5th Semester
Data Science & AI / ML
Analyze data and build predictive models
This program develops data analysis and machine learning skills aligned with industry workflows, focusing on structured data handling, modeling, and evaluation.
Overview
This course introduces students to practical data science and machine learning techniques used in real-world applications. It is a hands-on program focused on analyzing structured data, building predictive models, and understanding end-to-end ML workflows. Students work with Python, databases, statistical methods, and machine learning algorithms through guided exercises and a capstone project. By the end of the 12-week course, learners gain industry-relevant skills in data analysis, modeling, and evaluation applicable across domains.
Course Outcome
By the end of the course, students will be able to:
- Analyze and preprocess structured datasets using Python and industry-standard data handling techniques.
- Apply statistical and mathematical concepts to interpret data and support modeling decisions.
- Query and manage structured data using databases and SQL
- Build, train, and evaluate supervised and unsupervised machine learning models.
- Perform feature engineering and model optimization to improve predictive performance.
- Design and implement end-to-end machine learning workflows on real-world datasets.
FORMAT : Hybrid
DURATION : 12 Weeks
HOURS PER WEEK : 2 HOURS
NO OF VIDEO CONTENT : 12
NO OF ASSIGNMENTS : 6
Syllabus
Course Syllabus
Introduction to Data Science & Python
Week 1
Students are introduced to the data science lifecycle and its real-world applications. The session covers Python fundamentals for data handling, including basic libraries and data structures used in analysis.
Data Cleaning & Exploratory Data Analysis
Week 2
This week focuses on preparing raw data for analysis. Students learn data cleaning, preprocessing, and exploratory data analysis techniques to identify patterns, trends, and anomalies.
Statistical Foundations for Data Interpretation
Week 3
Students explore key statistical concepts such as distributions, measures of central tendency, and correlation. The emphasis is on interpreting data and making informed analytical decisions.
Mathematical Foundations for Machine Learning
Week 4
This module introduces mathematical concepts relevant to ML, including linear algebra and basic optimization ideas. The focus is on understanding how math supports model behavior.
Databases & SQL for Data Analysis
Week 5
Students learn how structured data is stored and managed using databases. The session covers SQL queries for data retrieval, filtering, aggregation, and joins.
Supervised & Unsupervised Learning Concepts
Week 6
This week introduces core machine learning paradigms. Students learn the intuition behind common supervised and unsupervised algorithms and their use cases.
Model Training & Evaluation
Week 7
Participants learn how to train machine learning models and evaluate their performance. Topics include train-test splits, validation techniques, and performance metrics.
Feature Engineering & Model Optimization
Week 8
Students explore feature selection, transformation, and engineering techniques. The session also covers basic model tuning to improve accuracy and generalization.
Applying ML Models to Real Datasets
Week 9
This week emphasizes practical application of machine learning models on real-world datasets. Students work through end-to-end examples from data preparation to prediction.
End-to-End ML Workflows & Pipelines
Week 10
Students learn how to structure complete ML workflows, including data preprocessing, modeling, and evaluation pipelines. The focus is on reproducibility and best practices.
Capstone Project Development
Week 11
Teams or individuals work on a capstone project using real datasets. Students apply all learned concepts to solve a data-driven problem.
Project Presentation & Review
Week 12
The course concludes with project presentations and reviews. Students demonstrate their models, explain insights, and receive structured feedback on their approach and outcomes.
learning experience
WHAT STUDENTS TAKE AWAY
- Practical data analysis and ML skills
- Experience with real-world datasets
- Understanding of end-to-end ML workflows
Students pursuing analytics, ML, or AI roles.
Turn data into actionable insights.
