Overview
Syllabus
Sr No.
Units and Lessons/Sub-Units
1.
Structured Instruction – Relevance to real-world problems – collaborative learning activities for solving the problem – Application oriented teaching – feedback and support
2.
Introduction to Programming and Python - Installing Python and Setting Up Development Environment - Basic Syntax, Variables, and Data Types - Input/Output Operations – Learning Activities: Overview lectures on programming concepts and Python fundamentals - Hands-on exercises to practice basic syntax and data types - Coding tasks involving input/output operations.
3.
Data Structures: Lists, Tuples, Dictionaries, and Sets - Creation and Manipulation String Manipulation and Formatting - Reading from and Writing to Files in Python Handling Exceptions and Errors.
Learning Activities: Exercises and coding challenges to practice working with lists, tuples, dictionaries, and sets - String manipulation tasks and formatting exercises - list comprehensions - File manipulation exercises: reading, writing, and handling different file formats - Error handling exercises to manage exceptions
4.
Functions and Modules: Basics and Usage - Built-in and Standard Libraries - Pandas and NumPy Libraries - Loading and Displaying Data: CSV, Excel, JSON, and other formats8 - Data Manipulation with Pandas - DataFrame and Series: Creation and Basic Operations - Indexing, Slicing, and Filtering DataFrames -Grouping and Aggregation - Creating and Importing Modules
Learning Activities: Coding exercises to create and use functions and modules - Exploring built-in libraries and their functionalities - Coding tasks to introduce basic Pandas and NumPy operations - Hands-on exercises on creating DataFrames and Series, performing basic operations - Coding tasks involving indexing, slicing, and filtering of data - Practical exercises demonstrating grouping and aggregation - Hands-on tasks to create and import custom modules.
5.
Importance and Principles of Data Visualization- Matplotlib, Seaborn, and Plotly - Basic Plotting with Matplotlib: Line Plots, Scatter Plots, Bar Plots - Customizing Plot Appearance: Colors, Labels, Titles, and Legends - Multiple Plots and Subplots in Matplotlib - Working with Different Plot Types: Histograms, Pie Charts, Box Plots - Seaborn for Statistical Visualization: Heatmaps, Pair Plots, Violin Plots - Facet Grids and Categorical Plots in Seaborn
Learning Activities: Explanation of data visualization concepts and libraries through lectures - Hands-on exercises on basic plotting using Matplotlib - Coding tasks to create various types of plots (line, scatter, bar) for data representation - Practical exercises on customizing plot attributes and appearance - Hands-on tasks to create multiple plots and subplots - Coding challenges for generating histograms, pie charts, and box plots - Hands-on exercises to create advanced plots using Seaborn
Taught by
Dr. V. Shanmuganeethi