Data Science Master's Program
Master Data Science & Analytics with Comprehensive Training
Course Overview
🎯 Course Objective
Master comprehensive data science skills including statistics, machine learning, big data processing, and advanced analytics. Learn to extract insights from data and build predictive models that drive business decisions across various industries.
⏱️ Duration & Schedule
20-week comprehensive program with 3 sessions per week. Each session is 2 hours long with hands-on coding practice and real-world data science projects.
💼 Career Prospects
Prepare for roles like Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, Data Engineer, and Analytics Manager. Includes interview preparation and placement assistance.
🏆 Certification
Receive industry-recognized certification upon successful completion with portfolio of data science projects and practical assessments.
Meet Your Instructor
Trilochan Tarai
Data Science Expert
With over 20 years of experience in data science and analytics, Trilochan has worked with leading tech companies like TCS and Majesco. He specializes in Python, machine learning, statistical analysis, big data processing, and business intelligence, and has mentored over 40000+ students in their data science journey.
Course Curriculum
Module 1: Python for Data Science
Duration: Week 1-3 | Topics: 12 sessions
- Python Programming Fundamentals
- Data Structures and Algorithms
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Matplotlib and Seaborn for Visualization
- Jupyter Notebooks and Development Environment
- Statistical Analysis with Python
- Data Preprocessing and Cleaning
- Data Import/Export and File Handling
- Working with APIs and Web Scraping
- Data Quality Assessment
- Exploratory Data Analysis (EDA)
Module 2: Statistics & Mathematics for Data Science
Duration: Week 4-5 | Topics: 8 sessions
- Descriptive Statistics
- Probability Theory
- Inferential Statistics
- Hypothesis Testing
- Linear Algebra for Data Science
- Calculus and Optimization
- Statistical Distributions
- Correlation and Regression Analysis
Module 3: Machine Learning & Predictive Analytics
Duration: Week 6-8 | Topics: 12 sessions
- Introduction to Machine Learning
- Supervised Learning Algorithms
- Unsupervised Learning Techniques
- Model Evaluation and Validation
- Feature Engineering and Selection
- Cross-validation and Hyperparameter Tuning
- Ensemble Methods and Boosting
- Time Series Analysis and Forecasting
- Scikit-learn Library Deep Dive
- Performance Metrics and Evaluation
- Model Interpretability
- ML Pipeline Development
Module 4: Big Data & Database Technologies
Duration: Week 9-11 | Topics: 12 sessions
- SQL and Database Fundamentals
- NoSQL Databases (MongoDB, Cassandra)
- Big Data Processing with Spark
- Hadoop Ecosystem
- Data Warehousing Concepts
- ETL/ELT Processes
- Data Pipeline Development
- Streaming Data Processing
- Cloud Data Platforms (AWS, Azure, GCP)
- Data Lake and Data Warehouse
- Data Governance and Security
- Performance Optimization
Module 5: Advanced Analytics & Business Intelligence
Duration: Week 12-14 | Topics: 12 sessions
- Advanced Data Visualization
- Dashboard Development (Tableau, Power BI)
- Business Intelligence Concepts
- KPI Development and Monitoring
- A/B Testing and Experimentation
- Customer Analytics and Segmentation
- Marketing Analytics
- Financial Analytics
- Risk Analysis and Management
- Data Storytelling
- Executive Reporting
- ROI and Business Impact Analysis
Module 6: Deep Learning & Advanced ML
Duration: Week 15-17 | Topics: 12 sessions
- Introduction to Deep Learning
- Neural Networks Fundamentals
- TensorFlow and Keras Framework
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transfer Learning and Pre-trained Models
- Natural Language Processing
- Computer Vision Applications
- Model Optimization and Tuning
- Model Deployment and Production
- MLOps and Model Monitoring
- Advanced ML Techniques
Module 7: Capstone Project & Portfolio Development
Duration: Week 18-20 | Topics: 12 sessions
- End-to-End Data Science Project
- Real-world Business Problem Solving
- Data Collection and Preparation
- Model Development and Validation
- Results Interpretation and Presentation
- Portfolio Development and GitHub
- Resume Building for Data Science
- Interview Preparation
- Industry Case Studies
- Career Planning and Networking
- Final Project Presentation
- Certification and Next Steps
Why Choose This Course?
Comprehensive Data Science Curriculum
From Python fundamentals to advanced analytics, covering everything needed for a successful data science career
Hands-on Practice
Live coding sessions, assignments, and real-world data science projects with industry datasets
Industry-Focused
Learn data science skills that are in high demand in the current job market with practical applications
Small Batch Size
Maximum 25 students per batch for personalized attention
Placement Support
Interview preparation and job placement assistance for data science roles
Lifetime Support
Continued mentor support even after course completion
Ready to Master Data Science?
Join hundreds of successful data scientists who advanced their careers with comprehensive data science skills