The Data Science Course 2020 Q2 Updated: Part 4 > Python & R [Free Online Course] - TechCracked

The Data Science Course 2020 Q2 Updated: Part 4 > Python & R

Learn the two most widely used programming languages with Data Science: Python and R

This course includes:
  • 22.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of completion

What you'll learn
  • You will learn both Python and R Programming with Data Science in this course.
  • Python: You will first learn how to Install Anaconda and Jupyter on your desktop/laptop
  • Python: You will understand and learn the basics of For Loops and Advanced For Loops. You will have clarity on Python generators and will master the flow of your code using "If Else"
  • Python: You will understand Why foundations Modify Lists and Dictionaries and Functions. Learn how to analyze, retrieve and clean data with Python
  • Python: Learn Concatenation (Combining Tables) with Python and Pandas and Manipulating Time and Date Data with Python Datetime
  • Python: You will learn to Use Pandas with Large Data Sets, Time Series Analysis and Effective Data Visualization in Python
  • R: You will learn the most important tools in R that will allow you to do data science
  • R: You will have the tools to tackle a wide variety of data science challenges, using the best parts of R.
  • R: You will learn how to Tidy the data. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored.
  • R: You will learn Visualisation, it is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data
  • R: You will learn Models, they are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they generally scale well.


Both Python and R are popular programming languages for Data Science. While R’s functionality is developed with statisticians in mind (think of R's strong data visualization capabilities!), Python is often praised for its easy-to-understand syntax.

Ross Ihaka and Robert Gentleman created the open-source language R in 1995 as an implementation of the S programming language. The purpose was to develop a language that focused on delivering a better and more user-friendly way to do data analysis, statistics and graphical models.

Python was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes.

Also See : The Data Science Course 2020 Q2 Updated: Part 1

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