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Course Materials
Course Features
Duration
Self-paced
Level
Beginner
Language
English
Mode
Online
Data Science
Data Scientist — Master’s Course
Master end-to-end Data Science: Python, R, SQL (MySQL/SQL Server) & basics of SAS; NumPy, Pandas, SciPy, scikit-learn; visualization with Matplotlib, Seaborn & ggplot2; ML (regression, trees, SVM, RF, Naive Bayes, KNN, clustering); Deep Learning (ANN, CNN, RNN, LSTM, Encoder-Decoder); NLP with BERT; Tableau & Power BI; Git/GitHub, Excel reporting, project management & soft skills; plus a real capstone. Includes 1:1 mentorship and mock interview prep.
Last updated December 2025
Next cohort starts Sep 8th
$1,200.00
$1,500.00
Save 20% - Limited Time Offer!
Become a job-ready Data Scientist. This master program builds your foundations in Python, SQL/R, data wrangling and statistics, then advances through machine learning, deep learning, NLP, and BI dashboards—with professional tooling and a real capstone.
- Core stack: Python, R, SQL (MySQL/SQL Server), and essentials of SAS
- Wrangling & stats: NumPy, Pandas, SciPy; visualization with Matplotlib, Seaborn, ggplot2
- ML: linear/logistic regression, decision trees, SVM, Random Forests, Naive Bayes, KNN, clustering
- DL & NLP: ANN, CNN, RNN, LSTM, Encoder-Decoder; BERT fine-tuning for NLP tasks
- BI & tooling: Tableau, Power BI, Excel reporting; Git/GitHub; VS Code & PyCharm
- Professional: project management, communication, problem-solving, and mock interview preparation
Finish with a capstone project that takes you from data collection and cleaning to model building, evaluation, and presentation.
1:1 Personalized Mentorship
Mock Interview Preparation
55% Average Salary Hike
Python, R, SQL & SAS basics
NumPy, Pandas, SciPy, scikit-learn
Matplotlib, Seaborn, ggplot2
ML: Regression, Trees, SVM, RF, NB, KNN, Clustering
Deep Learning: ANN, CNN, RNN, LSTM, Encoder-Decoder
NLP with BERT
Tableau & Power BI dashboards
Git & GitHub collaboration
Excel analysis & reporting
Project management & soft skills
Capstone project
Introduction to Data Science
4 lessons • Weeks 1
Overview of Data Science
Roles and Responsibilities
Importance in Modern Business
Key Concepts and Terminologies
Methodologies: SDLC, Agile & Waterfall
6 lessons • Weeks 2
Software Development Life Cycle (SDLC)
Phases of SDLC
Principles of Agile
Agile in Data Science Projects
Waterfall Methodology
Comparing Waterfall with Agile
Programming for Data Engineering
6 lessons • Weeks 3
Python Basics: Syntax & Structures
Python: Data Types and Functions
SQL Basics: Writing Queries
SQL: Database Operations
Introduction to SAS & Basic Syntax
R Basics & Data Manipulation
IDEs & Developer Setup
4 lessons • Weeks 4
VS Code: Setup & Extensions
VS Code: Debugging
PyCharm: Setup & Usage
PyCharm: Writing & Debugging Code
Data Manipulation & Visualization (Py Data Stack)
4 lessons • Weeks 5
NumPy: Array Operations
Pandas: DataFrame Manipulation
Matplotlib: Core Plots
Seaborn: Advanced Visualization Techniques
SciPy, scikit-learn & ggplot2
3 lessons • Weeks 6
SciPy: Scientific Computing
scikit-learn: ML Workflows
R ggplot2: Creating Visualizations
Machine Learning I: Regression & Trees
5 lessons • Weeks 7
Linear Regression: Concepts & Applications
Logistic Regression: Concepts & Applications
Model Building & Evaluation
Decision Trees: Concepts & Applications
Decision Trees: Model Building & Evaluation
Supervised vs Unsupervised & Classification
3 lessons • Weeks 8
Supervised vs Unsupervised: Key Concepts
Classification Algorithms: Techniques
Building & Evaluating Classifiers
Advanced ML I: SVM & Random Forests
4 lessons • Weeks 9
SVM: Concepts & Applications
SVM: Model Building & Evaluation
Random Forests: Concepts & Applications
Random Forests: Model Building & Evaluation
Advanced ML II: Naive Bayes, K-Means, KNN
6 lessons • Weeks 10
Naive Bayes: Concepts & Applications
Naive Bayes: Model Building & Evaluation
K-Means Clustering: Concepts & Applications
K-Means: Model Building & Evaluation
KNN: Concepts & Applications
KNN: Model Building & Evaluation
Deep Learning I: Overview & ANN
3 lessons • Weeks 11
Intro to Deep Learning & ML vs DL
Neural Networks (ANN): Concepts & Architectures
ANN: Building & Training
Deep Learning II: CNN & RNN
4 lessons • Weeks 12
CNN: Concepts & Applications
CNN: Building & Training
RNN: Concepts & Applications
RNN: Building & Training
DL Architectures: LSTM & Encoder-Decoder
4 lessons • Weeks 13
LSTM: Concepts & Applications
LSTM: Building & Training
Encoder-Decoder: Concepts & Applications
Encoder-Decoder: Building & Training
NLP & Transformers (BERT)
3 lessons • Weeks 14
Introduction to NLP
BERT: Concepts & Applications
Fine-Tuning BERT for NLP Tasks
Business Intelligence: Tableau & Power BI
3 lessons • Weeks 15
Tableau: Connect & Build Dashboards
Power BI: Reports & Dashboards
Data Visualization Best Practices
Databases: SQL Server & MySQL
4 lessons • Weeks 16
SQL Server: Setup & Management
SQL Server: Advanced SQL
MySQL: Setup & Management
MySQL: Advanced SQL
Version Control & Collaboration
3 lessons • Weeks 17
Git Basics: Commands & Concepts
Branching and Merging
GitHub: Collaboration & Pull Requests
Data Analysis & Reporting
4 lessons • Weeks 18
Excel for Data Analysis
Advanced Excel Techniques
Creating Effective Reports
Presenting Data Insights
Project Management & Soft Skills
4 lessons • Weeks 19
Using MS Office for Documentation
Project Planning & Execution
Communication & Collaboration
Problem-Solving & Critical Thinking
Capstone Project
4 lessons • Weeks 20
Defining Project Scope & Objectives
Data Collection, Cleaning & Analysis
Model Building & Evaluation
Presenting the Project & Outcomes
Project: Capstone: Data Science Project