Identifiers and Variables

• Naming Conventions
• Assigning Values
• Dynamic Typing

Keywords

• List of Python Keywords
• Reserved Words

Operators

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators
• Membership Operators

Data Types

• Primitive Data Types: Integer, Float, String, Boolean
• Non-Primitive Data Types: List, Tuple, Dictionary, Sets

Comprehensions in Python

• List Comprehensions
• Dictionary Comprehensions
• Set Comprehensions
• Nested Comprehensions

Control Flow

• Conditional Statements: If, If-else, If-elif-else, Nested if
• Loops: While Loop, For Loop, Break, Continue, Pass

Hands-on Exercise:

• Write programs demonstrating variable assignments,
• operators, and control flow.
• Use comprehensions to create lists, dictionaries, and sets.

User Defined Functions

• Defining Functions
• Function Arguments
• Return Statement

Built-in Functions

• Common Built-in Functions
• Using Built-in Functions

Lambda Functions

• Anonymous Functions
• Syntax and Usage

Map, Filter, Reduce

• Map: Applying a function to all items in an input list
• Filter: Constructing a list from elements of the input list
• that return true for a function
• Reduce: Applying a rolling computation to sequential pairs
• of values in a list

Hands-on Exercise:

• Write functions to perform simple tasks.
• Use lambda functions with map, filter, and reduce.

File Operations

• Overview of File Handling in Python
• Importance of File Handling in Programming
• File Types: CSV, Excel, Text, PDF, JSON

Opening Files

• Using the open() Function
• Different Modes for Opening Files (r, w, a, x)

Creating Files

• Creating a New File Using 'w', 'a', or 'x' Mode

Reading Files

• Reading the Entire Content Using read()
• Reading Line by Line Using readline()
• Reading All Lines into a List Using readlines()

Writing to Files

• Writing a String to a File Using write()
• Writing Multiple Lines Using writelines()

Deleting Files

• Using the os Module to Delete Files

Hands-on Exercise

• Create, read, write, and delete files using Python.
• Perform file operations with CSV and JSON files.

Python re Module

• Compiling Regular Expressions

Methods with Regex Usage

• match()
• search()
• findall()
• sub()
• split()

Hands-on Exercise:

• Use regular expressions to search, match, and manipulate strings.

Types of Errors

• Syntax Errors
• Runtime Errors
• Logical Errors

Exception Handling

• try … except Block
• try … except … finally Block
• try … except … else Block
• Handling Multiple Exceptions
• Raising Exceptions

Hands-on Exercise:

• Write programs to demonstrate exception handling.
• Create custom exceptions and handle them appropriately

Classes and Objects

• Defining Classes
• Creating Objects
• Class Attributes and Methods

OOP Principles

• Polymorphism
• Encapsulation
• Inheritance

Hands-on Exercise:

• Create classes and objects.
• Implement OOP principles in Python programs.

NumPy Basics

• Difference Between NumPy and List
• Introduction to NumPy
• NumPy Array

Array Operations

• numpy.random Module
• Array Operations
• Vector Operations
• Statistical Functions

Array Manipulation

• Array Indexing
• Array Manipulation
• Array Broadcasting

Hands-on Exercise:

• Practice with NumPy arrays and perform mathematical operations.
• Manipulate and index arrays

Introduction to Pandas Library

• Series and DataFrame
• Data Structures in Pandas

Working with Series and DataFrames

• Creating Series and DataFrames
• Basic Operations on Series and DataFrames

Indexing and Selecting Data

• Selecting Rows and Columns
• Filtering Data

Data Cleaning and Preprocessing

• Dealing with Duplicate Data
• Handling Outliers
• Feature Scaling and Normalization
• Encoding Categorical Variables

Pandas Methods

• Creating DataFrames from various sources
• Viewing Data
• Selecting Data
• Filtering Data
• Adding/Modifying Columns
• Removing Data
• Handling Missing Data
• Pivot TablesDetecting and Dropping Duplicates
• Aggregation and Grouping
• String Methods
• Merging and Joining
• Date and Time Handling
• Pivot Tables
• Exporting Data

Hands-on Exercise:

• Create and manipulate DataFrames.
• Clean and preprocess data using Pandas methods

Introduction to Data Visualization

• Importance of Data Visualization
• Types of Data Visualization

Matplotlib for Basic Plotting

• Line Plot
• Bar Plot
• Histogram
• Scatter Plot
• Pie Chart
• Box and Whiskers Plot

Seaborn for Statistical Data Visualization

• Line Plot
• Barplot
• Boxplot
• Heatmap
• Pairplot
• Countplot
• Regplot
• Scatterplot
• Hueplot
• Violin plot
• Swarmplot
• Stripplot

Customizing Plots and Charts

• Choosing Axis
• Adding Grids
• Customizing Axis Values
• Adding Titles and Labels
• Customizing Colors and Styles
• Adding Legends

Hands-on Exercise:

• Create various types of plots using Matplotlib and Seaborn.
• Customize plots for better visualization.

Introduction to EDA

• Introduction to EDA
• Tools and Libraries for EDA

Loading Data

• Data Cleaning and Preparation
• Identifying and Handling Missing Data
• Identifying and Handling Duplicates
• Identifying and Handling Outliers

Feature Engineering

• One Hot Encoding
• Label Encoding
• Range Categorization

Univariate Analysis

• Summary Statistics
• Visualizations for Univariate Analysis
• Distribution Analysis

Bivariate Analysis

• Summary Statistics for Bivariate Analysis
• Visualizations for Bivariate Analysis
• Categorical vs. Numerical Analysis

Multivariate Analysis

• Summary Statistics for Multivariate Analysis
• Visualizations for Multivariate Analysis

Exploratory Data Analysis (EDA) Practice

• Case Study
• Reporting and Presenting EDA Findings

Hands-on

• Conducting a complete EDA on a given dataset
• Creating and presenting an EDA report

Mentored EDA Projects Hands-On

• Analyzing Diwali Sales Trends
• IPL Match Performance Analysis
• Sales Insights from Euromart Data
• Car Manufacturing and Pricing Analysis
• Titanic Survival Data Analysis

Hands-on Exercise: Conduct a complete EDA on a given dataset.

Project Definition

• Selecting a Problem Statement
• Defining Objectives and Goals

Data Collection and Preparation

• Collecting Data from Various Sources
• Cleaning and Preprocessing Data

Exploratory Data Analysis (EDA)

• Performing EDA to Understand Data
• Identifying Patterns and Insights

Data Visualization

• Creating Visualizations to Present Findings

Presentation

• Preparing a Comprehensive Report
• Presenting Findings and Recommendations

Hands-on Exercise:

• Execute a full data analysis project from problem definition to presentation.

SQL Statements & Operations

• Overview of SQL Statements
• Understanding SQL Operations
• Create, Read, Update, Delete (CRUD) Operations

Introduction to CRUD Operations

• Importance of CRUD in Database Management
• SQL Syntax and Queries
• Writing Effective SQL Queries
• Best Practices for SQL Syntax

Python-SQL Connector Package Installation

• Introduction to MySQL Connector for Python
• Steps to Install MySQL Connector
• Installing MySQL Connector
• Verifying Installation

Setting Up Database Connection

• Establishing Connection to MySQL Database
• Handling Connection Errors
• Closing the Database Connection

Python with CRUD Operations

• Integrating Python with MySQL for CRUD Operations
• Writing Python Code to Perform CRUD Operations

Creating Records

• Inserting Data into MySQL Database using Python
• Error Handling in Data Insertion

Reading Records

• Querying Data from MySQL Database using Python
• Fetching Data Efficiently

Updating Records

• Modifying Existing Data in MySQL Database using Python

• Ensuring Data Integrity during Updates

• Deleting Records

• Removing Data from MySQL Database using Python

• Precautions to Avoid Unintentional Data Loss

Hands-on Exercise:

• Perform CRUD operations with MySQL using Python.
• Establish and manage database connections using Python.

Modifying Data

• inserting Data: INSERT INTO
• updating Data: UPDATE SET
• deleting Data: DELETE FROM
• Working with NULL Values and IS NULL
• Real-life Example: Updating Inventory Levels in
• a Warehouse Management System

Data Normalization and Database Design

• understanding Data Normalization
• normal Forms (1NF, 2NF, 3NF, BCNF)
• designing Efficient Database Schemas
• case Study: Normalizing a Customer Relationship
• Management (CRM) Database

Advanced-Data Manipulation with Window Functions

• introduction to Window Functions
• ROW_NUMBER(), RANK(), DENSE_RANK()
• aggregate Window Functions: SUM(), AVG(), MIN(), MAX()
• practical Use Cases of Window Functions
• Real-life Example: Calculating Running Totals and Moving Averages in Financial Data

Mastering Subqueries and Correlated Subqueries

• Deep dive into subqueries and correlated subqueries.
• Optimizing subqueries for performance.
• performance Considerations and Best Practices
• Real-life example: Using complex subqueries for dynamic reporting.

Indexes and Performance Optimization

• Understanding Indexes and Their Types
• Creating and Managing Indexes
• Query Tuning and Optimization Techniques
• analyzing Query Performance with EXPLAIN
• case Study: Optimizing Queries for a High-traffic Ecommerce Platform

Stored Procedures, Functions, and Triggers

• Writing stored Procedures and User-Defined Functions
• benefits and Use Cases of Stored Procedures
• Implementing triggers for Automated Tasks
• Real-life Example: Automating Inventory Updates with
• Triggers

Working with Complex Data Types

• Handling JSON and XML Data
• Extracting and Manipulating JSON Data
• Working with Geospatial Data in MySQL
• Real-life Example: Storing and Querying User
• Preferences in JSON Format

Handling Time Series and Temporal Data

• Working with Date and Time Functions
• Analyzing Time Series Data
• Temporal Data Types and Functions
• Case Study: Time Series Analysis for Stock Market Data

Advanced Analytical Techniques

• Recursive queries and common table expressions (CTEs).
• Pivoting and unpivoting data.
• Advanced pattern matching with regular expressions.
• Real-life example: Using recursive queries for hierarchical data analysis.

Final Project: Solving a Complex Data Problem

• Project Overview and Requirements
• Data Exploration and Preprocessing
• Advanced-Data Analysis and Reporting
• Presenting Your Findings and Insights
• Project Application: Building a Comprehensive Sales Dashboard

• Installation Guidelines and Procedures
• Software Overview
• Power BI Account Registration
• Introduction to Tools and Terminology
• Updating Data in Power BI Service
• Defining a Dashboard
• Defining a Report
• Understanding the Interface
• Navigation Pane and Ribbon
• Working with Visualizations
• Saving and Publishing Reports

• Connecting to Data Sources
• Uploading Local CSV Files
• Connecting to Excel Data
• Utilizing the Query Editor
• Comparing Import Data vs. Direct Query
• Data Cleaning and Transformation Techniques
• Merging and Appending Data
• Introduction to ETL Concepts
• Extracting Data from Various Sources
• Transforming Data for Analysis
• Loading Data into Power BI

Hands-on: Datasets will be provided for practice in loading, cleaning, transforming data, and applying ETL concepts.

• Data Modeling Techniques
• Managing Data Relationships
• Understanding Cardinality and Cross Filtering
• Default Summarization and Sorting Options
• Creating Calculated Columns
• Developing Quick Measures and Measures
• Understanding Star Schema and Snowflake Schema
• Using Date Tables and Hierarchies

Hands-on:

• Participants will work with datasets to create
• calculated columns and manage relationships

• Introduction to DAX
• What is DAX?
• The role of DAX in Power BI
• Importance of DAX
• Why DAX is essential for data modeling and analysis
• Scenarios where DAX is used in Power BI
• DAX Syntax
• Basic syntax rules
• How to write DAX formulas
• Data Types in DAX
• Understanding different data types
• Implicit vs. explicit data type conversion
• Functions in DAX
• Overview of DAX functions
• Commonly used function
• Aggregation Functions: SUM, AVERAGE, MIN, MAX, COUNT
• Date and Time Functions: TODAY, NOW,
• DATEDIFF, YEAR, MONTH, DAY
• Logical Functions: IF, AND, OR, SWITCH
• Text Functions: CONCATENATE, LEFT, RIGHT,MID, SUBSTITUTE
• Mathematical Functions: ABS, CEILING, FLOOR, ROUND
• Statistical Functions: MEDIAN, PERCENTILE, STDEV
• Information Functions: ISBLANK, ISNUMBER, ISERROR
• Creating Measures with DAX
• Difference between calculated columns and measures
• How to create and use measures DAX Operators
• Arithmetic Operators: +, -, *, /, ^
• Comparison Operators: =, <>, <, <=, >, >=
• Text Concatenation Operator: &
• Logical Operators: &&, ||, IN
• Tables and Filtering in DAX
• Working with tables in DAX
• Filter functions: CALCULATE, FILTER, ALL, ALLEXCEPT, REMOVEFILTERS
• Context transition and its implications
• Time Intelligence Functions
• Overview of time intelligence in DAX
• Common time intelligence functions: TOTALYTD,
• SAMEPERIODLASTYEAR, PARALLELPERIOD, DATESBETWEEN
• Creating and using date tables
• Working with Relationships in DAX
• Understanding relationships and their impact on DAX formulas
• Using RELATED and RELATEDTABLE functions
• Advanced DAX Concepts
• Calculated Tables
• Row context vs. filter context
• Context transition
• Iterators: SUMX, AVERAGEX, MAXX, MINX

Hands-on:

• Participants will practice using various DAX functions

• Creating Visualizations
• Color Formatting Techniques
• Setting Sort Order
• Developing Scatter and Bubble Charts
• Using Tooltips
• Implementing Slicers
• Cross Filtering and Highlighting
• Applying Report-Level Filters
• Drill Down and Drill Up Functionality
• Creating Hierarchies
• Conditional Formatting for Tables and Matrices
• Utilizing KPIs
• Using Cards and Gauges
• Map Visualizations
• Custom Visuals
• Grouping and Binning Data
• Using Selection Pane, Bookmarks & Buttons
• Managing Z-Order
• Best Practices for Effective Visualizations
• Storytelling with Data

Hands-on:

• Participants will practice creating visualizations, applying color formatting, using slicers, and other visualization techniques.

• Modifying and Printing Reports
• Renaming and Deleting Report Pages
• Adding Filters to Pages or Reports
• Setting Visualization Interactions
• Printing Report Pages
• Exporting Reports to PowerPoint
• Creating Dashboards
• Managing Dashboards
• Pinning Report Tiles to Dashboards
• Pinning Live Report Pages to Dashboards
• Pinning Report Tiles to Dashboards
• Pinning Live Report Pages to Dashboards
• Pinning Tiles from Other Dashboards
• Pinning Excel Elements to Dashboards
• Managing Pinned Elements in Excel
• Adding Tiles to Dashboards
• Building Dashboards with Quick Insights
• Setting a Featured Dashboard
• Asking Questions about Your Data with Power BI Q&A
• Tweaking Datasets for Q&A
• Enabling Cortana Integration for Power BI
• Best Practices for Dashboard Design

Hands-on:

• Participants will practice adding filters to reports, setting interactions, and creating dashboards.

• Overview of Sharing Options
• Publishing Reports from Power BI Desktop
• Publishing Reports to the Web
• Sharing Dashboards Using Power BI Service
• Understanding Content Packs
• Saving Reports as PDF
• Implementing Row-Level Security
• Exporting Data from Visualizations
• Setting Permissions and Access Controls
• Collaborative Workspaces
• Best Practices for Report Distribution

Hands-on:

• Participants will practice sharing dashboards using Power BI Service, saving reports as PDFs, and exporting data from visualizations.

• Function
• Nested IF
• AND Function
• SUMIF
• COUNTIF
• IFERROR

Hands-on:

• Implement conditional functions in Excel formulas.
• Use nested IF statements for complex conditions.

• Creating Simple Charts
• Modifying Charts
• Changing Chart Types
• Chart Formatting
• Formatting Chart Items
• Formatting Plot Area
• Formatting Data Markers

Hands-on:

• Create various types of charts and customize them.
• Format charts to enhance visual clarity

• Converting Dates to Different Formats
• Obtaining Today’s Date
• Finding Workdays After Specified Days
• Extracting Year, Month, Day from Date
• Extracting Day of the Week from Date
• Calculating Differences Between Dates

Hands-on:

• Convert date formats using Excel functions.
• Perform date calculations based on given scenarios

• Highlight Cells Rules
• Top / Bottom Rules
• Data Bars
• Color Scales
• Icon Sets
• New Rule
• Clearing and Managing Rules

Hands-on:

• Apply various conditional formatting rules to Excel data.
• Manage and clear conditional formatting rules as needed.

• Sort by Text, Numbers, Dates
• Sort by Cell Color, Font Color, Cell Icon
• Sort by Custom List
• Multi-column or Multi-row Sorting

Hands-on:

• Sort Excel data using different sorting methods.
• Perform multi-column sorting operations.

• Filter by Selected Values, Text, Date, Numbers
• Filter by Cell Color
• Clear Filters
• Advanced Filtering Techniques
• Using Slicers for Filtering

Hands-on:

• Apply filters and Advance filterings to Excel data based on various criteria.
• Use slicers to interactively filter data.

• VLOOKUP and HLOOKUP Functions
• INDEX and MATCH Functions
• Range Lookup Techniques

Hands-on:

• Perform lookup operations using VLOOKUP, HLOOKUP, INDEX, and MATCH functions.
• Apply range lookup techniques in Excel formulas.

• Creating PivotTables
• Recommended PivotTables
• PivotTable Fields and Areas
• PivotTable Nesting and Filtering
• Summarizing Values by Calculations
• PivotTable Tools and Customizations
• Creating PivotCharts
• Using Pictures in Charts
• Advanced Chart Types: Band, Thermometer, Gantt,
• Waterfall
• Sparklines
• Working with Multiple Sheets
• Creating a formula across multiple worksheets
• Summarizing data in multiple worksheets
• Merging two tables using Append Queries

Hands-on:

• Create PivotTables and PivotCharts to summarize and visualize data.
• Customize PivotTables and PivotCharts for multiple worksheets

• Introduction to Macros
• Recording and Running Macros
• Introduction to VBA (Visual Basic for Applications)
• Basic VBA Syntax

Hands-on:

• Record and run macros to automate repetitive tasks.
• Write basic VBA scripts for Excel operations.