English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 0h 39m | 502 MB
Python and R are common programming languages used when working with data. Each language is powerful in its own way; however, it’s important that you select the language that will best help you achieve your end result. In this course, data scientist and coding instructor Lavanya Vijayan helps you make this choice, sharing important considerations for using each language in various circumstances. Lavanya starts by going over the background of both languages, as well as the strengths and disadvantages of each in different scenarios. She then walks through the process of working on a data science project and how you’d handle the data at various stages using Python and R. Lavanya then covers how to analyze data using both languages. She rounds out the course by discussing the use cases that play to each language’s strengths. By the end of this training, you’ll have the essential information you need to determine whether Python or R is right for you.
Table of Contents
Getting Started
1 Python vs. R
2 Important notes for Python and R
1. Python vs. R
3 Working with programming languages
4 Using Python
5 Using R
6 Comparing Python and R
2. Key Differences in Handling Data
7 Data loading in Python vs. R
8 Data exploration in Python vs. R
9 Data cleaning and manipulation in Python vs. R
10 Data visualization in Python vs. R
3. Data Analysis in R and Python
11 Data analysis in R
12 Data analysis in Python
4. Applications
13 Common data science applications with Python
14 Common data science applications with R
Moving Forward
15 Unlocking data analysis in Python or R
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