Data Science & Machine Learning (DSML)
- Description
- Curriculum
- Reviews
This 12-week live weekend course equips learners with the core skills in Data Science and Machine Learning required to succeed in today’s data-driven world. Delivered with live faculty interaction, hands-on projects, and industry case studies, the course blends theoretical knowledge with practical applications. Students will gain proficiency in Python, R, statistics, and data visualization, while mastering the workflow of data preprocessing, model building, and analytics for decision-making. By the end of this course, learners will complete a portfolio-ready capstone project and gain the foundation to pursue advanced data science, ML, and AI careers.
Learning Objectives
By the end of this course, learners will be able to:
• Understand the data science lifecycle and industry practices.
• Apply Python & R programming for analytics and machine learning.
• Use mathematical/statistical foundations for predictive modeling.
• Perform data preprocessing, cleaning, and visualization.
• Build, train, and evaluate ML models for real-world problems.
• Deploy insights from analytics to solve business challenges.
• Present results through dashboards and storytelling.
Career Opportunities
Graduates of this course can pursue roles such as:
• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Business Intelligence Specialist
• Quantitative Analyst
• AI/ML Consultant
• Analytics Engineer
Course Breakdown – 12 Weeks (Weekend Live Sessions)
Schedule: 6 Hours/Week (Sat & Sun, 3 Hours each) → 72 Total Hours
1. Basic Programming Knowledge:
Understanding of variables, loops, functions, data types.
2. Mathematics:
Statistics, linear algebra, calculus fundamentals.
3. Data Analysis:
Knowledge of data visualization & data manipulation.
4. Machine Learning Fundamentals:
Awareness of regression, classification, clustering concepts.
• Fresh graduates aspiring to enter the Data Science and ML industry.
• IT & software professionals looking to upskill in analytics and ML.
• Business analysts and decision-makers wanting data-driven strategies.
• Researchers and academicians applying DS/ML in applied studies.
