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 Business Analytics with Data Science

• 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

Application: 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.

A comprehensive overview of statistical concepts. Learn practically to analyze data by handling huge databases with SQL and analytics with Power BI, Tableau & Excel.

Module 1: Statistics for Decision-Making

• Fundamentals of Math, Probability & Statistics
• Descriptive vs inferential statistics
• Measure of central tendency & dispersion
• Handling outliers & missing values in data
• Covariance, Correlation, Covariance vs Correlation
• Visualizations for Categorical & Continuous features
• Types of data, Sample and Population
• Discrete and continuous probability distributions
• Normal distribution and central limit theorem
• Linear Algebra, Sampling and Estimation
• Hypothesis Testing Workflow
• Confusion Matrix, Performance Metrics

Class Hands-on:

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

Module 2: Power BI – Dashboard & Visualization

• 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 3: Tableau – Dashboard & Visualization

• 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 4: 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

Module 5: 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

You will learn predictive analytics using machine learning algorithms, and model building from scratch exploring various case studies to practice on real-world applications to reinforce your learning.

Module 1: Predictive Analytics with 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 2: Time-Series Forecasting 

• 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
• Forecasting

Class Projects:

• Project to predict the number of customers of an Airline organization
• Financial Market Stock Price analysis and forecasting
• Sales data forecasting to understand trend and seasonality

Tools Covered: SciKit Learn, Pandas, Matplotlib

Module 3: NLP – Natural Language Processing 

• 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
• Translation & summarization of News

Tools Covered: NLTK, Spacy, BERT

Note: This module will consist of projects related to Finance, Marketing, HR Analytics, Retail & Sales to provide domain exposure and practical training on making a business decision with analytics.

In this cloud deployment unit, you will learn to deploy models using AWS and Azure, two leading cloud platforms. 

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

In this unit, we’ll delve into prompt engineering tools based on generative AI models to help us craft effective prompts for guiding AI models to specific tasks.

Module 1: 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

In this unit, students will master the principles and practices of planning, organizing, executing, and controlling projects to achieve specific goals within constraints.

Module 1: Introduction to Project Management

• Importance of project management
• Project life cycle and phases
• Feasibility studies and project selection criteria
• Project Planning & Execution
• Performance measurement & Metrics
• Agile Project Management
• Project Management Tools
• Project management templates

Module 2: Agile & Scrum

• Introduction to Agile Methodologies
• Benefits & Challenges of Agile Implementation
• Understanding the Agile methodology and principles
• Scrum Framework Overview
• Scrum roles, events & artifacts
• Daily Scrum and Task Management
• Agile Planning and Estimation
• Sprint Execution and Delivery
• Scrum Master Role and Responsibilities
• Agile Execution and Monitoring
• Agile Metrics and Reporting
• Adaptation and Continuous Improvement
• Agile tools and software (Eg. Jira, Trello, Asana)

Module 3: Jira

• Introduction to Jira
• Jira projects, issues, and workflows
• Jira interface and project navigation
• Creating and Managing Projects
• Task Management and Collaboration
• Managing Issues and Workflows
• Configuring Agile Boards (Scrum & Kanban)
• Reporting and Dashboards
• Integrating Jira with other Tools and Systems

Tools Covered: Agile. Scrum, Jira, Kanban