Our world has undergone tremendous changes in the last few decades. Knowledge has become the cornerstone of our civilisation. We have embraced the use of data to gain that knowledge. This has been brought about by our ability to generate and capture vast amounts of data. Together with an explosion of data has come the ease of access to data and the ease of extracting knowledge from data.
Extracting knowledge from data was traditionally the task of statisticians and data analysts. Formal training in statistics was not for the masses. As data became abundant in so many fields, domain experts in these fields needed to learn how to analyse data. Their unique perspective, knowledge, and experience are invaluable in making sense of all the data in their fields.
Modern computers and software have led to the democratisation of analyses, understanding data, and the use of data. Expensive, closed-source software from large corporations only available to the elite have made way for free and open-source computer languages such as Python, R, and Julia. Any domain expert or interested party can now use data to contribute to the fundamental understanding of our world, solving problems today that would otherwise have taken decades more to solve.
Data Science is the umbrella term for this ability to gather, manipulate, analyse, visualise, and learn from data. There has never been a more exciting time.
se has one aim. To explain the essence of Data Science using the most popular and powerful tools available today. The journey is full of surprises and moments of enlightenment as you join the massive and ever-growing community of Data Scientist. The skills that you will become aware of during this course will open a new world.
Our vehicle will be the most popular language in Data Science. Python is an easy to learn, yet extremely powerful computer language. We will use cloud-based computing, negating the need to install any software one your own computer.
The course has been created to jump-start your Data Science skills. As such, it is very dense with information. I want you to have the best and most complete start to your new abilities. One week is not enough to learn all these new skills. As with learning a new spoken language, you will need time and experience well beyond just this week. I do, however, want to leave you with a clear path forward. A few toy examples will not satisfy you. Instead, this course aims to highlight everything that is possible. I do not want to leave you wanting. I want you to become an expert Data Scientist in your field.
There are several educational resources available for this course. First and foremost are a set of detailed video tutorial that serves as your first contact with the course. The video lectures make use of extensive notes and code. The code is available as Google Colab notebooks. The notebooks are also provided as reference documents in portable document format (PDF) for you to read. There are also sets of exercise materials that you complete before each day’s live session. During these sessions we work through the exercise material.
The School for Data Science and Computational Thinking wants to be your partner in the future, and we hope that you stay in touch after the course.
The course comprises 14 modules, which are described below. All the modules contain Google Colab notebook files. Modules 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, and 14 contain video lectures and PDF notes. Modules 3, 4, 5, 6, 7, 9, 10, and 11 contain exercise notebook files that you should attempt to complete. They will form the basis of the live sessions. A set of solution files are also available that you can use if you get stuck. Try not to use them, though. In Python, there are many ways to solve a problem and you will learn more by discovering your own solutions.
Course Author: Juan Klopper
Distributed under Creative Commons Attribution-ShareAlike
CC BY - NC - SA
What will i learn?
- Data Science
- Python language
- Pandas
- Statistics
- Data Visualisation
- Plotly library
- Randomness and Probabilities
- Hypothesis Testing
- Uncertainty
- Linear Modelling
- Machine learning
- Scikit-learn
- k nearest neighbours algorithm
- Random forests algorithms
- Python Language
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Introducing the course
00:10:24
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Downloadable ZIP course material
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Intro to DataScience
00:29:35
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Data & Definitions
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Learning Python Programming Language
00:56:47
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Learning Pandas
01:29:32
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Introduction to descriptive statistics
00:23:48
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Plotly for Data Visualisation
00:49:59
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Concept of randomness and probability
00:56:14
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Process of hypothesis testing
00:42:46
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Comparing two means for a numerical variable
00:33:52
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Uncertainty as confidence intervals using bootstrap resampling
00:22:41
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Basics of linear modelling
00:56:12
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Machine learning as a form of artificial intelligence
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Machine learning project using 'k' nearest neighbours algorithm
01:08:01
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Basic building block of random forest
00:51:25
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Akash KumarThis is just amazing content, all the things that you need to know in one place. Thank you so much for your time and efforts,and giving us this invaluable course! -
Pooja GuptaThis series is great, I highly recommend it to anyone who is interested in data science and wants to start taking active steps to familiarize themselves with the core concepts of data science. The series is very well designed -- it's engaging and it's well-paced. Watch the series if you're new to data science, you won't regret it.
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Rohit Yadav
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Asad AhmadIt's a pretty solid course overall. It is super beginner friendly but for those who have a background in other programming languages he can move very slowly. I did feel. however, that the Pandas, Plotly, and the project went well. I do recommend this course.
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Yuvraj SinghGood Course on Data Science
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Dheeraj Sharma
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