyone on the web. 4) Short and Long Term GoalsSetting long-term goals is just the first step toward your employment success; you should also create a strategy for achieving smaller, short-term goals. These will help keep you motivated and moving forward toward a career in Data Science. For example, if your long-term plan is to work in the industry within one year, you’ll need a tailored path to get there.Updating your resume and portfolio, networking with industry professionals and taking extra courses are just a few of the short-term goals you may want to consider adding to your action plan. 5) Ability to AdaptAs a data analyst, it is essential to identify the skills gap you have based on future goals. For example, the skills required for data professionals in marketing may be quite different from that of a data scientist in the financial services industry. A vital component of long-term success as a data professional is the ability to adapt your skills and knowledge to evolving business needs. 6) Collaboration SkillsData scientists do not work alone. They must combine their efforts with business and industry executives to seek out effective strategies. They may have to work with engineers or designers to manufacture better products or with marketing firms to create more effective campaigns. Scientists may share their insights with software engineers or key company stakeholders, and in both cases will need to tailor their communication strategies to do so effectively.
More detailsPublished - Sat, 30 Apr 2022
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Created by - Skillzam Team
Monolithic vs. Micro-services: An Overview
In the world of software development, there are two main architectures that are commonly used to build applications: monolithic and micro-services. Both have their strengths and weaknesses, and choosing the right architecture for a particular project depends on a variety of factors, including the size and complexity of the application, the development team's expertise, and the organization's goals and priorities. In this article, we'll take a closer look at the differences between monolithic and micro-services architectures, their respective strengths and advantages, and how to choose between them for your next project.Monolithic Architecture Monolithic architecture is the traditional approach to building software applications, where all the components of the application are tightly coupled and deployed as a single unit. This means that the entire application, including the user interface, business logic, and data access layer, is packaged and deployed as a single executable or binary file. When changes are made to any part of the application, the entire application must be rebuilt, tested, and deployed.Advantages: Simplicity: Monolithic architecture is simple to develop and maintain as the entire application is housed in a single executable file. Performance: As all components of an application are present in a single file, there is no communication overhead between different components, which make the application fast and efficient. Simple deployment: Monolithic technology is not as complex as micro-service technology. Monolithic applications have fewer moving parts, so there are fewer components to manage and fix together. All in all, the self-contained nature of a monolithic app makes it easier to deploy, manage, and maintain than a micro-services solution. Security: Security is easier to implement in monolithic applications as the entire application is running on a single machine, making it easier to control access to different components. Disadvantages: Scalability: Because monolithic architecture software is tightly coupled, it can be hard to scale. As your codebase grows and/or you want to add new features, you need to drag the entire architecture up with you. Even if you only want to boost or alter a single function, the entire application needs changing. This isn’t just time and resource-consuming but can also disrupt your continuous delivery. Limited Flexibility: Monolithic architecture can be limiting in terms of the flexibility it offers, as all components are tightly integrated, making it difficult to make changes to one component without impacting the others. Large Codebase: As all components of an application are present in a single file, the codebase of a monolithic application can become large and difficult to manage. Single Point of Failure: As the entire application is running on a single machine, any failure in one component can bring down the entire application. Development and Deployment Time: Monolithic architecture can be slow to develop and deploy, especially when the application is large, as changes made to one component can require recompiling the entire application. Micro-services ArchitectureMicro-services architecture, on the other hand, is a newer approach to building software applications that involves breaking down the application into small, independent services that communicate with each other over a network. Each service is designed to perform a specific function and can be developed, tested, and deployed independently of the other services. Advantages: Scalability: Micro-services architecture allows for easy scaling of individual services, making it easy to handle high traffic and large-scale applications. Flexibility: Micro-services architecture allows for greater flexibility in development and deployment, as each service can be developed and deployed independently of the others. Resilience: In micro-services architecture, if one service fails, it does not bring down the entire application, as other services continue to run. Technology Diversity: Micro-services architecture allows for the use of different technologies and programming languages for each service, enabling developers to choose the best tool for each task. Disadvantages: Complexity: Micro-services architecture can be complex to design, develop, test, and maintain as it involves multiple independent services communicating with each other. Overhead: Micro-services architecture requires additional overhead in terms of communication between services and API management. · High infrastructure costs: Each new micro-service can have its own cost for test suite, deployment playbooks, hosting infrastructure, monitoring tools, and more. Distributed System: Micro-services architecture creates a distributed system, which can make it challenging to manage and monitor, especially when dealing with failures and debugging. Integration Testing: Integration testing can be challenging in micro-services architecture as it involves multiple services interacting with each other, making it difficult to isolate problems. Technologies involved in implementing Monolithic and Micro-services ArchitectureThe underlying technology stack for implementing monolithic and micro-services architecture can vary, depending on the specific needs and goals of the organization. However, there are some common technologies and tools that are typically used for each architecture.For monolithic architecture, the technology stack typically includes a single codebase or repository, a web application framework, and a relational database management system. The web application framework is used to handle HTTP requests and responses, and the relational database is used to store data. Examples of popular web application frameworks for monolithic architecture include Ruby on Rails, Django, and Laravel.For micro-services architecture, the technology stack typically includes multiple independent services that communicate with each other through APIs. Each service may have its own technology stack, depending on its specific requirements. However, some common technologies and tools used in micro-services architecture include containerization platforms such as Docker and Kubernetes, service discovery tools such as Consul or Etcd, and message brokers like RabbitMQ or Kafka. Additionally, micro-services architecture often relies on lightweight and fast web frameworks like Node.js, Flask, or Dropwizard, and NoSQL databases like MongoDB, Cassandra, or DynamoDB.Regardless of the specific technology stack used, implementing both monolithic and micro-services architecture requires a good understanding of software design principles, distributed systems, and scalable infrastructure. Differences Summary Here is a table outlining the main differences between monolithic and micro-services architecture: Monolithic Architecture Micro-services Architecture Deployment Deployed as a single unit Deployed as independent services Scalability Horizontal scaling is difficult due to tight coupling Horizontal scaling is easy as services are independent Complexity Low complexity High complexity Development Simple and easy to develop, test, and deploy More complex development and deployment processes due to independent services Maintenance Small changes can have cascading effects Services can be updated independently Expertise Required Lower expertise required in distributed systems Higher expertise required in distributed systems Flexibility Low flexibility High flexibility Communication Tight coupling of components Loose coupling through APIs Overall, monolithic architecture is simpler and easier to develop, but it becomes more difficult to scale and maintain as an application grows in size and complexity. Micro-services architecture offers greater scalability and flexibility, but it requires a higher degree of expertise in distributed systems and can be more complex to develop and deploy. Choosing Between Monolithic and Micro-services ArchitectureChoosing between monolithic and micro-services architecture depends on a variety of factors, including the size and complexity of the application, the development team's expertise, and the organization's goals and priorities.For small to medium-sized applications or development teams that are just starting out, monolithic architecture may be the best choice. Monolithic architecture is simple and easy to develop, test, and deploy, making it a good choice for applications that don't require a high degree of scalability or flexibility.However, as an application grows in size and complexity, or as the development team gains more expertise in building distributed systems, micro-services architecture may become a better choice. Micro-services architecture offers greater scalability and flexibility, allowing organizations to respond more quickly to changing demands and user needs. However, it also requires a higher degree of expertise and knowledge of distributed systems, making it a better choice for development teams that have experience with building and managing distributed systems. Ultimately, the choice between monolithic and micro-services architecture depends on the specific needs and goals of the organization. Both architectures have their strengths and weaknesses, and choosing the right one requires careful consideration of the tradeoffs between simplicity and complexity, scalability and flexibility, and ease of development and maintenance. The choice between the two architectures ultimately depends on the specific needs and goals of the organization.
More detailsPublished - Tue, 21 Mar 2023
Created by - Skillzam Team
ChatGPT: The Future of Chatbots
Chatbots have been around for decades, but their capabilities have grown significantly in recent years. With advancements in artificial intelligence and natural language processing, chatbots are now able to understand and respond to human language in a more natural and nuanced way. One of the most advanced chatbots in existence today is ChatGPT, a large language model developed by OpenAI.What is ChatGPT?ChatGPT is an AI-powered chatbot that is designed to engage in natural language conversations with humans. It is based on the GPT-3.5 architecture, which is a variant of the GPT-3 language model developed by OpenAI. GPT-3 is currently one of the most advanced natural language processing models in existence, and ChatGPT builds on this technology to create a chatbot that is capable of understanding and responding to a wide range of human queries.One of the key features of ChatGPT is its ability to generate human-like responses. Unlike traditional chatbots, which rely on pre-programmed responses to specific queries, ChatGPT uses machine learning algorithms to generate responses on the fly. This means that the chatbot is able to adapt its responses to match the tone and style of the user, creating a more natural and engaging conversation.How does ChatGPT work?ChatGPT is based on a deep learning neural network that is trained on a massive corpus of text data. This includes everything from books and articles to social media posts and online forums. By analyzing this data, the model is able to learn how humans use language and develop an understanding of common patterns and structures.When a user interacts with ChatGPT, the chatbot analyzes the text input and uses its neural network to generate a response. This response is based on the user's input, as well as any context or information that the chatbot has gathered from previous interactions. The model is able to generate responses that are grammatically correct and semantically relevant, while also taking into account the user's intent and any relevant information that they have provided.One of the key advantages of ChatGPT is its ability to generate responses that are more personalized and contextually relevant than traditional chatbots. By analyzing previous interactions and understanding the user's intent, the chatbot is able to provide more accurate and useful responses. This makes it a valuable tool for businesses and organizations that want to provide a more engaging and personalized customer experience.Applications of ChatGPTChatGPT has a wide range of potential applications, from customer service and support to language learning and education. Some of the key areas where ChatGPT can be used include: Customer service and support: ChatGPT can be used to provide personalized customer support and answer common queries in real-time. This can help businesses to reduce the workload on their support teams and improve the overall customer experience. Language learning: ChatGPT can be used to help people learn new languages by engaging in conversational practice. This can be particularly useful for people who are learning a new language but don't have access to a native speaker. Mental health support: ChatGPT can be used to provide mental health support and counseling to people who may be struggling with anxiety, depression, or other mental health issues. The chatbot can provide a non-judgmental listening ear and offer practical advice and resources. Personalized shopping assistance: ChatGPT can be used to provide personalized shopping assistance to customers, helping them to find products that meet their needs and preferences. Education: ChatGPT can be used to provide personalized learning experiences to students, offering tailored feedback and guidance based on their individual strengths and weaknesses. Challenges and Limitations of ChatGPTDespite its many advantages, ChatGPT still faces several challenges and limitations that must be addressed in order for it to reach its full potential. Bias: Like any machine learning model, ChatGPT is only as unbiased as the data it is trained on. If the data contains biases or stereotypes, the model is likely to replicate them in its responses. This can be a particular concern in areas like mental health support, where bias and stereotype can be harmful. Addressing this challenge will require ongoing efforts to improve the diversity and quality of the data used to train the model. Limited domain knowledge: While ChatGPT is capable of generating responses on a wide range of topics, its domain knowledge is limited to what it has learned from the data it has been trained on. This means that the chatbot may struggle to respond to queries outside of its domain, or provide inaccurate or incomplete information. Addressing this challenge will require ongoing efforts to improve the depth and breadth of the data used to train the model. Safety and security: ChatGPT is designed to engage in natural language conversations with humans, which means that it may be vulnerable to attacks or abuse. For example, it may be used to spread misinformation or engage in phishing scams. Addressing this challenge will require ongoing efforts to improve the safety and security of the chatbot, including measures like content moderation and user verification. Limited emotional intelligence: While ChatGPT is capable of generating responses that are grammatically correct and semantically relevant, it is still limited in its emotional intelligence. This means that it may struggle to understand and respond to emotional cues in the same way that a human would. Addressing this challenge will require ongoing efforts to improve the emotional intelligence of the chatbot, such as incorporating sentiment analysis and other emotional recognition techniques. Limited memory: ChatGPT is designed to analyze each query in isolation, which means that it may struggle to maintain a coherent conversation over time. This can be particularly challenging for longer or more complex conversations, where context and history are important. Addressing this challenge will require ongoing efforts to improve the memory and long-term learning capabilities of the chatbot.Energy consumption of ChatGPT queryThe energy consumption of each query on ChatGPT can vary widely depending on several factors such as the complexity of the query, the size of the model, and the computational resources available to process the query. However, it is generally known that large language models like GPT-3, which is the architecture that ChatGPT is based on, are known to be energy-intensive and require significant computational resources to operate.According to a study conducted by researchers at the University of Massachusetts Amherst, the energy consumption of a single inference pass on a GPT-3 model with 175 billion parameters can range from 3.2 kWh to 13 kWh depending on the hardware used to perform the inference. This energy consumption is significant when compared to other common everyday devices like smartphones or laptops, which typically consume less than 1 kWh per day.It's worth noting that ChatGPT is a cloud-based service that is hosted by OpenAI, which means that the energy consumption of each query will also depend on the energy efficiency of the data centers used to host the service. OpenAI has publicly committed to using renewable energy sources to power its data centers and has also implemented energy-efficient hardware and cooling systems to minimize the environmental impact of its operations. Overall, while the energy consumption of each query on ChatGPT may not be easily quantifiable, it is clear that large language models like GPT-3 are energy-intensive and require significant computational resources to operate. As such, it is important to consider the environmental impact of these models and work towards developing more energy-efficient and sustainable technologies for the future. Conclusion ChatGPT is a powerful and versatile chatbot that has the potential to revolutionize the way we interact with machines. Its ability to generate human-like responses and adapt to user intent and context make it a valuable tool for businesses and organizations across a wide range of industries. However, as with any emerging technology, ChatGPT also faces several challenges and limitations that must be addressed in order for it to reach its full potential. By working to improve the quality and diversity of the data used to train the model, as well as its emotional intelligence and memory capabilities, we can ensure that ChatGPT continues to push the boundaries of what is possible in the world of chatbots.
More detailsPublished - Tue, 21 Mar 2023
Created by - Skillzam Team
React - A JavaScript Library : Highly in-demand skill
From conception of an idea or business case to testing, a top-notch React developer can help you build interactive components for your website or web applications using the React development concepts.What is React.js? What does a React developer do? These are a few of the most common questions we see on the web. We’ll answer these questions and more in this blog.Let’s first explore the interesting world of React. Alternately spelled as React.js or ReactJS; React is a declarative, efficient, and flexible JavaScript library for building user interfaces. It lets you compose complex User Interfaces (UIs) from small and isolated pieces of code called “components”. Simply put, UI is a term that describes any element that allows users to interact and communicate with the information saved in a device (laptop, tablet, smartphone, etc.). The user interface is the primary point of contact between humans and modern computers. Keyboards, mouse, computer texts, and desktop layout are some of the best examples of UI.React was created by Jordan Walke, a software engineer at Facebook, who released an early prototype of React called "FaxJS". He was influenced by XHP, an HTML component library for PHP. It was first deployed on Facebook's News Feed in 2011 and later on Instagram in 2012. It was open-sourced at JSConf US in May 2013.Before ReactJS was introduced, developers back then were stuck building UIs using raw JavaScript or with jQuery (a React predecessor that focused less on UI). This meant that the development process took longer with more room for errors and bugs. To solve this problem, Facebook engineer Jordan Wilke created FaxJS (first React prototype) in 2011.React makes it simple and easy to build websites and apps, create UI test cases, reuse existing codes on the website to its mobile counterpart, and improve the UI and performance of web applications. It’s versatile enough to use where JavaScript is required.Today, more and more enterprises benefit from ReactJS, a testament to its burgeoning popularity in software development. Some of the high-traffic websites that use React are Facebook, Netflix, Airbnb, Instagram, Uber, Reddit, Salesforce and so on.Along with the upswing in React usage, is the surge in demand for a React.js developer. A highly skilled React developer is involved in the entire stages of the development and maintenance of user interface components. What does a React Developer do?In a nutshell, a React developer designs and implements user-facing features for websites and applications using React.js.They are known as front-end developers, a sub-group of developers that manage everything that users see on their web browsers or applications. In other words, they are responsible for the look and feel of a website or application to ensure a seamless experience for users.The menu buttons, online forms, the graphic icons and images, voice recognition (Siri and Cortana), and gesture-driven interface (example: pinching the smartphone screen to zoom in and out) are just a few of the exciting things that a React developer can do.React developers use their knowledge in JavaScript, CSS, HTML, and other programming languages and incorporate them into the ReactJS open-source library ecosystem.Generally, they work with software testing specialists to test the codes before the web application or website goes live. React developers may also work with a team of developers, programmers, web designers, and project managers to work closely on a client’s software project. React Developer SkillsIndeed, React is a powerful tool for front-end development. Many organizations agree that having a solid background in React is essential to dominating this technology-driven industry.To level up in this competitive field, a React.js candidate should have the essential skills in these React developer tools: Web technologies – An in-depth knowledge and experience working with web technologies such as JavaScript, HTML, CSS, TypeScript, C#, SASS, Stylus, Stylus, Python, and LESS. React tools – Strong proficiency in Enzyme, Webpack, Redux, and Flux. Web frameworks – Proven expertise in different frameworks like React Native, AngularJS, Angular2, VueJS, SocketIO, Backbone, KnockoutJS, Svelte, and Bootstrap. Open-source Libraries – Demonstrable experience with JQuery, MomentJS, html5sql.js, RequireJS, and Loads. Software testing tools – A good working knowledge in testing codes using Mocha, Jest, Unit-tests, TDD, Karma, VCS, and Selenium (base). Databases – A familiarity working with different databases such as MySQL, NoSQL(e.g., MongoDB), PostgreSQL, Redis is recommended. Code repositories – Working experience using GitHub, Bitbucket, RhodeCode, and devZing Subversion Cloud management – A strong background in different cloud platforms like Amazon AWS, Microsoft Azure, OpenStack Apache, and Google Cloud Platform Project management – It’s a great advantage if they know how to manage projects using JIRA, Basecamp, Trello, Asana, ClickUp, or Zoho Projects. React Developer SalaryNow for the fun part. Let’s get an estimate on the cost of hiring a React developer. Overall, React developers are one of the highest-paid among JavaScript developers. Their salary depends on: years of experience different roles held over time level of expertise special skills training and certifications geographic location The average ReactJS developer salary in the USA is $119,997 per year or $61.54 per hour. Entry level positions start at $99,890 per year while most experienced workers make up to $150,000 per year.
More detailsPublished - Fri, 13 May 2022
Created by - Skillzam Team
How to compose a powerful & impressive job seeking resume
There are many positions for professional developers, Tester, Analysts etc today. However, to secure an interview and get hired for your dream job, a strong resume is critical. Writing the perfect one is not easy. Many candidates wonder what their resume should look like and what it should include, and this can vary because it represents different things to different people. To some, it’s a document that lists your work experience, to others, it’s a document that lists everything you’ve ever done. In the end, a technology skill based resume is one of the keys to success, despite variations in appearance.“Every corporate job opening on average attracts 250 resumes,” says Glassdoor. Only 4-6 of those applicants will receive an interview. That’s why the art of crafting a resume must not be approached lightly. We’ve outlined some key pointers to ensure that your resume stands out, helping you impress recruiters and find your dream job. Some key pointer to build impressive resume:1) Determine Your ObjectiveBefore you start looking for an entry level developer, tester, Analyst job, you should first determine what kind of expert you want to be. This will help you identify your goal. Take the time to ask and answer this question, and then determine the area of technology, you want to work in—for instance, you may want to showcase that you’re skilled in time management and have expertise in JavaScript or Python or Selenium. Once you’ve been able to identify your career goals, you’ll have a direction for your resume and job search. 2) List Your Best AchievementsNot all of your achievements should be included in your resume, but you should mention those that are applicable to the job you’re applying for. If you worked in retail super-store or in a non-profit as a teenager, this may not be an achievement that is essential to include in a Developer’s or Tester’s resume. However, you may have transferable skills such as prioritization, communication, and leadership. Each of these will make you an excellent candidate for a wide range of Technology roles. 3) Choose a Good Format and OrderWhat will carry the most weight for the role you’re applying to? What should be front and center on your resume? What’s going to persuade your future employer to choose you over other candidates? These are critical questions to ask yourself, when crafting a resume. Always begin with your elevator pitch, or objective statement, which contains a short overview of who you are, while convincing the reader that you are the best candidate for the role.Next, list your abilities. For developer jobs, this includes the programming languages you’re well versed in, or for Tester, this includes the tools, processes or frameworks but it doesn’t mean you need to list each skill. For instance, proficiency in Microsoft Office is a skill that most employers expect you to have, so it can be left out. Instead, attempt to concentrate on your top five or six technical strengths. Then, list your experience, but as we mentioned, only the experience that is directly linked to the work you’re applying for. Next, you’ll want to highlight projects that you’ve accomplished while learning to code or test or analyze. And lastly, list your education, beginning with your latest achievement and working backward. 4) Craft a Solid Opening StatementLike any other document you read, when a hiring manager first looks at a candidate’s resume, they begin at the top and work their way down. That’s why we suggest developing a powerful opening statement that will leave a good impression with the reader. This section should showcase your strengths and passions, for example: “An ambitious problem-solver with demonstrated expertise in front-end development. Passionate about generating work that exceeds expectations and speaks to the mission of the client.”As mentioned in Business Insider, “the skills segment of the CV is, according to most employers, the most important.” Why? Because many candidates may lack experience, but their skills and abilities match what the employer is looking for. That’s why the “skills” that you have mastered through Skillzam courses, section should be positioned at the top of the resume—right under your objective statement—to grab the reader’s attention. 5) Tailor Your Resume Each TimeDuring your search, you’ll likely be applying to hundreds of jobs. It is essential to read through each job description and tailor your resume to reflect the position and the needs of the company you’re applying to. Make use of all your knowledge and never sell yourself short.In addition, use keywords that are vital to your work. Many recruiters use an applicant tracking system (ATS) that filters keywords through resumes, eventually determining which ones are the best match. When selecting which keywords to use, refer to the job description and business website for each position. You may need to change your resume for every job you apply to depending on what keywords are used.In the experience section, it’s not necessary to list every responsibility of each job you’ve held, but rather the responsibilities that are most relevant to the jobs you are applying for. Also, be sure to tailor your resume to the culture of the company itself. Once you’ve completed research on work related sites like Workzam or Glassdoor, you will start to notice phrases like “collaboration” and “communication.” Use these keywords and provide examples of how you fit into the company’s workplace culture.Once you master the art of constructing a strong resume, you’ll get through the pre-interview gatekeepers and into the interview stage, bringing you one step closer to your dream career or better job.
More detailsPublished - Tue, 10 May 2022
Created by - Skillzam Team
Top 10 popular programming languages for 2022
The top 10 most in-demand programming languages for 2022You don’t want to waste your time. If you’re going to put aside the time and energy needed to learn new programming languages, you want to make sure, without a doubt, that the ones you choose are the most in-demand programming languages on the market. After all, if you’re trying to start (or advance) a career in software development, you’ll need to know which programming languages are popular, which are useful and which are up-and-coming on the market — and then set yourself up to learn them. Not sure where to start? Don’t worry; we’ve already done the heavy lifting for you, and looked through developer and market data from the past several years to identify trends and draw conclusions that will help you plan for your programming education. In this article, we’ll walk you through the Top 10 most in-demand programming .But before we get started, let’s recap how we built this list in the first place. When we sat down to figure out what the most in-demand programming languages for 2022 might be, we had two primary research questions:Which languages are in demand right now? Which ones are growing in popularity and will be in demand soon?You can probably guess the rationale behind our first question. While following trends isn’t always the best way to figure out which languages to prioritize, a language’s popularity can indicate its prominence in the market. Here’s a general rule of thumb for you — the more used or common a language is, the more likely it is that a developer (i.e., you!) will need to deal with it at work.Next, we considered which skills are poised to become the most sought-after programming languages on the market. To do so, we dug into Stack Overflow’s developer surveys from the last two years and focused on: The languages that developers report they use and want to continue using The languages that developers aren’t using yet, but are interested in usingBy looking at these trends, we aimed to gain a better understanding of which languages will become popular in the years ahead. We found that Rust, Python and Typescript all topped Stack Overflow’s list for the most-loved languages in 2019 and 2020, with only slight shuffling across years. Similarly, Python, JavaScript and Go ruled the top three of Stack’s Most Wanted lists for both years. Given their prevalence in developers’ minds, the odds of these five languages increasing in popularity is high — and are therefore worth including on our list. However, where we ranked each language depended on other factors. For example, because Python has consistently held a top-five spot on Stack Overflow’s most commonly-used, -loved and -wanted lists, we chose to rank it higher than Rust, which, while ranking #1 on Stack’s most-loved list, languishes at #19 on the organization’s common-languages list. Now that we’ve covered our process, let’s start answering the question at hand: What are the most in-demand coding languages for 2022? The Most In-Demand Programming Languages for 20221. JavaScriptWhat this language is used for: Web developmentGame developmentMobile apps Building web serversAccording to Stack Overflow’s 2020 Developer Survey, JavaScript currently stands as the most commonly-used language in the world (69.7%), followed by HTML/CSS (62.4%), SQL (56.9%), Python (41.6%) and Java (38.4%). JavaScript is used to manage the behavior of web pages. With it, coders can create dynamic web elements such as animated graphics, interactive maps, clickable buttons and more. Programmers who use HTML, CSS and JavaScript in tandem obtain a higher level of website control and can provide a better user experience in terms of navigation and readability. JavaScript is the most common coding language in use today around the world. This is for a good reason: most web browsers utilize it and it’s one of the easiest languages to learn. JavaScript requires almost no prior coding knowledge — once you start learning, you can practice and play with it immediately. Moreover, because the language is so ubiquitous, there are countless communities, courses and avenues of professional support available online. This support, in addition to the language’s top-notch usability, makes JavaScript number one on our list of the most in-demand programming languages. 2. PythonWhat this language is used for:Back end development Data science App developmentPython is a general-purpose programming language that empowers developers to use several different programming styles (i.e., functional, object-oriented, reflective, etc.) when creating programs. Several popular digital tools and platforms were developed with Python, including YouTube, Google Search and iRobot machines. It is also, according to TIOBE Programming Community index, the most popular programming language of 2022.As one of the more easy-to-learn and -use languages, Python is ideal for beginners and experienced coders alike. The language comes with an extensive library that supports common commands and tasks. Its interactive qualities allow programmers to test code as they go, reducing the amount of time wasted on creating and testing long sections of code. That said, even advanced users would benefit from adding Python to their mental catalog of programming languages, Python is easily one of the most marketable and in-demand programming languages of 2022. 3. HTMLWhat this language is used for: Web documents Website development Website maintenanceHTML stands for HyperText Markup Language. Don’t let the complicated-sounding name fool you, though; HTML is one of the most accessible stepping stones into the world of programming. Technically, HTML is a markup language, which means that it is responsible for formatting the appearance of information on a website. Essentially, HTML is used to describe web pages with ordinary text. It doesn’t have the same functionality as other programming languages in this list and is limited to creating and structuring text on a site. Sections, headings, links and paragraphs are all part of the HTML domain. As of 2020, HTML shares its #2 spot on Stack Overflow’s list of the most commonly used languages in the world with CSS. 4. CSSWhat this language is used for: Web documents Website development Website designCSS, or cascading style sheets, is usually applied in conjunction with HTML and governs the site’s appearance. While HTML organizes site text into chunks, CSS is responsible for determining the size, color and position of all page elements. CSS is convenient, too; the cascading part of the name means that an applied style will cascade down from parent elements to all children elements across the site. This feature means that once users determine aesthetics for the main parent, they won’t have to manually repeat their code across a website. Moreover, the delegation of site organization to HTML and aesthetics to CSS means that users don’t have to completely rewrite a web page just to change a color. CSS is an approachable language that allows beginning programmers to dip their toes in the metaphorical coding pool. If you’re new to coding, there’s no reason not to learn CSS before tackling more complex languages! 5. JavaWhat this language is used for: E-commerce FinanceApp developmentJava is a general use and object-oriented programming language. In object-oriented programming, developers create objects that encompass functions and data, which can then be used to provide structure for programs and applications. Java currently ranks as third most on TIOBE Programming Community index, the most popular programming language of 2022 and has held the #5 spot on Stack Overflow’s list of the most commonly used languages for two years. Java’s popularity is for good reason; this language is relatively easy to learn and use, boasts incredible security and can handle massive amounts of data. These features make Java an ideal language for the online finance sector, and it is often applied in industries such as banking, billing and the stock market. The versatility of the language, however, is what learners find really appealing. Touted as a “write-once, run-anywhere” language, Java can effectively run on any operating system, regardless of which OS was used to write the original code. It is thus ideal for writing apps not only for mobile phones and computers, but also remote processors, sensors and a variety of other consumer products. 6. SQLWhat this language is used for: Database management Sales reports Business managementSQL, or Structured Query Language, is a language that allows programmers to query and manipulate databases. As a domain-specific language, it is designed mainly for managing data within an RDBMS (relational database management system). Put simply, SQL can locate and retrieve data from a database, as well as update, add or remove records. While SQL is highly functional, it tends to work better with small databases and doesn’t always lend itself to managing expansive ones. That said, SQL still ranks as the third-most-used language in the programming industry, with over half (54.7%) of surveyed developers reporting that they use it. 7. NoSQLWhat this language is used for: Database management Sales reports Business managementNoSQL, or Non-relational SQL, was created to improve SQL’s scalability while retaining the other language’s ease of use. Remember, SQL utilizes relational database/stream management systems that keep data in tables and allows users to manipulate and extract data. NoSQL databases, on the other hand, don’t use tables and can be more useful than their predecessors for specific applications, such as storing data in a hierarchical network or supporting large-scale, cloud-based applications. Because these languages are both so versatile, they rank high on our list of the most in-demand coding languages. 8. C#What this language is used for: Game developmentDesktop/web/mobile apps VRAlso stylized as C Sharp, this language belongs to the object-oriented family of programming languages. C# was released in 2002 by Microsoft and stands today as a much-loved improvement on the C++ coding language. As a general-purpose language, C# is growing in popularity for developing web and desktop applications; according to TIOBE Programming Community index, it is the fifth most popular language. There’s no denying that it is one of the most in-demand coding languages for the upcoming year; however, there are other reasons to have this skill on your radar.As with other popular languages, an enormous community works with C# and offers support to new learners. Because of this, learning C# may be easier than attempting to learn some of the newer and less-documented languages. Plus, C# is ideal for building ever-more-popular mobile apps and games. There’s little doubt that this language will continue to be useful in the coming years. 9. C C is a general-purpose programming language that is designed to work efficiently on games, graphics, and enterprise applications. It offers amazing features like:· Presence of inbuilt functions and operators.· It is extendable.· Enhanced speed and efficiency.· The top companies using C are GitHub, Telegram messenger, MasterCard, etc.It is highly in demand despite the emergence of new programming languages. C provides the opportunities of building a successful career. 10. GoWhat this language is used for:System/network programming Audio/video editing Big DataDeveloped at Google in 2007, Go is a top-tier programming language. What makes Go really shine is its efficiency; it is capable of executing several processes concurrently. And as far as programming languages go, it has an extensive “vocabulary,” meaning it can display more information than other languages. Though it uses a similar syntax to C, Go is a standout language that provides top-notch memory safety and management features. Additionally, the language’s structural typing capabilities allow for a great deal of functionality and dynamism. Final ThoughtsWhether you are an established coder or just starting to look into the industry, learning a new language is one of the best ways to advance your programming career. But how can you start your educational journey? Your first step depends on you, your schedule and the resources you have at hand. A college program, for example, will provide you with a comprehensive education in the theory and practice of programming — however, the typical undergraduate program also demands four years of full-time study and tens of thousands of dollars in tuition funding. If you want a faster and less expensive educational experience that focuses on skills-based learning, you may want to consider a coding boot camp of SKILLZAM. Coding boot camps equip you with the hands-on skills and language proficiencies you’ll need to land an entry-level job in the field — all within three to six months. If you’re looking for the cheapest educational course and don’t mind taking on the responsibility of self-teaching, you may want to consider learning necessary coding skills via SKILLZAM online coursesWe hope that this list of the most in-demand programming languages for 2022 will help you begin your journey!
More detailsPublished - Sat, 30 Apr 2022
Created by - Skillzam Team
What does it takes to excel in the profession of Data Science
Today’s data science professionals are in high demand across a number of fields ranging from business operations and financial services to healthcare, science and more. A data scientist is an expert who uses data to extract valuable business insights. These professionals should have extensive knowledge related to computer science, data visualization and data mining along with statistics and machine learning. However, it all starts with learning the fundamentals. What You Should Learn: 1) Coding A data scientist should learn to code and create programs. They should have a solid understanding of basic coding languages, advanced analytical platforms and front end web visualization. For example:PythonPython is becoming an increasingly popular programming language. The platform is useful for a variety of processes performed by data scientists. Python’s versatility enables users to accomplish a variety of tasks that might include creating data sets or importing SQL tables. The platform is also recognized as being easy to pick up, making it a good choice for new data professionals because it helps at every stage of your career. New data analysts can learn Python quickly, but Python still has exceptional value for experienced professionals. While programmers rely on Python in many established fields, data analysts can also use Python for emerging processes.Python can even help to prepare you to learn other skills and languages down the line. Taken together, those factors make Python a great choice if you’re looking to learn a programming language. The Python platform is also open-source, making it free to install, so you don’t have to pay anything to start honing your Python skills. It is also known for its strong online community. The Python online community provides support, education, interaction and projects. There is also a great deal of potential for Python to become a common language for producing web-based analytics products and data science. SQLSQL is often a required skill for data scientists and is used to accomplish various functions that include adding, deleting or extracting information from databases. SQL also has the capability to perform analytical tasks. By using the platform’s precise commands, users are able to perform inquiries more quickly. Because of the prevalence of databases in today’s world, data professionals should at least have a basic familiarity with SQL. There is a growing demand for those who are experts in databases, so you can even specialize in SQL developing. Whether you want to turn SQL into a career or just want to supplement your programming knowledge, you’ll appreciate a quick introduction to the highlights of SQL.Before getting started with SQL, you should understand what exactly it is. As mentioned, it’s a language that lets us connect with databases. This is key since data is a crucial component of mobile and web applications, from profile information to those we follow on social media to cookies. Applications and websites use databases to hold the data. Professionals use SQL to interact with that data via a programming language. JavaScriptJavaScript is considered by many to be the scripting language of the web. This is a programming language or scripting that makes it possible to implement complex actions on a web page. JavaScript is part of nearly everything a website does that is more advanced than showing static information. JavaScript is incredibly versatile and can be used for both server-side and client-side language. Although not always the case, many Data Analytics boot camps include JavaScript in their curriculum.JavaScript builds on the web technologies of CSS and HTML, which are the other standards. As a quick reminder, HTML is a markup language that defines paragraphs and provides structure. CSS incorporates style rules, applying them to HTML. JavaScript allows for many other things, such as animating images and controlling multimedia. HTMLHTML stands for HyperText Markup Language and is the code that structures content on a website. It is important to note that HTML is not a type of coding language. Instead, it is a markup language. As a markup language, HTML outlines the structure for content. HTML includes various elements that you use to wrap or enclose portions of the content, with the goal of having it act or appear the way you want. The tags can do things such as add hyperlinks, change font size and italicize words, among others.Having the knowledge to build your own website with HTML gives you the chance to stand out from the crowd with an authentic, hand-crafted representation of your business — or any business for that matter. This powerful coding language is not only helpful to web developers, and you just might find yourself in a position where you may need them in a professional data setting. 2) Data VisualizationThe amount of data that businesses and industries produce today is greater than ever before. However, in order to be useful, the data must be converted into a format that is easily comprehended. A data scientist uses D3.js, ggplot, Matplotlib, Tableau and other tools for this purpose. By organizing and transforming data into usable formats, companies are able to make informed decisions based on the results. 3) Working With Unstructured DataUnstructured data refers to audio or visual feeds, blog posts, customer reviews and social media posts. The data included within multimedia formats often requires an ability to identify, analyze and manipulate the data in order to obtain critical information that may be beneficial to a company or industry. 4) Artificial Intelligence and Machine LearningData scientists who can create programs with artificial intelligence may find a benefit from advancing the program’s ability to learn independently. The program can use decision trees, logistic regression and other algorithms to analyze data sets, make predictions or solve problems once the platform receives a sufficient amount of data.Machine learning is a powerful tool. When you teach a machine how to use an algorithm to identify patterns, it can use those patterns to predict outcomes without using any preconceived notions or pre-programmed rules. A machine can only improve its own learning by using the information it has been given, so machine learning isn’t successful unless users provide a diverse and large enough range of data. 5) MathematicsCalculus, linear algebra and statistics are areas of math that data scientists should know in order to create their own data analysis platforms. A background in statistics is particularly helpful for understanding statistical distributions, estimators and tests. The results of statistical findings are commonly required by companies in order to make informed decisions. What You Should Already Have: 1) Natural CuriosityA data scientist needs to possess an innate desire to obtain more knowledge or information. This drive motivates them to begin the educational process and learn the field of data science in order to find answers and insights contained within data sets. Curiosity drives the best scientists forward despite obstacles to achieve the end result. 2) Effective CommunicationThe diagnoses, predictions or other findings that data scientists are able to formulate mean nothing to a company if they cannot comprehend the results. While presenting illustrated data, a data scientist must be able to explain how the results impact the business. As such, data scientists must be able to clearly translate their findings in order to make them useful to a company. 3) Commitment to LearningThe most successful data professionals will have a strong understanding of the core technical data analyst skills needed to succeed in the field. For example, it is important to develop a solid grasp in today’s most in-demand data languages such as SQL, NoSQL, Postgres/pgAdmin and MongoDB. It is also beneficial to learn advanced specialties like statistical modeling, forecasting and prediction, pivot tables and VBA scripting.Having a solid understanding of today’s critical programming languages can help a data professional stand out in the job field. Those new to the field should gain a thorough understanding of core data analytics tools like NumPy, Pandas and Matplotlib. You might also consider learning specific libraries for interacting with web data such as Requests and BeautifulSoup.Finally, it is helpful for data professionals to learn the inner workings of web visualization. Building visualizations is of little benefit without an effective way to communicate the message. Consider exploring the core technologies of front-end web visualization such as Bootstrap, Dashboarding and Geomapping in addition to the coding specialties above. Learning to use these tools will help any analysts create new, interactive data visualizations that can be shared with everyone on the web. 4) Short and Long Term GoalsSetting long-term goals is just the first step toward your employment success; you should also create a strategy for achieving smaller, short-term goals. These will help keep you motivated and moving forward toward a career in Data Science. For example, if your long-term plan is to work in the industry within one year, you’ll need a tailored path to get there.Updating your resume and portfolio, networking with industry professionals and taking extra courses are just a few of the short-term goals you may want to consider adding to your action plan. 5) Ability to AdaptAs a data analyst, it is essential to identify the skills gap you have based on future goals. For example, the skills required for data professionals in marketing may be quite different from that of a data scientist in the financial services industry. A vital component of long-term success as a data professional is the ability to adapt your skills and knowledge to evolving business needs. 6) Collaboration SkillsData scientists do not work alone. They must combine their efforts with business and industry executives to seek out effective strategies. They may have to work with engineers or designers to manufacture better products or with marketing firms to create more effective campaigns. Scientists may share their insights with software engineers or key company stakeholders, and in both cases will need to tailor their communication strategies to do so effectively.
More detailsPublished - Sat, 30 Apr 2022
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