AI-Assisted Data Science Program

About Course

The Data Science Program is designed to prepare learners for careers in Data Science, Machine Learning, Predictive Analytics and Data-Driven Decision Making. The curriculum builds strong foundations in Python, data analysis libraries, Power BI, statistics, and machine learning through extensive hands-on projects and industry datasets. Learners will collect, clean, analyze, visualize and model data to solve real business problems while building a professional portfolio and interview readiness.

Who Should Enroll

• Fresh Graduates
• Engineering Students
• BCA/BCS Graduates
• BSc/MSc Students
• Working Professionals
• Career Switchers
• Aspiring Data Scientists
• Data Analysts
• Business Analysts
• Python Developers

Module 1 – Python for Data Science

Topics Covered

• Python Fundamentals
• Variables & Data Types
• Operators
• Control Statements
• Loops
• Functions
• OOP
• Exception Handling
• File Handling
• Modules & Packages
• Working with CSV/Excel/JSON
• Mini Projects

Tools & Technologies Covered

Python, Jupyter Notebook

Expected Outcomes

• Write Python programs for data processing and automation.

Module 2 – Data Analysis Libraries for Python

Topics Covered

• NumPy
• Pandas
• Data Cleaning
• EDA
• Matplotlib
• Seaborn
• Plotly
• Feature Engineering Basics
• Missing Values
• Outlier Detection

Tools & Technologies Covered

NumPy, Pandas, Matplotlib, Seaborn, Plotly

Expected Outcomes

• Prepare and visualize datasets for analysis.

Module 3 – Power BI for Data Science

Topics Covered

• Power BI Desktop
• Power Query
• Data Modeling
• DAX
• Interactive Dashboards
• Power BI Service
• Publishing Reports
• Executive Dashboards

Tools & Technologies Covered

Power BI, Power Query, DAX

Expected Outcomes

• Build professional dashboards and business reports.

Module 4 – Statistics & Mathematics for Data Science

Topics Covered

• Descriptive Statistics
• Inferential Statistics
• Probability
• Distributions
• Hypothesis Testing
• Correlation
• Covariance
• Linear Algebra Basics

Tools & Technologies Covered

Python

Expected Outcomes

• Apply statistical methods to solve business problems.

Module 5 – Machine Learning

Topics Covered

• ML Fundamentals
• Regression
• Classification
• Decision Trees
• Random Forest
• SVM
• KNN
• Naive Bayes
• Clustering
• PCA
• Model Evaluation
• Cross Validation
• Hyperparameter Tuning

Tools & Technologies Covered

scikit-learn, Python

Expected Outcomes

• Build and evaluate predictive models.

Module 6 – Advanced Data Science Projects & Placement Readiness

Topics Covered

• Time Series
• Recommendation Systems
• Customer Segmentation
• Capstone Projects
• Git & GitHub
• Resume Building
• Mock Interviews
• Placement Preparation

Tools & Technologies Covered

Python, Power BI, GitHub

Expected Outcomes

• Complete end-to-end projects and become interview ready.

Career Opportunities

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Business Analyst
• BI Developer
• Research Analyst