Unit 1 : Foundation & Statistics

• AI & Data Science foundation
• R, Python, Spark, & Azure overview
• Data types & Real world data
• Descriptive Statistics
• Random Distributions
• Inferential Statistics

Unit 2 : Data Analytics with R

• R Studio Installation & Configuration
• Component of R programming
• R Objects & Attributes
• R Data Types
• Functions & Loops
• Reading Data in R
• Data Import from Database
• SQL & No SQL Data formats
• Working with Numbers & Strings
• Working with Dates & Time
• Working with Data frames
• Data Cleaning & Slicing
• Data Transformation
• NA treatments
• Case Study on Real World Data

Unit 3 : Data Visualization with R

• Business reporting with various plots
• Data visualization with base plot
• Data visualization with ggplot2 & plotly
• Case Study on Real World Data

Unit 4 : Data Analytics with Python

• Python Installation & Configuration
• Component of Python programming
• Importing Libraries & Data
• Python Data Types
• Working with Numpy & Pandas
• Data Transformation
• NA treatments
• Comparison of R & Python

Unit 5 : Machine  Learning Introduction

• Machine Learning Algorithms
• Supervised ML
• Un-Supervised ML
• Linear Regression OLS & Multiple
• Correlation & Test Errors
• Linear Regression in R
• Linear Regression in Python

Machine Learning Evaluation Parameters

CV Preparation

Effective selling of CV in job sites

Executive presence for Interview

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