Data Analytics Course in Pune

Python Essentials: Learn, Code, Create

Python Basics: Learn to Code

• Introduction to Python Programming:
  • Basic Data Types in Python
  • Variables and Naming Conventions
• Data Types in Python:
  • Data Structure
  • Python String Object
  • Python List and Tuple Objects
  • Python Dictionary Object
  • Python Set Object
• Indexing & Slicing:
  • Importance of indexing in Python
  • Introduction to Slicing
  • Indexing and Slicing in Strings
  • Indexing and Slicing in Lists and Tuples
• Operators in Python:
  • Arithmetic Operators
  • Comparison Operators
  • Logical Operators
  • Assignment Operators
  • Membership Operators
• In-Built Functions & Methods:
  • Exploring Built-in Functions
  • Use Built-in Functions with data types
  • Introducing Built-in Methods for specific data types

• Python Control Flow Simplified

  • Statements, Indentation and Conditionals:
    • Statements and Indentation
    • Introduction to Conditional Statements
    • Combining Multiple Conditions
  • Loops & Iterations:
    • Conditional Loops (While Loop)
    • Conditional Loops (For Loop)
    • Master ‘for’ loops, Learn ‘while’ loops
    • Comprehend loop control statements
    • Explore nested loops

• Building Smarter Programs

  • Custom Functions in Python:
    • Introduction to Custom Functions
    • Defining and Calling Custom Functions in Python
    • Working with Arguments and Return Values
  • Advanced Looping Concepts:
    • List Comprehension
    • Set Comprehension
    • Dictionary Comprehension
    • Lambda Functions
  • OOPs in Python:
    • Introduction to Object-Oriented Programming (OOPs)
    • Creating and Working with Classes
    • Understanding Objects and Instances
    • Working with Attributes
    • Utilizing Methods in Classes
    • Embracing Polymorphism
    • Leveraging Inheritance
  • Exception Handling & Logging:
    • Introduction to Exception Handling
    • Try-Except Block: Catching Exceptions
    • Handling Specific Exceptions
    • Finally Block: Cleaning Up After Exceptions
    • Raising Exceptions: Taking Control
    • Handling Multiple Exceptions: The Exceptional Mastery

Numerical Programming in Python

• Python Data Structures and Algorithms

  • Data Structures Fundamentals:
    • Introduction to Data Structures
    • Arrays
  • Efficient String Operations:
    • Introduction to Strings
    • Combining strings using concatenation (+)
    • Searching and Finding
    • Introduction to regex
  • Recursion Fundamentals:
    • Understanding Recursion: Introduction and Basics
    • Recursive Function Design
    • Implementing Recursion in Algorithms
    • Analyzing Recursive Calls
  • Mastering Recursion Concept:
    • Time and space complexity
  • Algorithm Fundamentals:
    • Introduction to Algorithms
    • Big O Notation
    • Sorting Algorithms
    • Searching Algorithms

• Python Data Wrangling

  • Getting Started with Pandas & Numpy:
    • Introduction to Pandas & Numpy
  • Mastering Data Wrangling:
    • Series and dataframes
    • Indexing and slicing
    • Manipulating data
  • Advanced Data Wrangling Concepts:
    • Introduction to Data Wrangling
    • Handling Missing Data
    • Merging and Joining Data
    • Grouping and Aggregation
    • Reshaping Data
    • Filtering Data
  • Data Wrangling – Code Optimisation:
    • Techniques for Code Optimization
    • Strategies for Parallel and Distributed Data Wrangling
    • Best Practices for Efficient Data Wrangling
  • Data Wrangling on Different Data Formats:
    • Data Wrangling on Different Data Formats
  • Data Management Libraries:
    • File Management Libraries
    • Data Manipulation Libraries
    • Data Visualization Libraries
    • Web Scraping
    • Regular Expression Libraries
    • Date and Time Libraries
  • Web Scraping using Python:
    • Introduction to Web Scraping

• Python Visualization Libraries in Action

  • Data Visualization with Matplotlib & Seaborn:
    • Introduction to Matplotlib and Seaborn Plots

Relational Databases

• Introduction to SQL: Learn the Lang. of Data
• SQL Basics for Data Analysis
  • Introduction to SQL
  • Setting up the SQL environment
  • Basic SQL Commands
  • Creating and Deleting Databases and Tables
  • Importing and Exporting Data from CSV Files
• Fundamentals of SQL Query
  • Anatomy of SQL Query
  • SQL Data Types and Operators
  • Filtering and Sorting Data in SQL
  • Aggregate functions in SQL
• Dealing With Multiple Tables
  • Grouping Data – GROUP BY
  • HAVING
  • Subqueries
  • Joining tables using INNER JOIN, LEFT JOIN, RIGHT JOIN & FULL OUTER JOIN
  • Alias in SQL queries
  • Working with Multiple Tables Using Subqueries
  • Using Set Operators
  • Aggregating Data from Multiple Tables using GROUP BY and HAVING
• Advanced SQL Joins
  • Advanced Join Techniques
  • Joining multiple tables
  • Handling duplicate records and eliminating duplicates
  • Using UNION and UNION ALL to combine data from multiple tables
  • SQL In-Built Functions
• Type Casting & Math Functions
  • Mathematical Functions
  • Type Conversion Functions
  • Using CASE Statements to Perform Conditional Operations
• DateTime & String Functions
  • Working with date/time data in SQL
  • Date/time functions
  • Formatting date/time data
  • String manipulation functions (UPPER, LOWER, LEFT, RIGHT, etc.)
  • Regular expressions in SQL for string operations
  • Using CONCAT_WS to concatenate strings with a separator
• Window Functions
  • Syntax of Windows Function
  • Ranking functions (ROW_NUMBER, RANK, DENSE_RANK, etc.)
  • Aggregate functions using windows (SUM, AVG, MAX, MIN, etc.)
  • Partitioning data for window functions
  • Understanding row-based vs aggregate-based window functions

Microsoft Excel

• Introduction to Excel
  • Excel Interface Overview
  • Navigating the Ribbon
  • Understanding Worksheets and Workbooks
  • Creating and Saving Workbooks
  • Data Entry and Editing
  • Customizing the Quick Access Toolbar
  • Excel File Formats
• Basics of Excel
  • Basic Data Types (Numbers, Text, Dates)
  • Basic Excel Operations (Copy, Cut, Paste)
  • Autofill and Flash Fill
  • Sorting and Filtering Data
  • Basic Cell Formatting (Font, Color, Alignment)
  • Using the Clipboard
  • Adjusting Column Width and Row Height
• Excel Formula
  • Basic Arithmetic Formulas (SUM, SUBTRACT, MULTIPLY, DIVIDE)
  • AutoSum Function
  • Cell References (Relative, Absolute, Mixed)
  • Common Statistical Functions (AVERAGE, MEDIAN, MODE)
  • Text Formulas (CONCATENATE, LEFT, RIGHT)
  • Date Formulas (TODAY, NOW, DATE)
  • Logical Formulas (IF, AND, OR)
• Formula Based Formatting
  • Conditional Formatting Rules
  • Highlighting Cells Based on Values
  • Data Bars, Color Scales, and Icon Sets
  • Custom Conditional Formatting
  • Managing Conditional Formatting Rules
  • Using Formulas in Conditional Formatting
  • Applying Conditional Formatting to Pivot Tables
• Conditional Statements & Logical Operators
  • IF Statements
  • Nested IF Statements
  • AND, OR Operators
  • NOT Operator
  • Combining Multiple Conditions
  • Using IF with Text, Numbers, and Dates
  • Conditional Formatting with Logical Operators
• Text Functions
  • CONCATENATE (or CONCAT)
  • LEFT, RIGHT, MID
  • LEN and TRIM
  • UPPER, LOWER, PROPER
  • SUBSTITUTE and REPLACE
  • FIND and SEARCH
  • TEXT Function for Formatting Numbers and Dates
• Date and Time Functions
  • TODAY and NOW
  • DATE and TIME
  • DAY, MONTH, YEAR
  • HOUR, MINUTE, SECOND
  • DATEDIF
  • NETWORKDAYS
  • EDATE and EOMONTH
• Excel Tables
  • Creating and Formatting Excel Tables
  • Table Styles and Options
  • Sorting and Filtering Tables
  • Adding and Removing Table Rows/Columns
  • Structured References
  • Table Calculations and Totals
  • Converting Table to Range
• Basic and Advanced Table Operations
  • Creating Dynamic Tables
  • Using Table Formulas
  • Advanced Filtering Techniques
  • Using Slicers with Tables
  • Advanced Sorting Techniques
  • Working with Subtotals
  • Merging and Splitting Tables
• Data Pivoting with Different Examples
  • Creating PivotTables
  • PivotTable Fields and Areas
  • Grouping Data in PivotTables
  • PivotTable Calculations (Sum, Count, Average)
  • Creating Pivot Charts
  • Filtering PivotTables with Slicers and Timelines
  • Advanced PivotTable Techniques (Calculated Fields, Custom Calculations)
• Cell Reference Functions
  • Understanding Cell References
  • Using Relative References
  • Using Absolute References
  • Mixed References
  • INDIRECT Function
  • OFFSET Function
  • Linking Cells Between Sheets and Workbooks
• LookUp and VLookUp
  • Introduction to LookUp Functions
  • VLOOKUP Basics
  • HLOOKUP Basics
  • Using VLOOKUP with Exact and Approximate Match
  • Combining VLOOKUP with Other Functions
  • Common VLOOKUP Errors and Fixes
  • Alternatives to VLOOKUP (INDEX and MATCH)
• Excel Data Visualization
  • Creating Basic Charts (Column, Line, Pie)
  • Customizing Chart Elements (Titles, Legends, Labels)
  • Using Sparklines for Miniature Charts
  • Creating Combo Charts
  • Using Conditional Formatting for Data Visualization
  • Creating Heat Maps
  • Using Data Bars and Color Scales
• Basic Charts in Excel
  • Creating Column Charts
  • Creating Line Charts
  • Creating Pie Charts
  • Creating Bar Charts
  • Creating Area Charts
  • Creating Scatter Plots
  • Customizing Chart Types and Styles
• Formatting Charts
  • Adding and Formatting Chart Titles
  • Customizing Axes (Titles, Scales, Gridlines)
  • Formatting Data Series (Colors, Styles)
  • Adding Data Labels and Legends
  • Applying Chart Styles and Themes
  • Using Templates for Charts
  • Interactive Charts with Form Controls
• Introduction to Power Query
  • What is Power Query?
  • Power Query Interface Overview
  • Loading Data into Power Query
  • Basic Data Transformation (Filter, Sort, Remove Columns)
  • Combining Data from Multiple Sources
  • Data Profiling in Power Query
  • Saving and Loading Data to Excel
• Creating Reports in Excel with Dataset
  • Data Preparation and Cleaning
  • Using PivotTables for Reports
  • Combining Multiple Data Sources
  • Adding Visual Elements (Charts, Sparklines)
  • Creating Interactive Dashboards
  • Automating Report Updates
  • Distributing and Sharing Reports
• VBA and Macros
  • Introduction to VBA and Macros
  • Recording and Running Macros
  • Editing Macro Code
  • VBA Basics (Variables, Loops, Conditionals)
  • Writing Custom Functions
  • Automating Tasks with VBA
  • Debugging and Error Handling in VBA
• Power Query Editor
  • Navigating the Power Query Editor
  • Applying Basic Transformations (Replace Values, Remove Duplicates)
  • Advanced Transformations (Pivot/Unpivot, Group By)
  • Creating Custom Columns
  • Merging Queries
  • Appending Queries
  • Managing Query Steps

Microsoft Power BI

• Introduction to Power BI
  • Overview of Power BI and its components
  • Power BI Service vs. Power BI Desktop
  • Benefits of using Power BI for data analytics
  • Key features and capabilities of Power BI
  • Understanding the Power BI ecosystem
  • User interface and navigation
  • Basic concepts and terminology in Power BI
• Installation of Power BI Desktop
  • System requirements for Power BI Desktop
  • Step-by-step installation guide
  • Initial setup and configuration
  • Updating Power BI Desktop
  • Troubleshooting common installation issues
  • Licensing and subscription options
  • Importing sample datasets for practice
• Connecting Power BI with Other Sources
  • Supported data sources in Power BI
  • Connecting to databases, cloud services, and files
  • Data import methods and best practices
  • Managing data connections
  • Data refresh and scheduling
  • Handling data privacy and security
  • Using Power BI gateways
• Basic Visualization in Power BI
  • Types of visualizations available in Power BI
  • Creating basic charts and graphs
  • Customizing visualizations (colors, labels, titles)
  • Using slicers and filters
  • Creating and managing dashboards
  • Adding interactivity to visualizations
  • Publishing and sharing reports
• Advanced Visualization in Power BI
  • Using advanced chart types (Waterfall, Funnel, Gauge)
  • Creating and using bookmarks
  • Implementing drill-through functionality
  • Using custom visuals from the marketplace
  • Creating dynamic visualizations with DAX
  • Advanced interactivity techniques
  • Combining multiple visualizations in a single report
• Advanced DAX
  • Introduction to DAX: Calculated Columns, Measures, and Tables
  • Time Intelligence Functions
  • Filtering Functions
  • Advanced Calculations and Complex DAX Queries
  • Optimizing DAX Queries for Performance
  • Practical Applications of DAX in Data Models
• Introduction to Power Query
  • Overview of Power Query and its interface
  • Connecting to various data sources
  • Understanding the M language
  • Basic data import and transformation
  • Using the Query Editor for data preparation
  • Managing and combining queries
  • Best practices for efficient data queries

Tableau Tool

• Tableau Basics
  • Introduction to Tableau and its components
  • Tableau Desktop vs. Tableau Public
  • Key features and benefits of Tableau
  • Understanding the Tableau interface
  • Basic concepts and terminology in Tableau
  • Connecting to data sources
  • Creating your first visualization
• Design Features
  • Overview of Tableau design principles
  • Customizing visualizations (colors, labels, titles)
  • Using shapes and images in dashboards
  • Applying themes and templates
  • Formatting numbers, dates, and text
  • Enhancing aesthetics with backgrounds and borders
  • Best practices for dashboard design
• Time Series Analysis
  • Understanding time series data
  • Creating time series visualizations
  • Using date functions in Tableau
  • Time series forecasting
  • Analyzing trends and seasonality
  • Implementing moving averages and running totals
  • Comparing time periods
• Aggregation and Granularity
  • Understanding aggregation in Tableau
  • Adjusting granularity of data
  • Using calculated fields for aggregation
  • Aggregating data in visualizations
  • Customizing aggregation settings
  • Drilling down and rolling up data
  • Aggregation best practices
• Filters
  • Types of filters in Tableau
  • Applying filters to visualizations
  • Using quick filters and filter actions
  • Creating dynamic and cascading filters
  • Managing filter hierarchies
  • Filter optimization techniques
  • Best practices for using filters
• Maps and Scatter Plots
  • Creating and customizing maps
  • Using geographic data in Tableau
  • Implementing map layers and controls
  • Creating scatter plots and bubble charts
  • Analyzing data with scatter plots
  • Adding interactivity to maps and scatter plots
  • Combining maps with other visualizations
• Joining and Union
  • Understanding joins and unions in Tableau
  • Types of joins (inner, left, right, full)
  • Performing data joins in Tableau
  • Using unions to combine datasets
  • Managing data relationships
  • Handling data blending
  • Best practices for joins and unions

Generative AI (GenAI)

• Foundations of LLMs & Prompt Engineering
  • Fundamentals of LLMs
    • Understand how LLMs work, including tokenization and embeddings.
    • Explore how models like GPT interpret and generate text.
    • Project: Visualize and compare embeddings.
  • Prompt Engineering in Practice
    • Learn how to structure and format prompts effectively.
    • Understand chat roles: system, user, assistant.
    • Practice designing multi-turn prompts and chaining logic.
    • Project: Create a multi-turn chatbot.
  • Prompt Engineering with OpenAI API
    • Apply prompting techniques for summarizing, classifying, and transforming text.
    • Use OpenAI API to test and iterate prompts programmatically.
    • Project: Implement and compare zero-shot, one-shot, and few-shot prompts for text summarization, sentiment detection, and email drafting.
  • Building Systems using Generative AI
    • Apply prompt engineering to solve real-world text tasks.
    • Split complex tasks into a pipeline of subtasks using multi-stage prompts.
    • Evaluate LLM inputs and outputs for safety, accuracy, and relevance.
    • Project: Build a customer-support agent that can flag, retrieve, and answer user queries.
• LangChain & Agent Systems
  • Application Development using LangChain
    • Understand the core LangChain framework.
    • Learn how to use your own data with RAG (Retrieval-Augmented Generation).
    • Build chatbots powered by structured chaining logic.
    • Project: Create a document Q&A assistant using LangChain and your own PDFs.
  • Multi-Agent Systems with AutoGen
    • Learn to scale LLM performance through Agentic AI systems using AutoGen.
    • Compare agent patterns such as coordinator-worker, role-based agents, and tool-invoking agents.
    • Explore real-world task automation like document processing or customer service escalation.
    • Optional: Contrast AutoGen with CrewAI for understanding trade-offs.
    • Project: Build a 3-agent AutoGen system to automate customer support (intake, triage, escalation).
  • Agentic Design with LangGraph
    • Learn to design structured agent workflows with LangGraph.
    • Combine LangGraph’s visual DAG-based control with AutoGen’s conversational memory.
    • Project: Implement a LangGraph/AutoGen hybrid agent that dynamically routes tasks and retries failed steps.
  • Reasoning with LLMs
    • Learn how reasoning models work and when to use them.
    • Use chain-of-thought and self-reflection prompting techniques.
    • Apply reasoning to code and vision-based tasks.
    • Project: Use reasoning prompts to debug code and answer image-based questions step-by-step.
• Reasoning Models & Advanced Agents
  • Advanced Use Cases with AutoGen
    • Design collaborative multi-agent systems using AutoGen’s planner-executor framework.
    • Integrate diverse LLMs and tools, support memory/context sharing, and enable agents to reason, critique, and self-correct.
    • Inject domain-specific logic, external APIs, and feedback loops for robust automation.
    • Project: Build an AutoGen-based resume screening system that ranks candidates using dynamic criteria.
  • Build with Anthropic – Computer Use
    • Understand Anthropic family of models, best practices, and multi-modal use cases.
    • Implement low-latency and cost-saving techniques like prompt caching.
    • Learn multi-modal and tool-augmented use cases.
    • Project: Build an AI assistant that opens a file, summarizes content, and suggests edits.
  • Reasoning + Agent Project Sprint
    • Apply reasoning and agent collaboration in a high-impact use case.
    • Combine LangGraph for workflow structuring and AutoGen for autonomous reasoning agents.
    • Project: Build a “Virtual Analyst” that reasons over documents, answers queries, and summarizes trends.
• Memory, Context Protocols & AI Safety
  • Long-Term Memory with LangGraph + AutoGen
    • Build agents with evolving memory using AutoGen’s user memory modules and LangGraph’s persistent nodes.
    • Support personalization and context continuity across sessions.
    • Project: Extend AutoGen agents with long-term adaptive memory.
  • Model Context Protocol (MCP)
    • Learn how MCP standardizes tool and data access across apps.
    • Connect multiple AI agents and services using a shared protocol.
    • Deploy and manage MCP servers and clients.
    • Project: Deploy an MCP server and connect mini-apps that share user context.
  • Guardrails for AI Systems
    • Recognize common failure modes in LLM systems.
    • Use guardrails to validate and filter model inputs and outputs.
    • Integrate I/O controls into RAG and agent-based apps.
    • Project: Add guardrails to a Generative AI app to block unsafe, biased, or irrelevant responses.
  • AI Project Best Practices – Building and Deploying End-to-End Applications
    • Learn the lifecycle of AI product development: scoping, prototyping, testing, and deployment.
    • Explore trade-offs: prompting vs. fine-tuning, cost vs. accuracy, latency vs. interactivity.
    • Understand fine-tuning fundamentals and implications.
    • Learn best practices for versioning, observability, feedback collection, and human-in-the-loop integration.
    • Evaluate AI systems for quality, safety, and robustness.