Data Science (AI/ML)

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Data Science (AI/ML)

Instructor admin
Last Update 22 Apr, 2025
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About The Course

This comprehensive 6-month course in Data Science (AI/ML), led by industry veteran Dr. Venkat, covers all essential topics required to become a skilled data science professional. It spans from foundational data handling and visualization techniques to advanced machine learning, big data tools, and a capstone project. Whether you’re a beginner or transitioning into data science, this course will equip you with real-world, in-demand skills.

The Course Curriculam

Data Science (AI/ML)
Duration: 6 Months Instructor: Dr. Venkat (20 Years of Experience in IT) Contact: +91 991 991 9452 Mode: Flexi and Fixed Timing (Online/Offline) Expected Salary After Completion: ₹15 LPA – ₹25 LPA (for 3–4 years of experience)

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Course Information

Course level: Intermediate
Instructor: admin
Duration: 4380h 262800m
Last Updated: Apr 22, 2025
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Week 1: Introduction to Data Science
Overview of Data Science and its decision-making importance
Data types, sources, and tools (Python, Jupyter, Anaconda)
Activities:
Anaconda installation and Python packages setup
Intro to Google Colab
Week 2: Data Collection and Sources
Structured vs. unstructured data
APIs, Web Scraping, Databases
Libraries: requests, BeautifulSoup, Scrapy
Activities:
Collecting data via public API
Web scraping with BeautifulSoup
Week 3: Data Cleaning & Preparation
Missing data, outliers, duplicates
Scaling, normalization, encoding
Activities:
Hands-on cleaning with Pandas
Dataset transformation exercises
Week 4: Exploratory Data Analysis (EDA)
Descriptive statistics and visual summaries
Visualization with Matplotlib & Seaborn
Activities:
EDA on real-world dataset
Pattern & correlation discovery
Week 5: Data Visualization
Principles & advanced visualizations
Interactive visuals with Plotly & Dash
Activities:
Build dashboards with Plotly
Advanced charts with Seaborn
Week 6: Introduction to Statistics
Probability, distributions, hypothesis testing
p-values, confidence intervals
Activities:
Hypothesis testing
Statistical analysis on datasets
Week 7: Regression Analysis
Simple & multiple linear regression
Evaluation metrics (R², MSE, RMSE)
Activities:
Regression modeling in Python
Application in real scenarios
Week 8: Classification and Clustering
Logistic regression, decision trees
K-means and hierarchical clustering
Activities:
Algorithm implementation
Use case exercises
Week 9: Working with Big Data
Hadoop ecosystem overview
SQL & NoSQL basics
Intro to Apache Spark & PySpark
Activities:
SQL queries
Big data processing in PySpark
Week 10: Capstone Project Preparation
Project proposal development
Team formation and planning
Activities:
Present project ideas
Initial data exploration
Weeks 11–12: Capstone Project Development
Iterative model building and analysis
Regular feedback sessions
Activities:
Group/individual project work
Peer reviews and collaboration
Week 13: Final Project Presentation
Formal presentation to peers and faculty
Evaluation and final feedback
Activities:
Final demos and documentation
Reflective discussion & wrap-up

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Data Science (AI/ML)