Module 1: Introduction to Python.
> What is Python?
> Who developed Python and when?
> How to install Python?
o Download from the official website.
o Use IDLE or install IDEs like VS Code, PyCharm, Jupyter notebook.
> 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 Conventions.
o Private Identifier.
o Strong Identifier.
o Magical Method Identifier.
o Rules to create an identifier.
2. Literal Types and Their Usage.
3. Operators and Their Functional Categories.
o Arithmetic Operators.
o Relational or Comparison Operators.
o Assignment Operators.
o Shift Operators.
o Logical Operators.
o Membership Operators.
o Identity Operators.

4. Reserved Keywords and Their Roles.
5. Comments Practice and Quotation Techniques. Comments:
o Single Line Comments.
o Multi-Line Comments. Quotations:
o Single Quotes.
o Double Quotes.
o Triple Quotes.

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

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

Module 5: Conditional Statements.
1. What is a Conditional Statement?
2. Types of Conditional Statements?
o Single Condition check / One-way decision.
o Binary Condition / Two-way decision.
o Multi-Way Branching / Conditional Ladder.
o Layered Condition / Hierarchical Check.

Module 6: Iterative Statements.
1. What are Iterative statements and related terms?
2. Types Of Iterative Statements.
o Count-Controlled Loop / Fixed Loop.
o Condition-Controlled Loop / Entry-Control loop.
3. Loop Practice Problems.
4. Pattern Printing Programs.

Module 7: Statements Controllers.
1. What are Statement Controllers and related terms?
2. Types of statement controllers?
o Null Operation / Placeholder Statement / Empty block holder.
o Loop Terminator / Exit Loop / Forced Exit.
o Skip Iteration / Loop Skipper / Next Cycle / Loop Trigger.
3. Structured Iteration Problems.
4. Decision-Based Pattern Programs.

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 Functions.
* Types of arguments used in UDF.
o Default arguments.

o Positional arguments.
o Keyword arguments.
o Arbitrary arguments.
2. Built-in Functions.
3. Recursive Functions.
4. Lambda Functions (map, filter, reduce).
5. Math Functions.

Module 9: Object-Oriented Programming Structure.
1. What is OOPS?
2. Why OOPS?
3. How does Python support OOPS concepts?
4. Variables in OOPS?
o Class Variable.
o Global Variable.
o Local Variable.
5. What are classes and objects? 6.Properties or Principles of OOPS?
o Data Abstraction.
o Data Encapsulation.
o Polymorphism.
o Inheritance.
* Single Inheritance.
* Multiple Inheritance.
* Multilevel Inheritance.
* Hierarchical Inheritance.
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?
o Syntax Error.
o Runtime Error.
o Logical Error.

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

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?
o OS Module.
o SYS Module.
o Math Module.
o Time Module.
o Datetime Module.
o Calendar Module.

Module 12: File Handling Management (Data Storage Unit).
1. What is a file?
2. File handling access modes?
o Read.
o Write.
o Append.
o Read +.
o Write +.
o Append +.
o Text +.
o Read binary.
o Write binary.

3. File handling Functions?
o Read.
o Read a line.
o Read lines.
o Write.
o Write lines.
o Tell.
o Seek.
3. How to store data in a CSV file format?

Module 13: Database Management.
1. Introduction to Databases.
o What is data, information, and insight?
o What is a Database?
o Need for a Database.
o What is Database Manager?
o What is a Database Management System?
o Types of Databases:
* Relational (RDBMS).
* Non-relational (NoSQL).
o Introduction to RDBMS.
o MySQL Overview and Features.
2. Installation and setup of MySQL.
o Installing MySQL Server.
o MySQL Workbench / phpMyAdmin.
o Connecting to MySQL via Command Line.
3. SQL Fundamentals.
o SQL Syntax & Commands.
o 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 Database.
* Create Table.
* Create Index.
* Alter Table.
* Drop.
* Truncate Table.

5. DML (Data Manipulation Language).
* Inserting Data (INSERT).
* Updating Data (UPDATE).
* Deleting Data (DELETE).
* Selecting Data (SELECT).
6. DCL (Data Control Language).
* Grant.
* Revoke.
* Create User.
* Drop User.
7. TCL (Transaction Control Language).
* Begin or start a transaction.
* Commit.
* Rollback.
* Savepoint.
* Release Savepoint.
* Set autocommit.
8. Schema Design Concepts.
1. Keys.
o Primary Key.
o Foreign Key.
2. Constraints.
o Unique, Not Null, Default.
o Auto Increment.
9. Functions.
1. Aggregate Functions.
* Count.
* Sum.
* Avg.
* Min.
* Max.
2. Datetime Functions.
* Day.
* Month.
* Year.
* Date add.
* Datediff.
* Curdate.

* Now.
3. String Functions.
* Concat.
* Length.
* Substring.
* Upper.
* Lower.
* Trim.
* Replace.
4. Math / Numeric Functions.
* Round.
* Ceil.
* Floor.
* Sqrt.
5. Window Functions.
1. Window Partitioning and Ordering.
* Partition by.
* Order inside over.
* Row and range-based frame specifications.
2. Ranking Functions.
* Row number.
* Rank.
* Dense rank.
* Ntile.
3. Value functions.
* Lead.
* Lag.
* First value.
* Last value.
* Nth_value.
4. Frame clauses.
* Rows between.
* Range between.
5. Aggregate window functions.
* Sum over.
* Avg over.
* Count over.
* Min over.

* Max over.
11. Common Table Expression (CTE).
1. Introduction to CTE.
2. Types of CTE.
3. Recursive and Non-Recursive CTE.
4. Multiple CTEs in one query.
5. Using CTE with joins.
12. Triggers.
1. Introduction to triggers.
2. Trigger basics.
3. Types of Triggers.
4. Managing Triggers.
13. Stored Procedure.
1. Introduction to SP.
2. Creating SP.
3. Procedure Logic.
4. Managing SP.
14. Cursor.
1. Working with cursors.
2. Cursor handling.
15. Joins.
* Inner Join.
* Left Join.
* Right Join.
* Full Join (Via Union).
* Joining Multiple Tables.
16. Subqueries and Nested Queries.
o Subquery in SELECT, FROM, WHERE.
o Correlated Subqueries.

17. Operators.
1. Arithmetic Operators.
2. Comparison Operators.
3. Logical Operators.
4. Bitwise Operators.
5. Set Operators.
18. Views and Indexing.
o Creating and Using Views.
o Indexing for Performance.
19. Backup and Restore.
o Exporting a Database (mysqldump).
o Importing a Database.
20. MySQL and Python Integration (Optional Advanced).
o Using MySQL-connector-python.
o Connecting Python with MySQL.
21. Normalization.
1. Introduction to Normalization.
2. Data Anomalies.
3. Normal forms:
* 1NF.
* 2NF.
* 3NF.
* BCNF.
* 4NF.
* 5NF.
* 6NF.

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Module 14: PANDAS.
Chapter 1: Introduction to Pandas.
1. What are Pandas and their importance in data analysis?
2. Comparison: Pandas vs Excel vs SQL.
3. How to Install Pandas.
4. How to import pandas.
5. Uses of pandas in real-world technologies.
6. What is a data structure and an array? Chapter 2: Data structures of pandas.
1. Series.
2. Data frame.
3. Panel.
4. Panel4d.
Chapter 3: Series creation of pandas.
1. Series creation using a list.
2. Series creation using a dictionary.
3. Series creation using a NumPy array.
4. Series creation using a scaler.
5. Accessing elements using:
* Labels.
* Integer positions.
6. Methods:
* head ().
* tail ().
* unique ().
* Nunique ().
* value_counts ().
7. Series attributes.
* Values.
* index.
* Type and dtype.
* shape.
* name.
8. Accessing elements of a series.
* Indexing.
* Slicing
* Ranging.

Chapter 4: Data frame creation in pandas.
1. Data frame creation using a list.
2. Data frame creation using a dictionary.
3. Data frame creation using a NumPy array.
4. Data frame creation using Series.
5. Data frame creation using a data frame.
Chapter 5: Indexing, Slicing, and Subsetting.
1. Selecting columns.
2. Selecting rows using:
* loc [] (label-based).
* Iloc [] (position-based).
3. Slicing rows and columns.
Chapter 6: Modifying and Updating Data
1. Adding new columns.
2. Updating values in cells/columns.
3. Renaming columns and indexes.
4. Dropping columns and rows.
5. Reindexing data.
6. Changing data types.
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 grouping.
2. Aggregation methods:
* Mean ().
* Sum ().
* Count ().
* Min ().
* Max ().

Chapter 9: Data operations in pandas.
1. Merging.
2. Concatenation.
3. Joining.
4. Map and apply map.
Chapter 10: Date & Time Operations
1. Creating Date Time objects.
2. Extracting parts of datetime:
3. dt. Year.
4. dt. month.
5. dt. day.
6. dt. Hour.
7. Filtering by date range.
8. Time-based indexing.
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:
* Line.
* Bar.
* Histogram.
* Box.
* Pie.
* Scatter.
2. Customizing visualizations:
* Title.
* Grid.
* Legend.
* Colors.
* Labels.

Module 14: NUMPY.
Chapter 1: Introduction to NumPy
1. What is NumPy, and why use it?
2. Applications and benefits of NumPy.
3. How to Install NumPy.
4. How to Import NumPy.
5. Differences between Python lists and NumPy arrays.
Chapter 2: NumPy Arrays Basics.
1. What is an Array?
2. Types of Arrays?
* One-dimensional array.
* Two-dimensional array.
* Three-dimensional array
3. Creating arrays:
* list/tuple.
* Multidimensional arrays.
4. Checking or NumPy array attributes.
* Shape.
* Size.
* Data type.
* Number of dimensions.
5. Perform arithmetic operations in an ndarray.
Chapter 3: Array Creation Functions.
1. np. zeros ().
2. np. ones ().
3. np. Arrange (start, stop, step).
4. np. linspace (start, stop, Num).
5. np.eye (n) for identity matrix.
6. Random arrays:
* np. random. Rand ().
* np. random. Randn ().
* np. random. Randint (low, high, size).

7. Algebraic functions.
* Inverse ().
* Transpose ().
* Trace ().
Chapter 5: Array Attributes and Properties
1. Changing shape or Shape Manipulation:
* Ravel ().
* Reshape ().
* Resize ().
2. Copy vs View:
* Shallow copy.
* deep copy.
3. Concatenation:
* np. concatenate ().
* np. vstack ().
* np. hstack ().
4. Splitting:
* np. split ().
* np. Hsplit ().
* np. 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: np.dot (), @ operator.
2. Matrix multiplication: np. Matmul ().
3. Transpose: T.
4. Inverse: np.linalg.inv ().
Chapter 9: Random Module in NumPy.
1. np. random. seed () for reproducibility.
2. Uniform distribution: np. random. Rand ().
3. Normal distribution: np. random. Randn. ().
4. Random integers: np. random. Randint ().

Chapter 10: Universal Functions.
1. Arithmetic functions.
o add, subtract, multiply, and divide.
2. Trigonometric functions.
o sin, cos, tan.
3. Rounding functions.
o floor, ceil, round, around.

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 BI.
4. Benefits of Power BI in data analytics.
5. Difference between Power BI vs Excel vs Tableau.
6. Installation and setup.

Chapter 2. Power BI Architecture & Workflow.
1. Power BI workflow: ETL ? Modelling ? Visualization ? Publishing.
2. Overview of components:
o Power BI Desktop.
o Power BI Service (Cloud).
o Power BI Mobile App.
o Power BI Report Server.
o Power BI Gateway.
o Power BI Embedded.
o Power BI Dataflows.

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

Chapter 4. Power Query Editor (Data Transformation).
1. Interface overview & applied steps.
2. M language basics.
3. Data cleaning:
o Rename, Remove, Replace columns.
o Filter, Sort, Group By, Transpose.
o Split, Merge, Pivot/Unpivot.
o Conditional columns.
4. Append vs Merge Queries.
5. Handle nulls, errors, and duplicates.
6. Parameterized queries.
Chapter 5. Data Modelling.
1. Star and Snowflake schemas.
2. Fact vs Dimension tables.
3. Relationships:
o One-to-many, Many-to-one.
o Cardinality, Active/Inactive.
4. Data types and formatting.

5. Role-playing dimensions.
6. Hide/unhide fields.
7. Best practices in data modelling.
Chapter 6. DAX (Data Analysis Expressions).
1. Syntax & structure.
2. Measures vs calculated columns.
3. Operators and data types.
Core Functions:
1. Aggregation:
* Sum.
* Count.
* Average.
2. Logical:
* If.
* Switch.
* And.
* Or.

3. Text:
* Left.
* Right.
* Concatenate.
* Search.

4. Date/Time:
* Today.
* Now.
* Date add.
* Datediff.
* Year.
* Month.

5. Filter:

* Calculate.
* All.
* Values.

6. Lookup:
* Related.
* Lookup value.
7. Time Intelligence:
* Totally.
* Same period last year.
* Parallel period.
8. Variables:
* Var.
9. Error handling:
* If error.
* Is blank.

Chapter 7. Visualizations in Power BI.
1. Basic Charts:
* Bar.
* Column.
* Line.
* Area.
* Pie.
* Donut.
2. Advanced:
* Table, Matrix.
* KPI, Gauge, Waterfall, Funnel, Tree Map.
* Decomposition Tree.
* Maps: Basic, Filled, ArcGIS.
3. Custom visuals from AppSource.
4. Tooltips, drilldowns.
5. Bookmarking and storytelling visuals.
Chapter 8. Filters and Slicers.
1. Types:
o Visual-level.
o Page-level.
o Report-level

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

Chapter 9. Report Design and Formatting.
1. Page setup: size, background, themes.
2. Use of images, shapes, and icons.
3. Alignment, layering, gridlines.
4. Buttons for navigation, bookmarks.
5. Selection and bookmarks pane.
6. Tooltips and drill-through pages.
7. Dynamic Titles using DAX.
Chapter 10. Power BI and Excel Integration.
1. Exporting data to Excel.
2. Analysing datasets using PivotTables.
3. Using the “Analyse in Excel” option.
Chapter 11. Performance Optimization.
1. DAX performance tips.
2. Visual load optimization.
3. Performance Analyzer and DAX Studio.
4. Best practices for data modelling.