Data Science Course in Hyderabad

➔    An Introduction to Python

➔    Introductory Remark about Python

-        A Brief History of Python

-        How Python differs from other languages

-        Python Version

➔    Getting Help

-        How to execute a Python program

-        Writing your first program: Google Colab, VS Code, and Anaconda

➔    Python Basics

-        Python keywords, variables, and Identifiers

➔    Decision making & Loops           

-        Introduction

-        Control flow and syntax

-        The if statement

-        Python operators

-        The while Loop

-        Break and continue

-        The for Loop

-        Pass statement

-        Exercise

➔    Data Structures

-        Introduction

-        List

-        Tuple

-        String

-        Dictionary

-        Set

-        Exercise

➔    Functions in Python

-        Introduction

-        Calling a function

-        Function arguments

-        Built-in function

-        Scope of variables

-        Decorators

-        Passing a function to a function

-        Lambda

-        Closures

-        Exercise

➔    Exception Handling

-        Errors

-        Run Time Errors

-        Handling IO Exception

-        Try…except statement

-        Raise

-        Assert

➔    Mini Project

Title: Simple Calculator Application

Description: Create a console-based calculator that takes user input for two numbers and performs mathematical operations (add, subtract, multiply, divide)

Tools Covered: Python basics (Control structures, basic I/O)

➔ An Introduction to Python 
             ➔ Introductory Remark about Python  
                       - A Brief History of Python 
                       - How Python differs from other languages 
                       - Python Version 
             ➔ Getting Help 
                       - How to execute a Python program 
                       - Writing your first program: Google Colab, VS Code, and Anaconda
➔ Python Basics 
                       - Python keywords, variables, and Identifiers 
                       - Python statements 
                       - Comments in Python 
                       - Command line arguments 
                       - Getting user input 
                       - Exercise 
➔ Decision making & Loops  
                       - Introduction 
                       - Control flow and syntax 
                       - The if statement 
                       - Python operators 
                       - The while Loop 
                       - Break and continue 
                       - The for Loop 
                       - Pass statement 
                       - Exercise 
➔ Data Structures  
                       - Introduction
                       - List 
                       - Tuple 
                       - String 
                       - Dictionary 
                       - Set 
                       - Exercise 
➔ Functions in Python 
                      - Introduction
                      - Calling a function 
                      - Function arguments 
                      - Built-in function 
                      - Scope of variables 
                      - Decorators 
                      - Passing a function to a function 
                      - Lambda 
                      - Closures 
                      - Exercise 
➔ Modules and Packages 
                      - Modules 
                      - Importing Modules 
                      - Standard Modules - sys 
                      - Standard Modules - OS 
                      - The dir Function 
                      - Packages 
                      - Exercise 
➔ Exception Handling 
                      - Errors 
                      - Run Time Errors 
                      - Handling IO Exception 
                      - Try…except statement 
                      - Raise 
                      - Assert 
➔ Files and Directories 
                      - Introduction 
                      - Writing Data to a file 
                      - Reading data from a file 
                      - Additional file methods 
                      - Working with files 
                      - Working with Directories 
                      - The pickle Module 
➔ Classes & Objects
                      - Introduction to classes and objects 
                      - Creating classes 
                      - Instance methods 
                      - Special class method 
                      - Inheritance 
                      - Method overriding 
                      - Data hiding 
                      - Exercise 
➔ Web Scraping using Beautiful Soup  
                      - Introduction to BeautifulSoup
                      - Understanding how BeautifulSoup works. 
                      - Initializing a BeautifulSoup object with HTML content 
                      - Navigating the HTML Tree Structure 
                      - Accessing specific elements by tag (find, find_all) 
                      - Working with attributes (class, id) 
                      - Extracting Content 
                      - Extracting text from elements 
                      - Extracting links, images, and other attributes 
                      - Dealing with Nested HTML Structures 
                      - Extracting content from nested tags 
                      - Navigating through parents, children, and sibling elements 
➔ Request Module in Python 
             ➔ Understanding HTTP Protocol  
                      - What is HTTP?
                      - Key components of an HTTP request: URL, headers, body, method (GET, POST, etc.) 
                      - HTTP status codes (200 OK, 404 Not Found, etc.) 
➔ Overview of Python's requests Module  
                      - Introduction to the requests library. 
                      - Comparison with other modules (urllib vs. requests). 
➔ GET Request 
                      - Syntax: requests.get(). 
                      - Passing query parameters (params). 
                      - Inspecting response objects (response.status_code, response.content, response.text). 
➔ POST Request
                      - Syntax: requests.post(). 
                      - Sending form data using data or JSON parameters. 
                      - Other HTTP Methods (Overview) 
                      - PUT, DELETE, HEAD, OPTIONS (with examples). 
➔ Inspecting Response Data
                      - Accessing response body (response.text vs response.content vs response.json()) 
                      - Checking status codes and their meaning (response.status_code) 
                      - Extracting response headers (response.headers) 
                      - JSON Responses 
                      - Working with JSON data (response.json()). 
                      - Handling non-JSON responses. 
➔ Sending Query Parameters in a GET Request 
                      - Using the params parameter to include query strings - 
                      - Example: requests.get(url, params = {'key1': 'value1', 'key2': 'value2'}) 
➔ Sending Data in a POST Request 
                      - Sending form data (data vs. JSON) 
➔ Mini Project  
                      - Title: Simple Calculator Application
                      - Description: Create a console-based calculator that takes user input for two numbers and performs 
                         mathematical operations (add, subtract, multiply, divide) 
                      - Tools Covered: Python basics (Control structures, basic I/O)

➔    Introduction to Databases and SQL

-        What is a Database?

-        Introduction to SQL

-        Creating and Managing Databases                          

➔    Working With Keys & Constraints

-        Describe the table

-        UNIQUE, NULL, NOT NULL

-        Check and Default

-        Primary Key, Foreign Key

-        Composite Key, Candidate Key, Super Key

-        Alter command

➔    Basic SQL Queries

-        SELECT Queries

-        Filtering Data with the WHERE Clause

-        ORDER BY and LIMIT

-        DISTINCT and COUNT Functions

-        Basic Arithmetic Operations and Aliases

➔    Data Manipulation and Transaction Control

-        Inserting Data

-        Updating and Deleting Data

-        Transaction Control

-        Handling NULL Values

➔    Working with Multiple Tables

-        Introduction to Relationships and Joins

-        INNER JOIN

-        LEFT, RIGHT, and FULL OUTER JOIN

-        CROSS JOIN and SELF JOIN

-        UNION and UNION ALL

➔    Aggregate Functions and Grouping

-        Aggregate Functions

-        GROUP BY Clause

-        HAVING Clause

-        ROLLUP and CUBE

-        CASE Statements

➔    Subqueries and Nested Queries

-        Introduction to Subqueries

-        Correlated Subqueries

-        EXISTS and NOT EXISTS

-        IN and NOT IN Subqueries

-        Common Table Expressions (CTEs)

➔ Introduction to Data Analysis 
                  - Overview of Data Analysis 
                  - Applications of Data Analysis in Real-world Scenarios 
                  - Data Types and Data Structures in Data Analysis 
➔ NumPy for Data Analysis 
                  - Introduction to NumPy 
                  - Working with Arrays 
                  - Array Operations and Broadcasting 
                  - Mathematical and Statistical Functions in NumPy 
➔ Data Manipulation with Pandas 
                  - Introduction to Pandas Library for Data Manipulation 
                  - Loading and Inspecting Datasets 
                  - Filtering, Sorting, and Selecting Data, indexing, and renaming 
                  - Handling Missing Data 
                  - Grouping and Aggregating Data 
➔ Data Cleaning Techniques
                  - Importance of Data Cleaning 
                  - Handling Duplicates and Invalid Data 
                  - Data Transformation Techniques 
                  - Data Imputation Techniques 
                  - Data Consistency and Integrity Checks 
➔  Exploratory Data Analysis (EDA)
                  - Introduction to EDA 
                  - Descriptive Statistics 
                  - Detecting and Treating Outliers 
                  - Data Distribution Visualization 
                  - Correlation and Covariance Analysis 
➔ Hypothesis Testing 
                  - Introduction to Hypothesis Testing 
                  - Types of Hypotheses (Null and Alternative) 
                  - t-Tests and ANOVA 
                  - Chi-Square Test 
                  - Interpreting p-values 
➔ Data Visualization with Matplotlib 
                  - Introduction to Matplotlib for Plotting 
                  - Creating Basic Line, Bar, and Scatter Plots 
                  - Customizing Plot Elements 
                  - Multi-plot Figures and Subplots 
➔ Advanced Data Visualization with Seaborn  
                  - Introduction to Seaborn Library
                  - Creating Heatmaps, Boxplots, and Violin Plots 
                  - Pairplots and Jointplots for Multivariate Analysis 
                  - Customizing Seaborn Visualizations 
➔ Time Series Data Analysis 
                  - Introduction to Time Series Data 
                  - Working with DateTime in Pandas 
                  - Plotting Time Series Data 
                  - Rolling, Shifting, and Resampling Time Series Data 
➔ Data Visualization for Business Insights 
                  - Creating Dashboards with Matplotlib and Seaborn 
                  - Storytelling with Data 
                  - Case Study: Building a Dashboard for Business Insights 
➔ Mini Project 
                  - Title: EDA and Data Cleaning on Titanic Dataset 
                  - Description: Perform exploratory data analysis and data cleaning on the Titanic 
dataset to prepare it for modeling 
                  - Tools Covered: Data wrangling with Pandas, handling missing values, outlier detection using Z-scores, 
feature engineering, bivariate and multivariate analysis, data visualization using Matplotlib, and 
Seaborn 

➔    Introduction to Excel

➔     Introduction to Excel Interface

-        Excel ribbon, workbook, worksheet, rows, columns, cells

-        Basic navigation and shortcuts

➔      Entering Data & Formatting Cells

-        Entering and editing data (text, numbers, dates)

-        Formatting (font, alignment, borders, number formats)

➔    Basic Formulas

-        Basic arithmetic operations (+,  -  ,  *  ,  /)

-        Using AutoSum, AutoFill

-        Introduction to cell references (relative, absolute)

➔    Working with Data           

➔    Sorting & Filtering Data

-        Sort data (ascending/descending)

-        Filtering using AutoFilter

➔    Data Validation & Drop-down Lists

-        Creating data validation rules

-        Setting up drop-down lists

➔     Find and Replace, Go To Special

-        Using Find & Replace for large datasets

-        Go To Special for selecting specific cell types (blanks, errors)

➔    Text Functions

-        CONCATENATE, LEFT, RIGHT, MID, TRIM, UPPER, LOWER

➔    Formulas and Functions

➔    Mathematical Functions

-        SUM, AVERAGE, MIN, MAX, COUNT

➔    Logical Functions

-        IF, AND, OR, NOT functions

-        Nested IF statements

➔    Lookup Functions

-         VLOOKUP, HLOOKUP

-        XLOOKUP (if available)

-        INDEX and MATCH

➔    Mini Project

Title: Student Performance and Attendance Analyzer

Description: Clean and organize student attendance and marks data, apply validation rules, calculate performance metrics using formulas.

Tools Covered: Data Import, Sorting/Filtering, Data Validation, Formulas & Functions.

➔    Introduction to Machine Learning 
               -    What is Machine Learning?
               -    Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
               -    Applications of Machine Learning in the Real World
➔    Supervised Learning: Regression
               -    Introduction to Regression
               -    Simple Linear Regression
               -    Multiple Linear Regression
               -    Polynomial Regression
               -    Regularization: Ridge, Lasso
➔    Supervised Learning: Classification
               -    Introduction to Classification
               -    Logistic Regression
               -    K-Nearest Neighbors (KNN)
               -    Support Vector Machines (SVM)
               -    Naive Bayes Classifier
➔    Model Evaluation and Tuning
               -    Confusion Matrix, Precision, Recall, F1-Score
               -    ROC Curve and AUC
               -    Cross-Validation
               -    Grid Search and Random Search
➔    Decision Trees and Random Forests
               -    Introduction to Decision Trees
               -    Decision Tree Algorithm: Classification and Regression
               -    Random Forests and Bagging
➔    Unsupervised Learning: Clustering
               -    Introduction to Clustering
               -    K-Means Clustering
               -    Hierarchical Clustering
               -    DBSCAN Algorithm
➔    Unsupervised Learning: Dimensionality Reduction
               -    Introduction to Dimensionality Reduction
               -    Principal Component Analysis (PCA)
➔    Ensemble Learning Techniques
               -    Introduction to Ensemble Methods
               -    Bagging: Random Forests
               -    Boosting: AdaBoost, Gradient Boosting, XGBoost
➔    Time Series Analysis
               -    Introduction to Time Series
               -    Time Series Forecasting with ARIMA
               -    Exponential Smoothing and Moving Averages
➔    Neural Network Introduction
               -    Introduction to Neural Networks
               -    Perceptron, Activation Functions
➔    Case Studies
               -    Case Study 1: Regression
               -    Case Study 2: Classification
               -    Case Study 3: Clustering
➔    What is Natural Language Processing (NLP) and Why It Matters?
               -    Definition of NLP and its role in Machine Learning
               -    Real-world applications: chatbots, sentiment analysis, translation, spam detection
➔    Text Preprocessing Basics
               -    Tokenization, stop-word removal, stemming & lemmatization
               -    Converting text to numbers: Bag of Words, TF-IDF
               -    Quick Python demo (e.g., using NLTK or spaCy)
➔    Feature Representation & Models
               -    Word embeddings: Word2Vec, BERT (conceptual overview)
               -    How embeddings capture meaning and context
               -    Example: Similarity between words using vectors
➔    Cloud Fundamentals
               -    What is cloud computing: IaaS, PaaS, SaaS
               -    Benefits: scalability, elasticity, pay-as-you-go
               -    Key concepts: regions, availability zones, instances
➔    AWS Core Services
               -    Compute: EC2, Lambda
               -    Storage: S3, EBS
               -    Database: RDS, DynamoDB
               -    Hands-on: upload data to S3 and access it in a notebook
➔    AWS for Machine Learning
               -    Amazon SageMaker overview: training, deployment, monitoring
               -    AI services: Comprehend, Rekognition, Polly, Lex
               -    Demo: Quick model deploy with SageMaker
➔    Integration and Best Practices
               -    Security and IAM basics
               -    Cost optimization and scaling
               -    Cloud-native ML project structure
 ➔    Introduction to MLOps
               -    What is MLOps and why it’s needed
               -    Difference between DevOps and MLOps
               -    Typical ML lifecycle: data → model → deployment → monitoring
➔    MLOps Pipeline
               -    Data versioning and management (DVC, Git, MLflow)
               -    Model training automation using CI/CD
               -    Model packaging with Docker
               -    Hands-on: simple MLflow model tracking demo
➔    Model Deployment with FastAPI
                       -    What is FastAPI?
               ➔    Creating a Simple Prediction API
                       -    Load trained model in FastAPI
                       -    Define a /predict endpoint
                       -    Use Pydantic for input validation
               ➔    Testing the API
                       -    Test requests and responses
               ➔    Deploying the FastAPI App
                       -    Basic Docker packaging
                       -    Run with Uvicorn
➔    Discussion / Use Case
               -    Real-world MLOps case study (e.g., churn model pipeline)
               -    Common challenges and best practices
➔    Mini Project
               -    Title: Customer Churn Prediction with Logistic Regression
               -    Description: Predict whether customers will leave a service using logistic regression
               -    Tools Covered: Data encoding (OneHot, Label Encoding), handling missing values (MCAR, MAR, MNAR), outlier treatment                              (IQR, Z-score methods), feature scaling, logistic regression with scikit-learn, and evaluation metrics

➔    Introduction to Data Analysis

-        Overview of Data Analysis

-        Applications of Data Analysis in Real-world Scenarios

-        Data Types and Data Structures in Data Analysis

➔    NumPy for Data Analysis

-        Introduction to NumPy

-        Working with Arrays

-        Array Operations and Broadcasting

-        Mathematical and Statistical Functions in NumPy

➔    Data Manipulation with Pandas

-        Introduction to Pandas Library for Data Manipulation

-        Loading and Inspecting Datasets

-        Filtering, Sorting, and Selecting Data, indexing, and renaming

-        Handling Missing Data

-        Grouping and Aggregating Data

➔     Exploratory Data Analysis (EDA)

-        Introduction to EDA

-        Descriptive Statistics

-        Detecting and Treating Outliers

-        Data Distribution Visualization

-        Correlation and Covariance Analysis

➔    Hypothesis Testing

-        Introduction to Hypothesis Testing

-        Types of Hypotheses (Null and Alternative)

-        t-Tests and ANOVA

-        Chi-Square Test

-        Interpreting p-values

➔    Data Visualization with Matplotlib

-        Introduction to Matplotlib for Plotting

-        Creating Basic Line, Bar, and Scatter Plots

➔    Advanced Data Visualization with Seaborn

-        Introduction to Seaborn Library

-        Creating Heatmaps, Boxplots, and Violin Plots

➔    Mini Project

Title: EDA and Data Cleaning on Titanic Dataset

Description: Perform exploratory data analysis and data cleaning on the Titanic dataset to prepare it for modeling

Tools Covered: Data wrangling with Pandas, handling missing values, outlier detection using Z-scores, feature engineering, bivariate and multivariate analysis, data visualization using Matplotlib, and Seaborn

➔    Introduction to Deep Learning
                 -    What is Deep Learning?
                 -    Neural Networks Overview
                 -    Key Concepts: Neurons, Weights, Activation Functions
➔    Feedforward Neural Networks (FNNs)
                 -    Architecture of Feedforward Neural Networks
                 -    Forward and Backpropagation
                 -    Gradient Descent and Optimization
➔    Deep Learning Frameworks
                 -    Introduction to TensorFlow and Keras
                 -    Building a Basic Neural Network with Keras
                 -    Training and Evaluating the Model
➔    Recurrent Neural Networks (RNNs)
                 -    Introduction to sequence modeling
                 -    Architecture of a basic RNN
                 -    Hidden state and temporal dependencies
                 -    Challenges: vanishing & exploding gradients
                 -    Applications: text, speech, time-series
➔    Long Short-Term Memory Networks (LSTMs)
                 -    Need for LSTMs vs. traditional RNNs
           ➔    LSTM architecture overview
                 -    Input gate
                 -    Forget gate
                 -    Output gate
                 -    How LSTMs capture long-range dependencies
                 -    Applications: language modeling, translation, forecasting
➔    Implementation Basics
                 -    Preparing sequential data for RNN/LSTM
                 -    Building simple RNN/LSTM models in Keras/PyTorch
                 -    Training, evaluation, and prediction
                 -    When to use RNN vs. LSTM
➔    Convolutional Neural Networks (CNNs)
                 -    Introduction to Convolutional Layers
                 -    Pooling Layers and Fully Connected Layers
                 -    Building a CNN for Image Classification
➔    Image Preprocessing
                 -    Image Rescaling, Normalization, and Augmentation
                 -    Data Augmentation Techniques
                 -    Preparing Datasets for Training
➔    Transfer Learning
                 -    Concept of Transfer Learning
                 -    Using Pre-trained Models (VGG, ResNet, etc.)
                 -    Fine-Tuning Pre-trained Models
➔    Object Detection and Localization
                 -    Introduction to Object Detection
                 -    YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector)
                 -    Applications of Object Detection
➔    Case Studies
                 -    Case Study 1: Image Classification with CNN
                 -    Case Study 2: Object Detection using YOLO
➔    Mini Project
                 -    Title: Smart Image Classifier for Everyday Objects
                 -    Description: Build a deep learning model that classifies everyday objects (e.g., fruits, vehicles, or household items) using an                           image dataset, apply preprocessing techniques, train a CNN, and evaluate performance with visualizations
                 -    Tools Covered: Neural Networks (Feedforward & CNNs), Image Processing, TensorFlow/PyTorch, Object Detection Concepts

➔    Introduction to Machine Learning

-        What is Machine Learning?

-        Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

-        Applications of Machine Learning in the Real World

➔    Supervised Learning: Regression

-        Introduction to Regression

-        Simple Linear Regression

-        Multiple Linear Regression

-        Polynomial Regression

-        Regularization: Ridge, Lasso

➔    Supervised Learning: Classification

-        Introduction to Classification

-        Logistic Regression

-        K-Nearest Neighbors (KNN)

-        Support Vector Machines (SVM)

-        Naive Bayes Classifier

➔    Model Evaluation and Tuning

-        Confusion Matrix, Precision, Recall, F1-Score

-        ROC Curve and AUC

-        Cross-Validation

-        Grid Search and Random Search

➔    Decision Trees and Random Forests

-        Introduction to Decision Trees

-        Decision Tree Algorithm: Classification and Regression

-        Random Forests and Bagging

➔    Unsupervised Learning: Clustering

-        Introduction to Clustering

-        K-Means Clustering

-        Hierarchical Clustering

-        DBSCAN Algorithm

➔    Ensemble Learning Techniques

-        Introduction to Ensemble Methods

-        Bagging: Random Forests

-        Boosting: AdaBoost, Gradient Boosting, XGBoost

➔    Time Series Analysis

-        Introduction to Time Series

-        Time Series Forecasting with ARIMA

-        Exponential Smoothing and Moving Averages

➔    Case Studies

-        Case Study 1: Regression

-        Case Study 2: Classification

-        Case Study 3: Clustering

➔    What is Natural Language Processing (NLP) and Why It Matters?

-        Definition of NLP and its role in Machine Learning

-        Real-world applications: chatbots, sentiment analysis, translation, spam detection

➔    Text Preprocessing Basics

-        Tokenization, stop-word removal, stemming & lemmatization

-        Converting text to numbers: Bag of Words, TF-IDF

-        Quick Python demo (e.g., using NLTK or spaCy)

➔    Feature Representation & Models

-        Word embeddings: Word2Vec, BERT (conceptual overview)

-        How embeddings capture meaning and context

-        Example: Similarity between words using vectors

➔    Mini Project

Title: Customer Churn Prediction with Logistic Regression

Description: Predict whether customers will leave a service using logistic regression

Tools Covered: Data encoding (OneHot, Label Encoding), handling missing values (MCAR, MAR, MNAR), outlier treatment (IQR, Z-score methods), feature scaling, logistic regression with scikit-learn, and evaluation metrics

➔    Cloud Fundamentals

-        What is cloud computing: IaaS, PaaS, SaaS

-        Benefits: scalability, elasticity, pay-as-you-go

-        Key concepts: regions, availability zones, instances

➔    AWS for Machine Learning

-        Amazon SageMaker overview: training, deployment, monitoring

-        AI services: Comprehend, Rekognition, Polly, Lex

-        Demo: Quick model deploy with SageMaker

➔    Integration and Best Practices

-        Security and IAM basics

-        Cost optimization and scaling

-        Cloud-native ML project structure

➔    Introduction to MLOps

-        What is MLOps and why it’s needed

-        Difference between DevOps and MLOps

-        Typical ML lifecycle: data → model → deployment → monitoring

➔    MLOps Pipeline

-        Data versioning and management (DVC, Git, MLflow)

-        Model training automation using CI/CD

-        Model packaging with Docker

-        Hands-on: simple MLflow model tracking demo

➔    Model Deployment with FastAPI

-        What is FastAPI?

➔     Creating a Simple Prediction API

-        Load trained model in FastAPI

-        Define a /predict endpoint

-        Use Pydantic for input validation

➔    Testing the API

-        Test requests and responses

➔    Deploying the FastAPI App

-        Basic Docker packaging

-        Run with Uvicorn

➔    Discussion / Use Case

-        Real-world MLOps case study (e.g., churn model pipeline)

Common challenges and best practices

➔    Introduction to Databases and SQL
                  -    What is a Database?
                  -    Introduction to SQL
                  -    Creating and Managing Databases
➔    Working With Keys & Constraints
                  -    Describe the table
                  -    UNIQUE, NULL, NOT NULL
                  -    Check and Default
                  -    Primary Key, Foreign Key
                  -    Composite Key, Candidate Key, Super Key
                  -    Alter command
➔    Basic SQL Queries
                  -    SELECT Queries
                  -    Filtering Data with the WHERE Clause
                  -    ORDER BY and LIMIT
                  -    DISTINCT and COUNT Functions
                  -    Basic Arithmetic Operations and Aliases
➔    Data Manipulation and Transaction Control
                  -    Inserting Data
                  -    Updating and Deleting Data
                  -    Transaction Control
                  -    Handling NULL Values
➔    Working with Multiple Tables
                  -    Introduction to Relationships and Joins
                  -    INNER JOIN
                  -    LEFT, RIGHT, and FULL OUTER JOIN
                  -    CROSS JOIN and SELF JOIN
                  -    UNION and UNION ALL
➔    Aggregate Functions and Grouping
                  -    Aggregate Functions
                  -    GROUP BY Clause
                  -    HAVING Clause
                  -    ROLLUP and CUBE
                  -    CASE Statements
➔    Subqueries and Nested Queries
                  -    Introduction to Subqueries
                  -    Correlated Subqueries
                  -    EXISTS and NOT EXISTS
                  -    IN and NOT IN Subqueries
                  -    Common Table Expressions (CTEs)
➔    Advanced SQL for Data Science
                  -    Window Functions
                  -    LEAD and LAG Functions
                  -    PIVOT and UNPIVOT
                  -    Recursive Queries
                  -    Materialized Views and Indexing

➔    Introduction to Statistics for Data Science
               -    Importance of Statistics in Data Science
               -    Descriptive vs. Inferential Statistics
               -    Data Types and Measurement Scales
➔    Descriptive Statistics
               -    Measures of Central Tendency: Mean, Median, Mode
               -    Measures of Spread: Range, Variance, Standard Deviation
               -    Data Distribution: Normal, Skewed, and Kurtosis
➔    Inferential Statistics
               -    Sampling Techniques
               -    Confidence Intervals
               -    Hypothesis Testing: t-tests and p-values
➔    Probability Theory
               -    Introduction to Probability
               -    Conditional Probability and Bayes' Theorem
               -    Probability Distributions: Binomial, Poisson, and Normal
➔    Correlation and Regression
               -    Correlation Coefficients
               -    Simple Linear Regression
               -    Multiple Linear Regression
➔    Introduction to Linear Algebra for Data Science
               -    Vectors and Scalars
               -    Matrices and Matrix Operations
               -    Matrix Transposition and Inverses
➔    Eigenvalues and Eigenvectors
               -    Introduction to Eigenvalues and Eigenvectors
               -    Applications of Eigenvalues in Data Science
➔    Mini Project
               -    Title: Product Performance Statistical Insight Report
               -    Description: Use a real or sample product dataset to analyze trends, test hypotheses, measure relationships between                                     variables, and build a simple regression model to identify key drivers of performance using solid statistical reasoning.
               -    Tools Covered: Statistics, Probability, Correlation & Regression, Linear Algebra

➔    Introduction to Deep Learning

-        What is Deep Learning?

-        Neural Networks Overview

-        Key Concepts: Neurons, Weights, Activation Functions

-        Feedforward Neural Networks (FNNs)

-        Forward and Backpropagation (conceptual)

-        Gradient Descent and Optimization (intuition only)          

➔    Deep Learning Frameworks

-        Introduction to TensorFlow and Keras

-        Building a Basic Neural Network with Keras

-        Training and Evaluating the Model

➔    Convolutional Neural Networks (CNNs)

-        Introduction to Convolutional Layers

-        Pooling Layers and Fully Connected Layers

-        Image Preprocessing

-        Image Rescaling and Normalization

-        Basic Data Augmentation Techniques

-        Building a CNN for Image Classification  

➔    Transfer Learning

-        Concept of Transfer Learning

-        Using Pre-trained Models (VGG, ResNet, etc.)

-        Fine-Tuning Pre-trained Models

➔    Recurrent Neural Networks (RNNs)

-        Introduction to Sequence Modeling

-        Basic RNN Architecture and Limitations

-        Need for LSTMs vs Traditional RNNs

-        LSTM Architecture Overview

-        Input Gate, Forget Gate, Output Gate (conceptual)

-        Applications: text, speech, time-series

-        When to use RNN vs LSTM

➔    Case Studies

-        Case Study 1: Image Classification with CNN

-        Case Study 2: Object Detection using YOLO

➔    Mini Project

-        Title: Smart Image Classifier for Everyday Objects

-        Description: Build a deep learning model that classifies everyday objects (e.g., fruits, vehicles, or household items) using an image dataset, apply preprocessing techniques, train a CNN, and evaluate performance with visualizations

-        Tools Covered: Neural Networks (Feedforward & CNNs), Image Processing, TensorFlow/PyTorch, Object Detection Concepts

➔    Introduction to Statistics for Data Science

-        Importance of Statistics in Data Science

-        Descriptive vs. Inferential Statistics

-        Data Types and Measurement Scales      

➔    Descriptive Statistics

-        Measures of Central Tendency: Mean, Median, Mode

-        Measures of Spread: Range, Variance, Standard Deviation

-        Data Distribution: Normal, Skewed, and Kurtosis

➔    Inferential Statistics

-        Sampling Techniques

-        Confidence Intervals

-        Hypothesis Testing: t-tests and p-values

➔    Probability Theory

-        Introduction to Probability

-        Conditional Probability and Bayes' Theorem

-        Probability Distributions: Binomial, Poisson, and Normal

➔    Introduction to Linear Algebra for Data Science

-        Vectors and Scalars

-        Matrices and Matrix Operations

-        Matrix Transposition and Inverses

➔    Eigenvalues and Eigenvectors

-        Introduction to Eigenvalues and Eigenvectors

-        Applications of Eigenvalues in Data Science

➔    Mini Project

Title: Product Performance Statistical Insight Report

Description: Use a real or sample product dataset to analyze trends, test hypotheses, measure relationships between variables, and build a simple regression model to identify key drivers of performance using solid statistical reasoning.

Tools Covered: Statistics, Probability, Correlation & Regression, Linear Algebra

➔    Introduction to AI
                          -    Definition and evolution of AI
                          -    Difference between Artificial Intelligence, Machine Learning (ML), and Deep Learning (DL)
                ➔    Common AI applications in daily life
                          -    Recommendation systems
                          -    Voice assistants
                          -    Chatbots
                          -    Autonomous vehicles
➔    Core Components of AI
                ➔    Data and training
                          -    Labeled vs. unlabeled data
                ➔    Algorithms and models
                          -    Supervised, unsupervised, and reinforcement learning
                ➔    Neural networks overview
                          -    Basic structure (input, hidden, output layers)
               ➔    Importance of feedback loops
➔    Generative AI Simplified
                          -    Introduction to Generative AI
                          -    Difference between Predictive and Generative AI
                ➔    Foundation Models
                          -    GPT, Gemini, Claude, LLaMA
                ➔    Diffusion Models
                          -    Basics of image generation
                ➔    How LLMs work
                          -    Tokenization
                          -    Embedding generation
                          -    Transformer architecture (attention mechanism overview)
                ➔    Basic algorithms
                          -    TF-IDF, n-gram
                ➔    Similarity search and vector databases
                          -    Concept and working
                ➔    RAG (Retrieval-Augmented Generation)
                ➔    Introduction to Agentic AI
➔    Integration of OpenAI
                          -    APIs Techniques for effectively integrating OpenAI APIs, including authentication and best practices for data handling.                                    Rate limits and error handling
➔    Transformers and Attention Mechanisms
                          -    Introduction to transformers and self-attention, BERT, GPT, and other transformer-based architectures.
                          -    Hands-on exercise: Finetuning a transformer model for text generation
➔    GANs (Generative Adversarial Networks)
                          -    Fundamentals of GANs and their components (Generator and Discriminator). Variants of GANs (DCGAN, StyleGAN,                                          CycleGAN). Applications of GANs in image synthesis, art creation, and more.
                          -    Hands-on exercise: Generate tabular data
➔    Introduction to AIOps
                          -    What is AIOps and how it extends DevOps
                          -    Use of AI/ML for IT automation and anomaly detection
                          -    Key pillars: Data ingestion, analytics, visualization, automation
➔    Architecture & Workflow
                          -    Data sources: logs, metrics, events
                          -    ML pipeline for event correlation and root cause analysis
                          -    Tools: Splunk, Dynatrace, Moogsoft, IBM Watson AIOps
➔    Use Cases
                          -    Predictive alerting and anomaly detection
                          -    Automated incident resolution and ticket routing
                          -    Performance optimization
➔    Hands-on / Conceptual Demo
                          -    Example: Using Python or ELK Stack for log anomaly detection
                          -    How AI models can identify abnormal system behavior
➔    Prompt Engineering
                          -    Text generation using ChatGPT or Gemini
                ➔    Prompt engineering basics
                          -    Clear and contextual prompts
                          -    Role-based prompting
                          -    Prompt refinement
➔    AI Tools for Productivity and Creativity
                ➔    AI for Office & Productivity
                          -    ChatGPT, Copilot, Gemini
                ➔    Automating writing, summarization, and email replies
➔    Responsible AI
                          -    Ethics and risks in AI
                          -    Bias in AI models
                          -    Privacy and data protection
                          -    Deepfakes and misinformation
➔    Mini Project
                          -    Title: Conversational AI model
                          -    Description: AI model that simulates a customer service assistant for a chosen domain (e.g., retail or tech support)
                          -    Tools Covered: LLM, ChatGPT, GPT-3

1.    US Health Care Analysis                                                                                                                                                                                           Domain: Health Care

2.    Emotion Recognition
       Domain: Marketing, Customer Services, and Human-Computer Interaction

3.    Distracted Driver Recognition
        Domain: Automotive and Transportation Safety

4.    E-commerce Product Management                                                                                                                                                                        Domain: E-commerce

5.    Time Series Forecasting of Retail Sales                                                                                                                                                                    Domain: Supply chain
 
6.    Customer Attrition Prediction                                                                                                                                                                                  Domain: Telecom, Finance

7.    Movie Recommendation System                                                                                                                                                                              Domain: Media and Entertainment

8.    Crop Yield Prediction                                                                                                                                                                                                Domain: Agriculture

Generative AI (10 hours)

➔    Introduction to AI & Generative AI Foundations

-        Definition and evolution of Artificial Intelligence

-        Difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)

-        Common AI applications in daily life

o   Recommendation systems

o   Voice assistants

o   Chatbots

-        Introduction to Generative AI

-        Difference between Predictive AI and Generative AI

➔    Foundation Models & LLM Basics

-        Foundation Models overview

o   GPT, Gemini, Claude, LLaMA

-        How Large Language Models (LLMs) work

o   Tokenization

o   Embeddings

o   Attention mechanism (conceptual overview)

➔    Transformers and Attention Mechanisms

-  Introduction to transformers and self-attention

-  BERT, GPT, and other transformer-based architectures.

-  Hands-on exercise: Finetuning a transformer model for text generation

➔     Text, Embeddings & Retrieval

-        Traditional text techniques (context-setting only)

o   TF-IDF

o   n-grams

-        Embeddings and semantic similarity

-        Vector databases and similarity search

-        Retrieval-Augmented Generation (RAG)

o   Why RAG is needed

o   High-level RAG workflow

o   Use cases in enterprise data

➔    Prompt Engineering & Productivity Tools

-        Prompt engineering fundamentals

o   Clear and contextual prompts

o   Role-based prompting

o   Prompt refinement techniques

-        Text generation using ChatGPT / Gemini

-        AI tools for productivity and creativity

o   ChatGPT, Copilot, Gemini

o   Writing, summarization, email replies

AGENTIC AI & APPLIED AI SYSTEMS (6 HOURS)

➔    Introduction to Agentic AI

-        What is Agentic AI

-        Difference between Chatbots and AI Agents

-        Core concepts

o   Goal-driven behaviour

o   Memory

o   Tool usage

o   Feedback loops

-        Real-world examples of agent-based systems

➔    AI Agents with APIs & Tools

-        Integrating LLM APIs (OpenAI / Gemini – conceptual + demo level)

-        API fundamentals

o   Authentication

o   Rate limits

o   Error handling

o   Data handling best practices

-        Tools and function calling (conceptual)

-        Multi-step reasoning workflows

➔    Applied AI Systems & AIOps Overview

-        Introduction to AIOps

-        How AIOps extends DevOps

-        Core pillars

o   Data ingestion

o   Analytics

o   Automation

-        Use cases

o   Anomaly detection

o   Predictive alerting

o   Automated incident resolution

-        Tools overview (no deep dive)

o   Splunk, Dynatrace, IBM Watson AIOps

➔    Responsible & Ethical AI

-        Ethics and risks in AI

-        Bias in AI models

-        Privacy and data protection

-        Deepfakes and misinformation

-        Responsible usage in enterprise AI systems

➔    Mini Project

-        Title: Conversational AI model

-        Description: AI model that simulates a customer service assistant for a chosen domain (e.g., retail or tech support)

-        Tools Covered: LLM, ChatGPT, GPT-3

1.     US Health Care Analysis
Domain: Health Care

2.     Emotion Recognition
Domain: Marketing, Customer Services, and Human-Computer Interaction

3.     Distracted Driver Recognition

Domain: Automotive and Transportation Safety

4.     E-commerce Product Management

Domain: E-commerce

5.     Time Series Forecasting of Retail Sales

Domain: Supply chain

6.     Customer Attrition Prediction

Domain: Telecom, Finance

7.     Movie Recommendation System
Domain: Media and Entertainment

8.     Crop Yield Prediction

Domain: Agriculture

“Self-Learning” module videos are available on the LMS dashboard throughout the training course, allowing you to learn at your own pace and convenience.

     -    Installation
     -    Introduction to Power BI
     -    Power Query
     -    DAX
     -    Functions
     -    Visualizations
     -    Reports
     -    Dashboards

“Self-Learning” module videos are available on the LMS dashboard throughout the training course, allowing you to learn at your own pace and convenience.

     -    Installation
     -    Introduction to Tableau
     -    Tableau UI
     -    Connecting to Excel files
     -    Tableau Charts and graphs
     -    Functions
     -    Reports
     -    Dashboards

Self-Learning” module videos are available on the LMS dashboard throughout the training course, allowing you to learn at your own pace and convenience.

-        Installation

-        Introduction to Power BI

-        Power Query

-        DAX

-        Functions

-        Visualizations

-        Reports

-        Dashboards

“Self-Learning” module videos are available on the LMS dashboard throughout the training course, allowing you to learn at your own pace and convenience.

-        Installation

-        Introduction to Tableau

-        Tableau UI

-        Connecting to Excel files

-        Tableau Charts and graphs

-        Functions

-        Reports

-        Dashboards