+ View more
Course overview
Learn Data Science in this full tutorial course for absolute beginners. Data science is considered the "sexiest job of the 21st century". The course provides accessible and non-technical overviews of the field of data science and its facets, such as common programming languages and applications, the practical aspects of data sourcing, important mathematical concepts, and common statistical approaches. Data science sits at the intersection of statistics, computer programming, and domain expertise. This non-technical overview introduces the basic elements of data science and how it is relevant to work in the real world.
DATA SCIENCE: AN INTRODUCTION, PART 1
Data Science: An Introduction is the first of five courses designed to outline the principles and practices of applied data science.
DATA SOURCING, PART 2
Data science can’t happen without data. That means the first task in any project is source – that is, to get – the raw materials that you will need. This short course discusses some of the more familiar methods of gathering data and some of the less familiar that are specific to data science. Data Sourcing is the second of five courses designed to outline the principles and practices of applied data science.
CODING, PART 3
Data science professionals rely on a range of tools, from basic spreadsheets to advanced languages like R and Python. In these videos, we’ll cover the basic elements of the most important tools for data science. Coding is the third of five courses designed to outline the principles and practices of applied data science.
MATHEMATICS, PART 4
Data science relies on several important aspects of mathematics. In this course, you’ll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems. Mathematics is the fourth of five courses designed to outline the principles and practices of applied data science.
STATISTICS, PART 5
Statistics is distinct from - but critical to - data science. In this non-technical, conceptual overview, you can learn how statistical practices such as data exploration, estimation, and feature selection give data science its power and insight. Statistics is the final of five courses designed to outline the principles and practices of applied data science.
DATA SCIENCE: AN INTRODUCTION, PART 1
Data Science: An Introduction is the first of five courses designed to outline the principles and practices of applied data science.
DATA SOURCING, PART 2
Data science can’t happen without data. That means the first task in any project is source – that is, to get – the raw materials that you will need. This short course discusses some of the more familiar methods of gathering data and some of the less familiar that are specific to data science. Data Sourcing is the second of five courses designed to outline the principles and practices of applied data science.
CODING, PART 3
Data science professionals rely on a range of tools, from basic spreadsheets to advanced languages like R and Python. In these videos, we’ll cover the basic elements of the most important tools for data science. Coding is the third of five courses designed to outline the principles and practices of applied data science.
MATHEMATICS, PART 4
Data science relies on several important aspects of mathematics. In this course, you’ll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems. Mathematics is the fourth of five courses designed to outline the principles and practices of applied data science.
STATISTICS, PART 5
Statistics is distinct from - but critical to - data science. In this non-technical, conceptual overview, you can learn how statistical practices such as data exploration, estimation, and feature selection give data science its power and insight. Statistics is the final of five courses designed to outline the principles and practices of applied data science.
What will i learn?
- Data Science Fundamentals
- Principles and practices of applied data science
- Range of tools
- Mathematics for Data Science
- Statistics
- Methods of gathering data
- Data exploration
- Estimation
Requirements
- Computer Basics
- Python ( Nice to know )
Curriculum for this course
77 Lessons
06:09:15 Hours
DATA SCIENCE: AN INTRODUCTION, PART 1
22 Lessons
01:37:27 Hours
-
Course Welcome
00:01:58
-
Demand for Data Science
00:05:54
-
The Data Science Venn Diagram
00:06:58
-
The Data Science Pathway
00:04:49
-
Roles in Data Science
00:03:59
-
Teams in Data Science
00:03:31
-
Big Data
00:04:56
-
Coding
00:03:01
-
Statistics
00:04:16
-
Business Intelligence
00:03:04
-
Do No Harm
00:06:01
-
Methods Overview
00:02:20
-
Sourcing Overview
00:03:40
-
Coding Overview
00:03:32
-
Math Overview
00:04:00
-
Statistics Overview
00:04:02
-
Machine Learning Overview
00:02:43
-
Interpretability
00:09:06
-
Actionable Insights
00:05:14
-
Presentation Graphics
00:07:12
-
Reproducible Research
00:06:11
-
Next Steps
00:01:00
DATA SOURCING, PART 2
14 Lessons
00:52:43 Hours
-
Welcome to Data Sourcing
00:00:44
-
Metrics
00:06:13
-
Accuracy
00:03:51
-
Social Context of Measurement
00:03:36
-
Existing Data
00:07:08
-
APIs
00:06:23
-
Scraping
00:05:19
-
New Data
00:02:16
-
Interviews
00:02:53
-
Surveys
00:03:22
-
Card Sorting
00:03:38
-
Lab Experiments
00:03:46
-
A/B Testing
00:02:59
-
Next Steps
00:00:35
CODING, PART 3
16 Lessons
01:28:18 Hours
-
Welcome
00:04:48
-
Spreadsheets
00:07:14
-
Tableau Public
00:09:05
-
SPSS
00:08:08
-
JASP
00:06:49
-
Other Software
00:06:52
-
HTML
00:03:17
-
XML
00:05:26
-
JSON
00:04:43
-
R Language
00:05:06
-
Python
00:05:56
-
SQL
00:04:43
-
C, C++, & Java
00:02:50
-
Bash
00:05:42
-
Regex
00:04:54
-
Next Steps
00:02:45
MATHEMATICS, PART 4
10 Lessons
00:52:22 Hours
-
Welcome
00:02:53
-
Elementary Algebra
00:03:03
-
Linear Algebra
00:05:38
-
Systems of Linear Equations
00:05:24
-
Calculus
00:04:17
-
Calculus & Optimization
00:08:43
-
Big O
00:05:19
-
Probability
00:07:33
-
Bayes' Theorem
00:07:57
-
Next Steps
00:01:35
STATISTICS, PART 5
15 Lessons
01:18:25 Hours
-
Welcome
00:04:01
-
Exploration Overview
00:02:23
-
Exploratory Graphics
00:08:01
-
Exploratory Statistics
00:05:05
-
Descriptive Statistics
00:10:16
-
Inferential Statistics
00:04:28
-
Hypothesis Testing
00:06:04
-
Estimation
00:08:04
-
Estimators
00:05:29
-
Measures of Fit
00:03:30
-
Feature Selection
00:06:15
-
Problems in Modeling
00:05:58
-
Model Validation
00:03:50
-
DIY
00:03:18
-
Next Steps
00:01:43
+ View more
Other related courses
About instructor
Techitaka .
Don't Just Change. Grow.
Technology Data Science Software Tools Full Stack Developement Mobile Apps Blockchain AI-ML Web 3.0
Technology skills are always evolving. Every Techie strives to be upto date. Techitaka helps get Access To Thousands of Inspiring courses and the support of a learning community. D...
Student feedback
Reviews
-
Hemanth BabuThis is a great course. Thank you for giving us this opportunity to those like me who have not a background in programming. I am trying to make 30 minutes per day everyday and follow the course well. Thanks! -
Gaurav KishoreI feel that it's really great for you guys to do this! For starters, my school doesn't only teach us ANY programming and stuff like that, but it doesn't even have a computer course anymore. Now, we only use the computer room as a spare for science projects or daily announcements. Secondly, it's GREAT! That really helps! You guys are giving me the actual education I need, even for a hobby, but hey, at least I have more to look forward too!
-
Rashmi Pawar
-
Shahzeb KhanWatching this course even if it's only halfway through, I can't picture how much and useful the knowledge this gives. Easy to comprehend, straight to the point, applicable to real life needs, and overall just amazing. Awesome!
-
Rohit YadavFantastic Knowledge.
Write a public review