Courses / Data Science & Generative AI
Python • SQL • Machine Learning • Power BI • AI Projects

Data Science & Generative AI Training

Master Data Science anytime, anywhere, at your own speed. Learn Python, statistics, SQL, data visualization, machine learning, AI concepts, business intelligence and real-world projects to build an industry-ready Data Science career.

Course Fee₹14,999/-
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About This Course

In today’s digital era, data has become one of the most valuable assets for every business. Organizations use Data Science to analyze large datasets, discover hidden patterns, predict future trends, automate decisions and improve business performance.

This self-paced Data Science course is designed for students, freshers, software professionals, analysts, engineers and business professionals who want to move into data-driven roles. The course combines Python, statistics, data analysis, SQL, visualization, machine learning, AI concepts and real-world business projects.

Whether you are starting from scratch or transitioning into a Data Science career, this program gives you practical skills, portfolio projects and interview-focused preparation to become job-ready.

What You Will Learn

  • Complete Data Science lifecycle and industry workflow
  • Python programming for data analysis and automation
  • NumPy, Pandas, Matplotlib, Seaborn and Scikit-learn
  • Statistics, probability, hypothesis testing and linear algebra basics
  • Data cleaning, transformation, feature engineering and EDA
  • SQL for data extraction, reporting and analytics
  • Machine Learning models for regression, classification and clustering
  • Power BI dashboards, business intelligence and data storytelling

Course Modules

01

Introduction to Data Science

  • What is Data Science?
  • Data Science lifecycle
  • Data Science vs AI vs Machine Learning
  • Business applications and industry use cases
  • Career opportunities and future scope
02

Python Programming

  • Python fundamentals
  • Variables, data types and operators
  • Conditions, loops and functions
  • Object-oriented programming
  • File handling and exception handling
  • Modules and packages
03

Python Libraries

  • NumPy for numerical computing
  • Pandas for data manipulation
  • Matplotlib and Seaborn for visualization
  • Plotly for interactive charts
  • Scikit-learn for machine learning
  • TensorFlow and Keras overview
04

Mathematics & Statistics

  • Probability concepts
  • Descriptive and inferential statistics
  • Mean, median, mode and standard deviation
  • Variance, correlation and covariance
  • Hypothesis testing
  • Linear algebra basics
05

Data Preparation & EDA

  • Data collection methods
  • Data cleaning and missing value treatment
  • Outlier detection
  • Feature engineering and selection
  • Data transformation and normalization
  • Pattern and trend analysis
06

SQL for Data Science

  • SQL fundamentals
  • SELECT queries and filtering
  • Joins and subqueries
  • Aggregate and window functions
  • Views and stored procedures
  • Data extraction techniques
07

Machine Learning

  • Supervised and unsupervised learning
  • Regression and classification
  • Decision Trees and Random Forest
  • K-Means clustering
  • Support Vector Machines
  • Model evaluation and improvement
08

Deep Learning & AI

  • Neural networks and ANN basics
  • CNN and RNN overview
  • TensorFlow and Keras introduction
  • Generative AI overview
  • LLMs and prompt engineering
  • AI for business applications
09

Business Intelligence

  • Power BI introduction
  • Dashboard development
  • KPI reporting
  • Business analytics
  • Data storytelling
  • Executive dashboards
10

Cloud & Big Data Concepts

  • Big Data overview
  • Hadoop and Spark basics
  • Data lakes and data warehouses
  • ETL concepts
  • AWS, Google Cloud and Azure basics
  • Cloud storage and data pipelines

Industry Tools Covered

Python Jupyter Notebook Google Colab NumPy Pandas Matplotlib Seaborn Plotly Scikit-learn SQL & MySQL MongoDB Basics Power BI Tableau Excel Git & GitHub AWS Basics TensorFlow Keras VS Code

Real-Time Projects

Sales Forecasting
Customer Churn Prediction
House Price Prediction
Loan Approval Prediction
Credit Risk Analysis
Fraud Detection
Customer Segmentation
Recommendation System
Sentiment Analysis
Business Performance Dashboard
Employee Attrition Analysis
Healthcare Data Analytics

Additional Learning

  • Business problem solving
  • Data storytelling
  • Model deployment basics
  • API integration
  • Agile methodology
  • Resume building
  • GitHub portfolio development
  • LinkedIn profile optimization
  • Interview preparation
  • Mock assessments

Who Should Take This Course?

This course is ideal for learners who want to start or switch into Data Science, Data Analytics, Machine Learning, Business Intelligence or AI-related roles.

  • B.Tech, BE, BCA, MCA, B.Sc, M.Sc and Diploma students
  • Freshers looking to start a Data Science career
  • Python Developers and Software Engineers
  • Data Analysts and Business Analysts
  • AI and Machine Learning enthusiasts
  • Finance, marketing and operations professionals
  • Researchers and IT professionals
  • Anyone interested in Data Science

No prior Data Science experience is required. The course starts with programming fundamentals and gradually moves toward analytics, machine learning and real-world data applications.

Why Choose This Course?

  • Self-paced learning with lifetime access
  • Beginner to advanced curriculum
  • Real-time industry projects
  • Hands-on coding sessions
  • Practical assignments
  • Business case studies
  • Machine learning implementation
  • Data visualization projects
  • Power BI integration
  • SQL practice
  • Resume building assistance
  • GitHub portfolio development
  • Mock technical interviews
  • One-to-one doubt resolution
  • Internship and placement assistance
  • Course completion certificate
  • Regular course updates

Career Opportunities

After completing this course, learners can apply for Data Science, Analytics, Machine Learning, Business Intelligence and decision science roles across multiple industries.

  • Data Scientist
  • Data Analyst
  • Business Intelligence Analyst
  • Machine Learning Engineer
  • Data Engineer
  • AI & Data Science Engineer
  • Business Analyst
  • Analytics Consultant
  • Research Analyst
  • Product Analyst
  • Quantitative Analyst
  • Data Visualization Specialist
  • Decision Science Associate
  • Junior Data Scientist

Course Outcome

Understand the complete Data Science lifecycle and industry workflow.
Analyze, clean and prepare structured and unstructured datasets.
Perform statistical analysis and exploratory data analysis.
Build predictive machine learning models using Python.
Create professional dashboards and business reports.
Work with SQL databases for data extraction and analysis.
Build a professional portfolio with real-world projects.
Prepare confidently for Data Science, Analytics and ML job roles.

Certification & Placement Support

  • Course Completion Certificate
  • Data Science Career Roadmap
  • Resume Building Workshops
  • LinkedIn Profile Optimization
  • GitHub Portfolio Development
  • Mock Technical Interviews
  • Industry Project Experience
  • Internship Assistance
  • Placement Assistance
  • Career Mentorship
  • Lifetime Learning Resources

Ready to Build Your Data Science Career?

Start your Data Science journey with structured self-paced learning, real-world projects, Python practice, machine learning models, dashboards and career-focused preparation.