Course Objectives
This course is designed to provide participants with a fundamental understanding of the Python programming language and its practical applications in data analysis and computational tasks. The course introduces Python syntax, programming logic, data structures, file handling, and essential libraries such as NumPy, Pandas, SciPy, Matplotlib, and Seaborn. Participants will develop foundational programming skills required for data processing, visualization, and analytical applications.
Learning Outcomes
• Understand the fundamental concepts and syntax of Python programming.
• Use Python variables, data types, operators, control structures, and functions.
• Work with Python data structures including lists, tuples, sets, and dictionaries.
• Perform basic data manipulation and analysis using NumPy and Pandas.
• Create basic data visualizations using Matplotlib and Seaborn.
• Handle files, user inputs, modules, dates, and built-in Python functionalities.
• Develop foundational programming skills for further applications in analytics and financial modelling.
Lesson plan (Tentative)
Day 1
Session 1: Introduction to Python: Overview of Python programming language; Applications and importance; Python environment setup; Python syntax; Python output; comments; variables; data types; numbers; casting; strings; booleans
Session 2: Python Programming Fundamentals: Operators; built-in functions; string formatting; None; user input; string methods; basic problemsolving using Python expressions.
Day 2
Session 1: Python Data Structures: Lists, tuples, sets, dictionaries; list methods; dictionary methods; arrays; practical data organization and manipulation.
Session 2: Python Control Flow and Functions: If...Else statements; match statements; while loops; for loops; range function; functions and modular programming concepts.
Day 3
Session 1: Python Modules and File Handling: Python modules; dates; file handling concepts; reading files; writing/creating files; deleting files; managing external data.
Session 2: Scientific Computing with Python: Introduction to NumPy; arrays and numerical operations; SciPy fundamentals; applications in analytical computation.
Day 4
Session 1: Data Analysis and Visualization: Pandas fundamentals; data loading; data manipulation; introductory data analysis; Matplotlib and Seaborn for visualization.
Assessment: Assignment will be given on the topics covered so far
Session 2: Integrated Practical Session: Application of Python fundamentals; revision of core concepts; practical exercises using Python libraries; preparing participants for advanced Python applications and Assignment submission and presentation
