Data Science & Machine Learning

Course Overview

Master Data Science and Machine Learning end-to-end — from Python and R to AI, Deep Learning, Cloud, and MLOps with real-world deployment projects.

Duration: 24 weeks
Rating: 4.8 / 5
3,500+ Students

Detailed Syllabus

1-4: Python for Data Science

  • Introduction to Python Programming
  • Variables, Data Structures & Data Types
  • Control Flow, Loops & Functions
  • Object-Oriented Programming (OOP)
  • Modules, Packages & Virtual Environments
  • File & Exception Handling
  • PEP8 & Best Practices
  • APIs & Data Collection (JSON, CSV, REST)
  • NumPy, Pandas, and Data Cleaning Techniques
  • Mini Project: Data Cleaning & Analysis Pipeline

Tools: Python, Jupyter Notebook, Google Colab, VS Code

5-8: R Programming & Statistics

  • Introduction to R & RStudio
  • Vectors, Lists, Matrices, Arrays, Factors
  • Data Frames & Importing Data
  • Control Structures & Functions
  • Data Cleaning & Transformation in R
  • Descriptive & Inferential Statistics
  • Hypothesis Testing & Correlation
  • Probability Distributions & Sampling
  • Data Visualization with ggplot2
  • Project: Exploratory Data Analysis with R

Tools: RStudio, ggplot2, tidyverse

9-12: Machine Learning Foundations

  • Statistics for Data Science
  • Data Visualization & Exploratory Data Analysis (EDA)
  • Data Preprocessing & Feature Engineering
  • Linear & Logistic Regression
  • Decision Trees, Random Forests, and Ensemble Models
  • Naive Bayes, KNN, and SVM Algorithms
  • Dimensionality Reduction (PCA, LDA)
  • Hyperparameter Tuning & Cross Validation
  • Model Evaluation Metrics (Accuracy, F1, ROC-AUC)
  • Mini Project: Predictive Modeling on Real Data

Tools: Scikit-learn, NumPy, Pandas, Matplotlib, Seaborn

13-16: Deep Learning & NLP

  • Introduction to Deep Learning & Neural Networks
  • Understanding Activation Functions & Backpropagation
  • ANNs, CNNs, and RNNs (with TensorFlow/Keras)
  • LSTM & GRU Models for Time Series and Text
  • Autoencoders and Transfer Learning
  • Hyperparameter Optimization & Regularization
  • Natural Language Processing (NLP) Fundamentals
  • Text Cleaning, Tokenization, POS, NER
  • Sentiment Analysis, Topic Modeling, and Chatbots
  • Deep Learning Project: Image or Text Classification

Tools: TensorFlow, Keras, Scikit-learn, NLTK, SpaCy

17-19: Cloud, Databases & Big Data Integration

  • AWS Cloud Overview (IAM, S3, EC2, Route53)
  • Data Storage & Retrieval using AWS S3
  • Networking & Analytics Services Overview
  • Introduction to MySQL & CRUD Operations
  • Database Design, Constraints & Keys
  • Joins, Aggregations, and Stored Procedures
  • Database Optimization Techniques
  • Working with BigQuery / MongoDB Basics
  • Integrating Databases with Python & Flask
  • Mini Project: Cloud-Connected Analytics Pipeline

Tools: AWS Console, MySQL Workbench, BigQuery, FastAPI

20-21: Data Visualization & Business Intelligence

  • Tableau Interface & Data Connection
  • Charts, Maps, and Dashboards
  • Table Calculations & Storytelling
  • Advanced Data Preparation and Blending
  • Power BI Desktop Setup
  • Data Modeling & Relationships in Power BI
  • DAX Calculations and AI Visuals
  • Publishing Reports and Collaboration
  • Data Storytelling for Business Decisions
  • Visualization Project: Interactive BI Dashboard

Tools: Tableau, Power BI

22-23: MLOps & Deployment

  • Introduction to MLOps & Lifecycle
  • Model Serving using FastAPI
  • Docker Containerization & Image Building
  • Streamlit for Interactive Dashboards
  • Version Control & CI/CD Pipelines
  • Model Monitoring & Retraining
  • Deployment on Heroku, AWS, or Render
  • Automating ML Pipelines
  • Testing and Model Validation
  • Mini Project: End-to-End Model Deployment

Tools: Docker, Heroku, Streamlit, GitHub Actions, FastAPI

24: Capstone Project & Career Preparation

  • End-to-End Data Science Capstone
  • Data Collection, Cleaning & Feature Engineering
  • Model Building, Evaluation, and Deployment
  • Cloud Hosting and Dashboard Presentation
  • Documentation & Reporting
  • Portfolio & GitHub Setup
  • Mock Interviews & Resume Building
  • Presentation with Instructors and Mentors

Tools: Python, SQL, Tableau, Power BI, Docker, Streamlit, AWS

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