This unit serves as a primer for data science, introducing key tools and concepts. It’s designed to equip non-programmers with foundational Python skills, facilitating a deeper understanding and practical application throughout the course.

Module 1: Introduction To Data Science, Analytics & Artificial Intelligence

• Introduction to tools, key concepts, and definitions
• Real-time project applications in different domains
• Practical applications of data science in various industries

Module 2: Fundamentals of Programming

• Introduction to Python tools
• Installation of Python
• Python Fundamentals

Tools Covered: Python, Anaconda, Jupyter, Google Colab

Module 3: Fundamentals of Statistics

• Importance and Use of Statistics in Data Science
• Descriptive Statistics & Predictive Statistics
• Learn how predictive Statistics connects with Machine Learning

Note:

Module 2 and Module 3 of Unit 1 are specially designed for non-programmers to understand the basics of computer programming and math.

This unit provides an extensive roadmap for building a robust portfolio in data science. You’ll master GitHub, a version control system, for efficient collaboration and project management. Additionally, you’ll harness LinkedIn‘s power for networking and career advancement.

Module 1: Git & GitHub (VCS)

• Introduction to Version Control Systems
• Installing and Configuring Git
• Git Essentials

• Branching and Merging
• GitHub Essentials

• Collaborating on GitHub
• Forking repositories
• Creating pull requests

• Best Practices and Workflows

Class Hands-On: Initiate, collaborate, and work on a real-time project

Tools Covered: Git, GitHub

Module 2: LinkedIn Profile building

• Introduction to LinkedIn as a Professional Networking Platform
• Crafting a Compelling LinkedIn Profile

• Leveraging LinkedIn Features for Engagement

• Growing Your Network on LinkedIn

• Increasing Followers and Engagement

• Enhancing Professional Branding on LinkedIn

• Leveraging LinkedIn for Career Advancement

This Python course introduces fundamental to advanced concepts tailored for data science and AI applications. Learn Python step by step from basics to advanced. Learn all libraries, functions, and modules to perform data science projects by analyzing and building ML & AI models using Python.

Module 1: Core Python Programming

• Python Environment
• Data types & Operators
• Operators & Loop controls

Project: Build a simple calculator

Module 2: Advanced Python Programming

• Functions & Modules
• Regular Expressions (RegEx)
• File Handling & Exception Handling
• Generators & Decorators

Class Hands-on:

25+ programs/coding exercises on data types, loops, operators, functions, generators, file I/O, reg-ex, and exception handling

Module 3: Web Scraping using Python

• Introduction to Web Scraping
• Web Requests & HTTP
• Parsing HTML with Beautiful Soup

Project: Scrape and Analyze Data from a Website (2-3 Projects)

Module 4: OOPs in Python

• Classes and Objects
• Encapsulation, Inheritance, and Polymorphism
• Abstraction and Interfaces
• Method Overriding and Overloading
• Class Variables and Instance Variables

Module 5: Python For Data Analytics

• Data Analysis using NumPy (Array Operations)
• Data Analysis using Pandas (On Dataframes)
• Data Visualization using Matplotlib
• Data Visualization using Seaborn

Tools Covered: NumPy, Pandas, MatplotLib, Seaborn, Beautiful Soup

EDA Project (Create Insights using Data Analytics) 

2 Full-Length Projects on Data Analytics using Pandas, MatplotLib & Seaborn to analyze Data to Gain Insights and Identify Patterns.

This course provides a comprehensive overview of statistical concepts and machine learning techniques, along with their practical applications. You will learn machine learning algorithms, explore various case studies to understand real-world applications and build models to reinforce your learning.

Module 1: Statistics & Probability

• Fundamentals of Math, Probability & Statistics
• Descriptive vs inferential statistics
• Types of data, Sample and Population
• Descriptive Statistics
• Handling outliers & missing values in data
• Discrete and continuous probability distributions
• Normal distribution and central limit theorem
• Linear Algebra, Sampling and Estimation
• Hypothesis Testing Workflow
• Confusion Matrix, Performance Metrics
• P-values, Z Scores, Confidence Level
• Significance Level, Sampling Techniques
• Parametric Tests: T-test, Z-test, F-test, ANOVA test
• Non-Parametric Tests: Chi-square test, Man Whitney U Test & Wilcoxon Rank Sum Test
• Regression & Classification Analysis

Class Hands-on:

Problem-solving for central tendency, ANOVA, central limit theorem & hypothesis testing Case study

Module 2: Machine Learning

• Set Theory
• Data Preprocessing
• Traditional coding vs Machine learning
• Supervised and unsupervised learning
• Model evaluation
• Exploratory Data Analysis
• Data Analysis & Visualisation
• Feature Engineering
• Machine learning model building & evaluation
  • Linear Regression Model & Evaluation
  • L1 & L2 Regularization (Lasso and Ridge Regression)
  • Logistic Regression Model & Evaluation
  • K Nearest Neighbours (KNN) & Evaluation
  • Decision Tree Classifier & Regressor
  • Random Forest Classifier & Regressor
  • Naive Bayes Classifier
• Overfitting, bias-variance tradeoff
• Cross-validation

Project:

• EDA for Weight Prediction task from Height (Regression task)

• 1 project each for Regression & Classification

Module 3: Advanced Machine Learning

• Clustering & K-means
• K-Means Clustering Model
• Ensemble approach
• Bootstrapping + Aggregation = Bagging
• Bagging vs Boosting
• Hyperparameter Tuning for GridSearchCV
• XGBoost Explanatory Model Building
• Boosting Ensemble Models
• Adaptive Boosting (AdaBoost)
• Handling Imbalanced Dataset
  • Resampling (Oversampling & Undersampling)
  • Oversampling Technique (SMOTE)
• Gradient Boosting
• CatBoost
• LightGBM
• Support Vector Classifier (SVC) & Support Vector Machines (SVM)
• Principal Component Analysis (PCA)
  • Use of Dimensionality Reduction Technique
  • Difference with Feature Selection Techniques
• Density-based Spatial Clustering of Applications with Noise (DBSCAN)
• Hyperparameter Tuning

Tools Covered: Pandas, Matplotlib, Sk Learn, LightGBM

Class Projects:

• Project with practical application of Regression, Classification, and Clustering algorithms using Machine Learning concepts.
• Case studies in various domains (e.g., healthcare, finance, marketing, supply chain, etc.) like:

• Spam Mail Classifier using Naive Bayes Algorithm
• Detect car Insurance Fraud Claims
• Heart disease detection using ML

Note: All Machine Learning Algorithms will be covered in depth with real-time projects & case studies for each algorithm. Once Machine learning is completed, the Capstone Project will be released for the batch.

The NLP specialization will help you gain experience in techniques such as; text preprocessing, sentiment analysis, and building text-based models. The Time Series Analysis course will help you learn how to model, forecast, and analyze time-based data that contains date and time parameters.

Module 1: NLP

• Introduction to Natural Language Processing
• Text Preprocessing
• Text Embedding Techniques
• Word2Vec Text Embedding
• Topic modeling (LDA, LSA)
• Named Entity Recognition (NER)
• Part-of-Speech Tagging (POS Tagging)
• Transformer architecture and BERT model
• Text classification models

Class Projects:

• To classify an email as spam or not spam
• Social media sentiment analysis
• Translation & summarization of News
• Generate optimized title/headline
• Case Study on Recommendation Engine

Tools Covered: NLTK, Spacy, BERT

Module 2: Time-Series Data Analysis 

• Introduction to time series data
• Linear Regression Vs ARIMA model
• Time series visualization and exploration
• Time series decomposition
• Stationarity and its tests
• Autoregressive (AR) Models
• Moving Average (MA) Models
• Autoregressive Integrated Moving Average (ARIMA) Models
• Seasonal ARIMA (SARIMA) models
• Exponential smoothing methods

Class Projects:

• Project to predict the number of customers of an Airline organization using Time Series Model ARIMA & SARIMAX
• Financial Market Stock Price analysis and forecasting
• Sales data forecasting to understand trend and seasonality
  
  

Tools Covered: SciKit Learn, Pandas, Matplotlib

Deep Learning, is a subset of machine learning that focuses on training neural networks to build a model by studying hierarchical patterns and features from the input data. On the other hand, in reinforcement Learning, you will learn to build a sequential model that interacts with the environment to achieve a goal by receiving real-time feedback.

Module 1: Deep Learning 

• Introduction to Deep Learning
• Forward Propagation in ANN
• Backpropagation in ANN
• ReLU vs Leaky ReLU
• Exploding Gradient Problem
• Stochastic Gradient Descent (SGD) Optimizer
• Artificial Neural Network (ANN)
• L1 & L2 Regularization in ANN
• Loss Functions for Regression (MSE, RMSE, MAE, Huber Loss)
• Loss functions for classification (Cross Entropy Loss)
• Weight Initialisation Techniques
• Recurrent Neural Network (RNN)
• Vanishing Gradient Problem in RNN
• Long Short Term Memory (LSTM) Neural Networks
• Convolutional Neural Networks (CNNs)
• Generative Adversarial Networks (GAN)
• Autoencoders & Variational Autoencoders (VAEs)
• Optimization Techniques for Deep Learning
• Hyperparameter Tuning

Class Projects

• Diabetes detection using Artificial Neural Network (ANN)
• Fake News Classification using LSTM Network
• Sentiment analysis for social media & customer reviews
• Stock Price Forecasting using LSTM Neural Network
• Applications in Information Retrieval & Recommendation Systems
• Heart Disease Detection project

Tools Covered: Tensorflow, Keras, PyTorch

Module 2: Reinforcement Learning 

• Fundamentals of Reinforcement Learning
• Markov Decision Processes (MDPs)
• Monte Carlo Methods
• Temporal Difference Learning
• Q-Learning and SARSA
• Policy Gradient Methods
• Multi-Agent & Hierarchical Reinforcement Learning
• Reinforcement Learning with Deep Learning
• Deep Q-Networks (DQN)
• Transfer Learning & Lifelong learning and Fine-tuning

Class Projects:

• Dynamic Pricing Strategies in E-commerce
• Optimizing Supply Chain Logistics
• Personalized Healthcare Treatment Planning
• Reinforcement Learning-Based Autonomous Driving

In this unit, we’ll delve into computer vision for image analysis. We’ll explore image classification, object detection, and segmentation in computer vision using deep-learning architectures like CNNs.

Module 1: Computer Vision 

• Introduction to Computer Vision
• Convolutional Neural Network (CNN)
• Difference between CNN and other neural networks
• Concept of CNN architectures
• Introduction to OpenCV
• Image Processing using OpenCV
• Deep CNN
• Capturing videoframes
• Object Tracking using HSV colorspace range
• Image Thresholding techniques
• Canny Edge Detection Algorithm & Implementation
• Hough Line & Circle Transform
• Image classification & segmentation using OpenCV
• Identifying Contours using OpenCV
• Object Detection in OpenCV

In this unit, we’ll delve into generative AI and prompt engineering tools. Generative AI will introduce us to large language models, GANs, and autoregressive models for creating new content. At the same time, prompt engineering tools will help us craft effective prompts for guiding AI models, particularly language models like GPT.

Module 1: Generative AI and Large Language Models 

• Introduction to Generative AI
• Traditional AI vs Generative AI
• Regular Model Building vs Generation
• Introduction to Transformer Architecture 
• Embedding component (Word Embedding & Positional Embedding)
• BERT (Encoder-Decoder Architecture) vs GPT (Decoder Architecture)
• Introduction to Generative Pretrained Transformers (GPT) – Text Generation: Word Generation, Sentence Generation
• ChatGPT (GPT-3.5-Turbo & GPT-4 model)
• Open Source Large Language Models (LLMs)
• Huggingface Open LLM Leaderboard
• LLM Benchmarking datasets
• Prompts, Contexts, and Structure of Prompts
• Retrieval Augmented Generation (RAG) Workflow
• Langchain implementation of RAG
• Fine-tuning: Concepts of Text Embeddings, Text Similarity 
• Generation vs Chat Generation
• Text Generation Model vs Chat Model
• Reinforcement Learning Human Feedback (RLHF) loop
• Image Generation: Generative Adversarial Networks (GANs)
• Auto Encoders & Variational Autoencoders

Tools Covered: Tensorflow, Open CV, BERT, Huggingface 

Class Project:

• Tomato Leaf Disease Classification using OpenCV Inception V3
• Fake news classification using LSTM
• Objects/Persons Tracking using OpenCV
• Road Lane Detection using OpenCV
• Face & Eye detection using OpenCV
• Domain-specific (eg: Healthcare) Chatbot using Gen AI
• Chatbot using Meta/Llama-2 LLM
• Context-based chatbot using RAG workflow – Indexing a PDF file on Pinecone Vector Database, Implementation using Langchain library

Module 2: Prompt Engineering 

• Exploring prompt tools
• Understanding prompt tools & their architecture
• Future advancement in AI and Large Language tools
• Overview of tools like (GPT, Dall E, Midjourney Etc.)

ChatGPT: Prompt for text Generation (Natural Language Processing)

• Introduction to NLP concept and role in GPT tools
• ChatGPT and its architecture
• Hands-on with ChatGPT / Microsoft Copilot prompt for Text Generation
• Tuning ChatGPT for desired output and application

Dall E / Midjourney: Prompt for image Generation

• Introduction to image generation using prompt
• Exploring Midjourney / Dall E 2 & 3 / Gencraft prompt for Image generation
• Tuning prompt for the desired output
• Ethical consideration for AI-generated images

Synthesia for Video Generation & Slides AI for PPT creation

• Learning prompt with Slides AI (from Google) / Simplified.com for PPT generation
• Using prompt on Synthesia / Invideo AI for Video Generation

Tools Covered: ChatGPT, Midjourney, Dall E, MS Copilot, Synthesia, Invideo AI, Slides AI

Learn practically data mining, optimizing query performance, and ensuring data integrity on SQL. Advanced topics include NoSQL databases like MongoDB, distributed systems, and data warehousing, preparing students for diverse data roles.

Module 1: SQL – Structured Query Language 

• Introduction to SQL
• SQL & RDBMS
• SQL Syantax and data types
• CRUD operations in SQL
• Retrieving Data with SQL
• Filtering, sorting & formatting query results
• Advanced SQL Queries
• Database Design and Normalization
• Advanced Database Concepts
• Stored Procedures
• Integrating SQL with Python for Data

Hands-on practice:

• Joins, Sub-queries, Aggregation query
• Views, Filtering, Sorting
• Group By and Having clause

Module 2: MongoDB 

• Introduction to MongoDB
• MongoDB essentials
• Structure of MongoDB
• Advanced MongoDB Queries

• Integrating MongoDB with Python for Data

Tools Covered: MySQL, SQL Server, MongoDB

This unit consists of two of the most prominently used tools for data visualization & analytics: Power BI and Tableau. You will learn to create interactive dashboards, reports, and visualizations to analyze and communicate insights effectively.

Module 1: Power BI 

• Introduction to Power BI
• Data Preparation and Modeling
• Clean, transform & load data in Power BI
• Data Visualization Techniques
• Advanced Analytics in Power BI
• Designing Interactive Dashboards

• Power Query

• Design Power BI Reports
• Connecting Power BI to SQL
• Create, Share, and Collaborate on Power BI Dashboards

Class Project & Assignments:

Project 1: Education Institute’s student data analysis

Project 2: Sales Data Analysis

– Learn to visualize data to find patterns & insights using interactive charts

Module 2: Tableau

• Introduction to Tableau
• Connecting Tableau to data sources
• Data Types in Tableau
• Data Preparation and Transformation
• Building Visualizations in Tableau

• Advanced Analytics in Tableau
• Tableau Dashboards and Storytelling
• Connecting Tableau to SQL
• Tableau Online to collaborate, share & publish dashboards

Class Project & Assignments:

Project 1: Supermarket data analysis

Project 2: Covid Data Analysis

– Learn to visualize data to find patterns & insights using interactive charts

– Deployment of Predictive model in Tableau

Tools Covered: Power BI, Tableau, Excel

Module 1: Excel for Analytics 

• Introduction to Excel for Analytics
• Basic Formulas & Function
• Data Preparation and Cleaning
• Charts & Graphs in Excel
• Data Analysis Techniques in Excel
• PivotTables and PivotCharts for data summarization
• Data visualization techniques in Excel
• Excel’s data analysis add-ins

In this unit, big data analytics tools Spark and Hadoop, the key components of modern data processing ecosystems. You will learn to harness Spark’s distributed computing power, Hadoop’s storage and processing capabilities, and Kafka’s real-time data streaming for scalable data processing & analysis.

Module 1: Apache Hadoop 

• Overview of Big Data and Distributed Computing
• The Hadoop ecosystem and its components
• Architecture: HDFS and MapReduce
• Setting Up Hadoop Environment
• Managing files and directories in HDFS
• Performing HDFS operations
• MapReduce paradigm: mapper, reducer, and shuffle phases
• Running and monitoring MapReduce jobs on Hadoop clusters
• YARN and Hadoop Ecosystem
• Hadoop ecosystem projects: Hive, Pig, HBase, etc.
• SQOOP (SQL in HADOOP)
• Integrating Hadoop with other Big Data technologies

Module 2: Apache Spark 

• Overview of Apache Spark and its features
• Spark architecture: RDDs, DAGs, and transformations/actions
• Introduction to Spark ecosystem components
• Setting Up Spark Environment
• Managing Spark clusters with Apache Mesos or Hadoop YARN
• Understanding RDDs: creation, transformation, and actions
• Spark SQL and DataFrames
• Querying structured data with SQL and DataFrame operations
• Interoperability between RDDs and DataFrames
• Spark Streaming for real-time data processing
• Integrating Spark Streaming with Kafka
• Spark MLlib: machine learning library for Spark
• Building and training machine learning models with MLlib
• Performing analytics tasks with Spark MLlib

Tools Covered: Spark, Hadoop

In this cloud deployment unit, you will learn to deploy machine learning and AI models using AWS and Azure, two leading cloud platforms. You’ll gain proficiency in deploying, scaling, and managing models in the cloud environments through practical exercises.

Module 1: AWS

• Introduction to Cloud Deployment for ML and AI Models
• AWS cloud platform and its services for model deployment
• Understanding deployment architectures and best practices
• AWS IAM (Identity and Access Management)
• Elastic Compute Cloud (Amazon EC2)
• Elastic Block Storage (EBS) and Elastic File System (EFS)
• Model Deployment with AWS
• Model Deployment using Python on AWS using Flask
• Model Deployment using Python on AWS using Django

Module 2: Azure

• Azure cloud platform and its services for model deployment
• Understanding deployment architectures and best practices
• Fundamental Principles of Machine Learning on Azure
• Model Deployment on Azure
• Model Deployment using Python on Azure using Flask
• Model Deployment using Python on Azure using Django

Tools Covered: AWS, EC2, S3, ECS, Sagemaker, Lambda, Azure, Azure ML, Flask, Django