Teaching and Learning practices of Python programming for the faculty members is paramount in today's educational landscape due to its presence in all Engineering and Technology curriculum and its multifaceted benefits. Its versatility spans numerous domains, from web development to data science and artificial intelligence, offering learners a comprehensive skill set applicable to a wide array of career paths. The industry's high demand for Python skills further underscores its significance, ensuring that learners equipped with Python proficiency are well-positioned for employment opportunities across diverse sectors.
Teaching & Learning (TL) Practices for Python Programming
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
