This category includes courses related to Data Science and Analytics:

PY101: Introduction to Python
This course offers a comprehensive introduction to Python programming, covering essential programming concepts such as variables, data types, control structures, and functions. Students will learn how to write and run Python code, with a focus on solving real-world problems using programming.

PY102: Data Structure in Python
This course explores fundamental data structures (lists, dictionaries, sets, tuples) and advanced collections. Students will learn how to choose the right structure for data efficiency and memory management.

PY103: Algorithm Analysis in Python

This course introduces Big O notation and algorithmic efficiency. Students will learn how to optimize code for large-scale data processing, focusing on sorting, searching, and recursion.

DSAN_101: Introduction to Data Science
This course provides an overview of data science concepts, including data collection, cleaning, visualization, and basic statistical analysis. Students will learn how to work with real-world datasets using Python while exploring key tools such as Pandas and Matplotlib.

DSAN_102: Data Engineering & MLOps
This course covers the plumbing of data science. Students learn to build robust data pipelines, containerize applications using Docker, and manage model versions. Key Focus: Maintaining a standardized project directory structure for reproducibility.

DSAN_103: The Data Cleaning Pipeline (End-to-End) This project-based course focuses on transforming "dirty" real-world data into a "golden source" for analysis.

  • Module 1: Advanced Pandas & RegEx for data validation.

  • Module 2: Building a modular /src folder with cleaner.py and validator.py scripts.

  • Module 3: Logging and Error Handling (tracking what data was dropped and why).

  • Outcome: A fully automated Python Project that takes a raw .csv and outputs a cleaned .parquet file.

DSAN_104: Data Storytelling & Dashboarding This course bridges the gap between analysis and stakeholders.

  • Visual Design: Choosing the right charts for the right data (Comparison vs. Distribution).

  • Tooling: Building interactive dashboards using Streamlit (Python-based) or Tableau/PowerBI.

  • Integration: Connecting your DSAN_103 cleaned data directly to a live dashboard.

PY101_Introduction to Python

This course is primarily designed for first-year undergraduate students as an introduction to the field of computer science and fundamental concepts of computer programming. It utilizes Python programming and is suitable for students without any prior programming experience. Throughout the course, students will acquire knowledge in Python fundamentals, data types, writing functions, debugging, and solving real-life problems using programming concepts. Additionally, students will learn the essentials of version control using Git.

PY102_Data Structure in Python

This course builds upon the foundational programming skills introduced in Py101 and focuses on the organization, storage, and management of data within computer programs. Students will explore fundamental data structures such as lists, stacks, queues, trees, graphs, and hash tables, learning how each structure supports efficient data access and manipulation. Emphasis is placed on understanding abstract data types (ADTs), analyzing trade-offs in implementation, and applying these structures to solve practical computational problems. Through Python-based programming exercises, students will gain experience in implementing data structures, measuring their performance, and selecting appropriate structures for various tasks.

PY103_Algorithm Analysis in Python

Building on the concepts from Py102, this course introduces students to the principles of algorithm design and analysis. It emphasizes developing efficient solutions to computational problems and rigorously evaluating their performance. Topics include asymptotic analysis, time and space complexity, recurrence relations, and common algorithmic paradigms such as divide-and-conquer, greedy algorithms, dynamic programming, and backtracking. Students will also be introduced to the fundamentals of computational complexity, including NP-completeness. By the end of the course, students will be able to design, analyze, and compare algorithms using both theoretical and empirical methods in Python.

Introduction to Data Science

This course provides an overview of data science concepts, including data collection, cleaning, visualization, and basic statistical analysis. Students will learn how to work with real-world datasets using Python while exploring key tools such as Pandas and Matplotlib.