Unit 1

Introduction to Data Analytics: Foundation of Data Analytics, use cases of Data Analytic, Describing the Data, Types of Data, Types of Variables, Data Exploration, Data Cleaning and Preprocessing, Feature Engineering, Statistical Analytics, Data Visualization

Unit 2

Data Analytics and Data processing using Python: Programming for Data Analytics using Python, Overview, Control flow statements, Strings, Data Structures, Numerical Data Processing, NumPy Dimensions, NumPy Shapes, Size and Bytes, NumPy Arange and Random Package, NumPy and Reshape, NumPy Slicing and Masking, NumPy Broadcasting

Unit 3

Advanced Data Handling and Visualization in Python: Introduction to Pandas, Pandas Series, Data Frame, Data Manipulation using Pandas, Data Visualization with Matplotlib, Data Visualization with Matplotlib with Seaborn, Interactive Plotting with Bokeh, 3D Plotting with Plotly, Geo plotting with Folium, Pandas Data plotting

Unit 4

Introduction to R and Fundamentals: Fundamentals of R, Coercion Rules, Vectors, Vector Operations, Matrices, Conditional and Iterative Statements, Data Frames, Data Manipulation, Dplyr Package, Tidying Data in R

Unit 5

Advanced Data Handling and Analysis with R: Data Visualization using ggplot2, Histogram, Bar Chart – Building a Box, Whiskers Plot and Scatterplot, Exploratory Data Analysis, Hypothesis Testing – Type I and Type II Errors, Test for the Mean – Population Variance Known – The P-Value, Test for the Mean, Population Variance Unknown, Linear Regression Analysis

Pre-Requisites

23CSE106 – Computer Programming and Algorithmic Problem Solving

Course Objectives

• Understand the Fundamental concepts of Data Analytics.
• Acquire proficiency in Python programming essentials and Numerical data processing.
• Explore Data manipulation and advanced Data visualization techniques using Python.
• Obtain an understanding of R programming fundamentals, covering basic operations and manipulation. Develop practical skills through real-world case studies and projects using Python and R.

Course Outcomes

• CO1 :  Demonstrate understanding of foundational concepts in data analytics.
• CO2 :  Demonstrate understanding of Python programming essentials for numerical data processing.
• CO3 :  Demonstrate ability to use Python for Advanced data manipulation and diverse libraries for Dynamic Visualizations.
• CO4:   Demonstrate core proficiency in R programming. CO5 Demonstrate ability to use Python and R for solving real-world projects.

CO-PO-PSO Mapping

(Affinity: 3- High, 2- Moderate, 1- Slightly)

1. Python for Data Analysis: Dive into the World of Data Science, 3rd Edition (2022) by Wes McKinney
2. R for Data Science: Statistical Computing and Statistical Thinking with R (2023) by Hadley
3. Hands-On Python and R for Data Analysis: Leverage Python and R Together for Advanced Data Analysis, 2nd Edition (2020) by Stephanie Dawson and Gary King
4. Python for Data Analysis: Dive into the World of Data Science, 3rd Edition (2022) by Wes McKinney

E-learning Content on L&T Edu Tech Platform

*CA – Can be Quizzes, Assignment, Projects, and Reports