• Introduction to Data Science and its applications
• Basics of Python programming for Data Science
• Data manipulation and cleaning using pandas
• Exploratory data analysis and visualization with matplotlib and seaborn
• Introduction to statistical concepts for Data Science

• Supervised learning algorithms: Linear regression, logistic regression, decision trees
• Unsupervised learning algorithms: Clustering, dimensionality reduction
• Model evaluation metrics and techniques
• Cross-validation and hyperparameter tuning

Project 1 – Exploratory Data Analysis and Predictive Modeling

Students will work on a project that involves exploratory data analysis, data preprocessing, feature engineering, and predictive modeling.
The project will require students to analyze a real-world dataset, extract insights, and build machine learning models to solve a specific problem.
Students will present their project results, including visualizations, model performance metrics, and actionable recommendations.

• Support Vector Machines (SVM) and Kernel Methods
• Ensemble methods: Random Forests, Gradient Boosting, and AdaBoost
• Neural networks and deep learning architectures
• Reinforcement learning and its applications

• Text preprocessing techniques
• Text classification and sentiment analysis
• Named Entity Recognition (NER) and language generation
• Topic modeling and text summarization
• Transfer learning and pre-trained models for NLP tasks

   

Project 2 –Natural Language Processing (NLP) and Text Analytics

Students will undertake a NLP project that involves preprocessing and analyzing text data, performing sentiment analysis, and building NLP models.
The project will require students to apply techniques such as text classification, topic modelling, and named entity recognition to extract meaningful insights from textual data.
Students will present their project outcomes, including model performance evaluations and demonstrations of the applied NLP techniques.

• Image preprocessing and augmentation techniques
• Convolutional neural networks (CNN) for image classification
• Object detection and localization with techniques like YOLO and Faster R-CNN
• Image segmentation and instance segmentation
• Generative Adversarial Networks (GAN) for image synthesis

• Distributed computing frameworks: Apache Spark
• Handling big data with Spark RDD, DataFrame, and SparkSQL
• Big data visualization and analytics with libraries like Dask and Plotly
• Time series analysis and forecasting
• Model explainability and interpretability techniques
• Ethical considerations in Data Science and AI
  

  
  Project 3 – Computer Vision and Image Recognition

  Students will work on a comprehensive computer vision project that involves image preprocessing, object detection, and image classification.
  The project will require students to apply deep learning techniques, such as convolutional neural networks (CNNs), for tasks like object detection, image segmentation, or facial recognition.
  Students will present their project outcomes, including visualizations, model performance metrics, and applications of the computer vision techniques implemented.
  
  

  
  
  CASE STUDIES 

• Customer Segmentation for Personalized Marketing in E-commerce
• Credit Card Fraud Detection using Machine Learning
• Medical Image Classification for Diagnosing Rare Diseases
• Movie Recommendation System based on User Preferences
• Demand Forecasting for Retail Inventory Management
• Social Media Sentiment Analysis for Brand Reputation Management
• Predicting Customer Churn in Telecommunications Industry
• Image Recognition for Autonomous Driving Applications
• Real Estate Price Prediction using Housing Market Data
• Network Intrusion Detection for Cybersecurity Threat Monitoring