Module 1: Introduction to Python.

• What is Python?
• Who developed Python and when?
• How to install Python?
  • Download from the official
  • Use IDLE or install IDEs like VS Code, PyCharm, Jupyter
• Why choose or learn Python?
• Name some of the Real-world applications of Python?
• General & Salient Features of Python?
• Colour coding schemes in Python?
• Flavours in Python?

 Module 2: Core Python Tokens & Syntax.

1. Naming Rules and Identifier
   • Private
   • Strong
   • Magical Method
   • Rules to create an
2. Literal Types and Their
3. Operators and Their Functional

o Arithmetic Operators.

• Relational or Comparison
• Assignment
• Shift
• Logical
• Membership
• Identity

4. Reserved Keywords and Their
5. Comments Practice and Quotation Comments:
   • Single Line
   • Multi-Line Quotations:
   • Single
   • Double
   • Triple

Module 3: String Operations and Handling Techniques.

1. Understanding Strings in Python?
2. Core String
   • Accessing Individual Characters (Indexing).
   • Extracting Substrings (Slicing).
   • Range-Based Substring Extraction (Ranging).
   • String Reversal Techniques (Reversing).
3. String Methods and Manipulation
   • String Concatenation or Merging
   • Repeating String
   • String Formatting Techniques
4. Built-in Functions for String

Module 4: Core Data Structures in Python.

1. Introduction to Python Data Types?
2. Working with Lists and Their
   • Compact List Creation using
   • Built-in Functions and methods for
   • Copying Lists: Deep vs
3. Working with Tuples and Built-in
4. Set Data Type and Its
5. Dictionaries and Mapping Structures in

Module 5: Conditional Statements.

1. What is a Conditional Statement?
2. Types of Conditional Statements?
   • Single Condition check / One-way
   • Binary Condition / Two-way
   • Multi-Way Branching / Conditional
   • Layered Condition / Hierarchical

Module 6: Iterative Statements.

1. What are Iterative statements and related terms?
2. Types Of Iterative
   • Count-Controlled Loop / Fixed
   • Condition-Controlled Loop / Entry-Control
3. Loop Practice
4. Pattern Printing

Module 7: Statements Controllers.

1. What are Statement Controllers and related terms?
2. Types of statement controllers?
   • Null Operation / Placeholder Statement / Empty block
   • Loop Terminator / Exit Loop / Forced
   • Skip Iteration / Loop Skipper / Next Cycle / Loop
3. Structured Iteration
4. Decision-Based Pattern

Module 8: Functions.

1. What are Functions?
2. Components of functions?
3. Difference between a Method and a Function?
4. What is a Parameter?
5. What are Arguments?
6. Types of Functions?
   1. User-Defined
      • Types of arguments used in
        • Default

• Positional
• Keyword
• Arbitrary

2. Built-in
3. Recursive
4. Lambda Functions (map, filter, reduce).
5. Math

Module 9: Object-Oriented Programming Structure.

1. What is OOPS?
2. Why OOPS?
3. How does Python support OOPS concepts?
4. Variables in OOPS?
   • Class
   • Global
   • Local
5. What are classes and objects? 6.Properties or Principles of OOPS?
   • Data
   • Data
   • Single
   • Multiple
   • Multilevel
   • Hierarchical
   •  

7. How does the Constructor work?
8. Use of init  constructor?
9. Use of the Self keyword?

Module 10: Error and Exception Handling.

1. What Is an Error?
2. Types of Error?
   • Syntax
   • Runtime
   • Logical

3. What is an Exception?
4. Difference between Error and Exception?
5. Types of common Exceptions?
   • Zero Division
   • Value
   • Type
   • Index
   • Key
   • FileNotFound
   • Import
6. Exception handler components?
   • Try
   • Except
   • Finally

Module 11: Modules and Packages.

1. What are libraries, modules, and packages?
2. How to use internal modules of Python?
3. Importing strategies or module access techniques in Python?
4. Types of commonly used Modules?
   • OS
   • SYS
   • Math
   • Time
   • Datetime
   • Calendar

Module 12: File Handling Management (Data Storage Unit).

1. What is a file?
2. File handling access modes?
   •  
   •  
   •  
   • Read +.
   • Write +.
   • Append +.
   • Text +.
   • Read
   • Write

3. File handling Functions?
   • Read a
   • Read
   • Write
   •  

3. How to store data in a CSV file format?

H_]   Module 13: Database Management.

1. Introduction to
   • What is data, information, and insight?
   • What is a Database?
   • Need for a
   • What is Database Manager?
   • What is a Database Management System?
   • Types of Databases:
     • Relational (RDBMS).
     • Non-relational (NoSQL).
   • Introduction to
   • MySQL Overview and
2. Installation and setup of
   • Installing MySQL
   • MySQL Workbench /
   • Connecting to MySQL via Command
3. SQL
   • SQL Syntax &
   • SQL Statement Types:

• DDL (Data Definition Language).
• DML (Data Manipulation Language).
• DCL (Data Control Language).
• TCL (Transaction Control Language).

4. DDL (Data Definition Language).

• Create
• Create
• Create
• Alter
•  
• Truncate

5. DML (Data Manipulation Language).

• Inserting Data (INSERT).
• Updating Data (UPDATE).
• Deleting Data (DELETE).
• Selecting Data (SELECT).

6. DCL (Data Control Language).

• Create
• Drop

7. TCL (Transaction Control Language).

• Begin or start a
• Release
• Set

8. Schema Design
   • Primary
   • Foreign
   • Unique, Not Null,
   • Auto
9.  
10. Aggregate
    •  
11. Datetime
    • Date
    •  

3. String
   •  
4. Math / Numeric
   •  
5. Window
6. Window Partitioning and
   • Partition
   • Order inside
   • Row and range-based frame
7. Ranking
   • Row
   • Dense
8. Value
   • First
   • Last
9. Frame
   • Rows
   • Range
10. Aggregate window
    • Sum
    • Avg
    • Count
    • Min

• Max

11. Common Table Expression (CTE).
    1. Introduction to
    2. Types of
    3. Recursive and Non-Recursive
    4. Multiple CTEs in one
    5. Using CTE with
    1. Introduction to
    2. Trigger
    3. Types of
    4. Managing
12. Stored
    1. Introduction to
    2. Creating
    3. Procedure
    4. Managing
    1. Working with
    2. Cursor
13.  

• Inner
• Left
• Right
• Full Join (Via Union).
• Joining Multiple

16. Subqueries and Nested

• Subquery in SELECT, FROM,
• Correlated

17. 1. Arithmetic
    2. Comparison
    3. Logical
    4. Bitwise
    5. Set
18. Views and

• Creating and Using
• Indexing for

19. Backup and

• Exporting a Database (mysqldump).
• Importing a

20. MySQL and Python Integration (Optional Advanced).

• Using MySQL-connector-
• Connecting Python with

21. 1. Introduction to
    2. Data
    3. Normal forms:
       •  

📘 Module 14: PANDAS.

Chapter 1: Introduction to Pandas.

1. What are Pandas and their importance in data analysis?
2. Comparison: Pandas vs Excel vs
3. How to Install
4. How to import
5. Uses of pandas in real-world
6. What is a data structure and an array? Chapter 2: Data structures of pandas.
7. Data
8.  

Chapter 3: Series creation of pandas.

1. Series creation using a
2. Series creation using a
3. Series creation using a NumPy
4. Series creation using a
5. Accessing elements using:
   • Integer
6. Methods:
   • head ().
   • tail ().
   • unique ().
   • Nunique ().
   • value_counts ().
7. Series
   • Type and
   •  
8. Accessing elements of a
   • Slicing
   •  

Chapter 4: Data frame creation in pandas.

1. Data frame creation using a
2. Data frame creation using a
3. Data frame creation using a NumPy
4. Data frame creation using
5. Data frame creation using a data

Chapter 5: Indexing, Slicing, and Subsetting.

1. Selecting
2. Selecting rows using:
   • loc [] (label-based).
   • Iloc [] (position-based).
3. Slicing rows and

Chapter 6: Modifying and Updating Data

1. Adding new
2. Updating values in cells/columns.
3. Renaming columns and
4. Dropping columns and
5. Reindexing
6. Changing data

Chapter 7: Handling Missing Values and Sorting.

1. Detecting missing data:
   • Isnull ().
   • Notnull ().
2. Handling missing data:
   • Fillna () (forward/backward fill, mean/median).
   • Dropna () (rows or columns).
3. Replacing values using replace ().
4. Sorting:
   • sort_values (ascending/descending, multiple columns).
   • sort_index ().

Chapter 8: Aggregation, Grouping, and Statistical Functions.

1. Using groupby () for:
   • Single-column.
   • multi-column
2. Aggregation methods:
   • Mean ().
   • Sum ().
   • Count ().
   • Min ().
   • Max ().

Chapter 9: Data operations in pandas.

1. Map and apply

Chapter 10: Date & Time Operations

1. Creating Date Time
2. Extracting parts of datetime:
3. Year.
4. month.
5. day.
6. Hour.
7. Filtering by date
8. Time-based

Chapter 11: File Input and Output (I/O).

1. Reading files:
   • read_csv ().
   • read_excel ().
   • read_json ().
   • read_html ().
2. Writing files:
   • to_csv ().
   • to_excel ().
   • to_json ().

Chapter 12: Data Visualization with Pandas.

1. Basic visualizations:
   •  
2. Customizing visualizations:
   •  

📘 Module 14: NUMPY.

Chapter 1: Introduction to NumPy

1. What is NumPy, and why use it?
2. Applications and benefits of
3. How to Install
4. How to Import
5. Differences between Python lists and NumPy

Chapter 2: NumPy Arrays Basics.

1. What is an Array?
2. Types of Arrays?
   • One-dimensional
   • Two-dimensional
   • Three-dimensional array
3. Creating arrays:
   • list/tuple.
   • Multidimensional
4. Checking or NumPy array
   • Data
   • Number of
5. Perform arithmetic operations in an

Chapter 3: Array Creation Functions.

1. zeros ().
2. ones ().
3. Arrange (start, stop, step).
4. linspace (start, stop, Num).
5. eye (n) for identity matrix.
6. Random arrays:
   • random. Rand ().
   • random. Randn ().
   • random. Randint (low, high, size).

7. Algebraic
   • Inverse ().
   • Transpose ().
   • Trace ().

Chapter 5: Array Attributes and Properties

1. Changing shape or Shape Manipulation:
   • Ravel ().
   • Reshape ().
   • Resize ().
2. Copy vs View:
   • Shallow
   • deep
3. Concatenation:
   • concatenate ().
   • vstack ().
   • hstack ().
4. Splitting:
   • split ().
   • Hsplit ().
   • vsplit ().

Chapter 6: Mathematical and Statistical Operations.

1. Element-wise operations:

• +, -, *, /, **, sqrt (), log (), exp ().

2. Aggregate functions:
   • Sum (), min (), max (), mean (), median (), std (), var ().

Chapter 7: Linear Algebra with NumPy.

1. Dot product: dot (), @ operator.
2. Matrix multiplication: Matmul ().
3. Transpose:
4. Inverse: linalg.inv ().

Chapter 9: Random Module in NumPy.

1. random. seed () for reproducibility.
2. Uniform distribution: random. Rand ().
3. Normal distribution: random. Randn. ().
4. Random integers: random. Randint ().

Chapter 10: Universal Functions.

1. Arithmetic
   • add, subtract, multiply, and

2.      Trigonometric functions.

o sin, cos, tan.

3.      Rounding functions.

• floor, ceil, round,

📘 Module 15: POWER BI

Chapter 1. Power BI Introduction.

1. What is Business Intelligence (BI)?
2. What is Power BI and why is it popular?
3. History and evolution of Power
4. Benefits of Power BI in data
5. Difference between Power BI vs Excel vs
6. Installation and

Chapter 2. Power BI Architecture & Workflow.

1. Power BI workflow: ETL → Modelling → Visualization →
2. Overview of components:
   • Power BI
   • Power BI Service (Cloud).
   • Power BI Mobile
   • Power BI Report
   • Power BI
   • Power BI
   • Power BI

Chapter 3. Connecting to Data Sources.

1. Files: Excel, CSV, Text, XML,
2. Databases: SQL Server, MySQL, PostgreSQL, Oracle,
3. Online: Web, SharePoint, OneDrive, OData
4. Cloud: Azure Blob, Azure SQL, Azure Data
5. APIs: Web API,
6. Scripting: R and Python
7. Import vs Direct Query vs Live
8. Combine and merge multiple
9. On-premises data access via Data

Chapter 4. Power Query Editor (Data Transformation).

1. Interface overview & applied
2. M language
3. Data cleaning:
   • Rename, Remove, Replace
   • Filter, Sort, Group By,
   • Split, Merge, Pivot/Unpivot.
   • Conditional
4. Append vs Merge
5. Handle nulls, errors, and
6. Parameterized

Chapter 5. Data Modelling.

1. Star and Snowflake
2. Fact vs Dimension
3. Relationships:
   • One-to-many, Many-to-
   • Cardinality, Active/Inactive.
4. Data types and

5. Role-playing
6. Hide/unhide
7. Best practices in data

Chapter 6. DAX (Data Analysis Expressions).

1. Syntax &
2. Measures vs calculated
3. Operators and data

Core Functions:

1. Aggregation:
2. Logical:
3. Text:
4. Date/Time:
   • Date

6. Filter:.
7. Lookup:
   • Lookup
8. Time Intelligence:
   • Same period last
   • Parallel
9. Variables:
10. Error handling:
    • If
    • Is

Chapter 7. Visualizations in Power BI.

1. Basic Charts:
2. Advanced:
   • Table,
   • KPI, Gauge, Waterfall, Funnel, Tree
   • Decomposition
   • Maps: Basic, Filled,
3. Custom visuals from
4. Tooltips,
5. Bookmarking and storytelling

Chapter 8. Filters and Slicers.

1. Types:
   • Visual-level.
   • Page-level.
   • Report-level

2. Filter modes: Basic & Advanced
3. Slicers:
   • Single-select, Multi-select,
   • Sync across
4. Drill through
5. Drill Down/Up in visual

Chapter 9. Report Design and Formatting.

1. Page setup: size, background,
2. Use of images, shapes, and
3. Alignment, layering,
4. Buttons for navigation, bookmarks.
5. Selection and bookmarks
6. Tooltips and drill-through
7. Dynamic Titles using

Chapter 10. Power BI and Excel Integration.

1. Exporting data to
2. Analysing datasets using
3. Using the “Analyse in Excel”

Chapter 11. Performance Optimization.

1. DAX performance
2. Visual load
3. Performance Analyzer and DAX
4. Best practices for data