Introduction to popular data science tools such as R, and SQL

Module 2: Overview of Data Science Tools and Technologies

1. An Introduction to Popular Tools, Machine Learning, and Visualization

2. Replay Module 1 : Understanding the Role of Data Science in Business Definition and scope of data science Importance of data science in modern business environments Introduction to python and its basics (variables, data types)

3. Session 4 Introduction to Data Science Tools (R, SQL) Programming Languages R: Statistical computing, data analysis, graphical representation Databases and Query Languages SQL: Structured Query Language for managing and querying relational databases

4. R Programming R is a programming language for statistical computing and data visualization

5. R Programming - Features Wide range of libraries Large and active community of users Optimal data storage and handling Suite of data analysis tools and operators Packages to develop neural networks for artificial intelligence (AI) Conditionals, loops, input and output facilities, and user-defined iterative functions Platform-independent and capable of running on all operating systems (Windows, Mac, UNIX, Linux)

6. R Programming - Syntax

7. R Packages tidyverse : a package that expands R’s utility in data science, allowing you to transform and visualize data and streamline your workflow.

8. R Packages ggplot2 : this package enhances R’s data visualization capabilities .

9. R Packages tensorflow : package enhances R’s data machine learning algorithms for predictions

10. R Programming – Use Cases

11. Q N A

12. Q: How would you use the dplyr package?

13. Q What are the key differences between the ggplot2 package and the base R plotting functions?