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

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

• 1.
  An Introduction toPopular Tools, Machine Learning, and Visualization
• 2.
  Replay Module 1 :Understandingthe 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 toData 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 isa programming language for statistical computing and data visualization
• 5.
  R Programming - Features •Widerange 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: thispackage enhances R’s data visualization capabilities.
• 9.
  R Packages •tensorflow: packageenhances R’s data machine learning algorithms for predictions
• 10.
  R Programming –Use Cases
• 11.
  Q N A
• 12.
  Q: How wouldyou use the dplyr package?
• 13.
  Q What arethe key differences between the ggplot2 package and the base R plotting functions?