Accredited & Recognized Excellence

Program Key Benefits

Master Skills: Industry-relevant skills with hands-on training from top corporate professionals.

Eligibility: Open to Any Graduates and individuals with Career Gaps.

Expert Trainers: Led by Mr. Siva • Mr. Mani • Mr. Sharif (15+ Yrs experience), successfully completing 80+ batches.

Program Duration: Comprehensive 6 Months training schedule.

Lifetime Live Classes & Video Access: Lifetime access to live classes and recorded videos for learning, revision, and practice.

Doubt Clarification: Support via Live Classes, WhatsApp, and LMS.

Assessments: Regular Interview Questions and Code Assessments included.

Career Support: Resume Preparation, LinkedIn Profile Creation, and Portfolio Website Development.

Certificates: Free Training Completion Certificate (Soft Copy) provided.

Industry Terms & Tips: Coverage of Agile, Scrum, Monitoring, and corporate career success strategies.

One-on-One Demo: Get Personalized Training & Clarifications before getting started.

Tools & Technologies Covered

Core & Advanced Python NumPy, Pandas & Matplotlib Business Statistics Machine Learning SQL (SQL Server, Oracle, MySQL, PostgreSQL) NoSQL (MongoDB, DynamoDB) Microsoft Excel (Basic & Advanced) Microsoft Power BI Tableau Microsoft Cloud: Azure Microsoft Power Automate Business Process Analysis Financial, Marketing & Sales Analytics Customer & Supply Chain Analytics Predictive Analytics Agile, Jira & Confluence Generative AI Tools (CoPilot, ChatGPT, Gemini) 10 Practical Projects

Complete Course Syllabus

Introduction to Programming and Python
History, Features, Advantages, and Applications of Python
Python vs. Other Programming Languages
Installing Python and Setting Up the Environment
Python Interpreter and Execution Process
Python Interactive Mode and Script Mode
IDEs: IDLE, VS Code, and PyCharm
Writing and Executing the First Python Program

Variables, Keywords, and Identifiers
Naming Conventions and Comments
Literals and Types of Literals
Numeric, String, Boolean, and None Literals
Collection Literals: List, Tuple, Set, and Dictionary
Python Built-in Data Types
Type Checking Using type() and Object Identity Using id()
Type Conversion and Type Casting
Built-in Conversion Functions: int(), float(), str(), bool()
Collection Conversions: list(), tuple(), set(), and dict()
User Input Using input()
Output Using print() and Parameters sep and end
String Formatting: f-strings, format(), and % Formatting
Multiple Assignment and Variable Unpacking
Comments, Indentation, and Basic Python Syntax

Arithmetic Operators
Comparison and Logical Operators
Assignment and Compound Assignment Operators
Bitwise Operators
Membership Operators: in and not in
Identity Operators: is and is not
Difference Between == and is
Operator Precedence and Associativity
Practical Programs Using Python Operators

Introduction to Decision-Making
if Statement
if-else Statement
if-elif-else Statement
Nested if Statements
Conditional Expressions (Ternary Operator)
match-case Statement (Python 3.10+)
Truth Values and Conditions
Real-Time Decision-Making Programs

Introduction to Iteration and Loops
while Loop and for Loop
range() Function and Its Variations
Nested Loops
break, continue, and pass Statements
else with for and while Loops
Loop Control and Infinite Loops
Pattern Programming
Number and Sequence-Based Programs
Real-Time Loop Examples

String Creation and String Literals
Indexing, Negative Indexing, and Slicing
String Immutability and String Operators
String Concatenation and Repetition
String Searching Methods: find(), rfind(), index(), rindex()
Case Conversion Methods: upper(), lower(), title(), capitalize(), swapcase()
String Validation Methods: isalpha(), isdigit(), isalnum(), isspace(), islower(), isupper()
String Cleaning Methods: strip(), lstrip(), rstrip()
String Replacement and Translation Methods
String Splitting and Joining: split(), rsplit(), splitlines(), join()
String Partitioning: partition() and rpartition()
String Prefix and Suffix Checking: startswith() and endswith()
String Formatting: format(), format_map(), and f-strings
String Alignment: center(), ljust(), and rjust()
Raw Strings and Escape Sequences
String Validation, Manipulation, and Processing Programs

Introduction to Python Collections
Lists: Creation, Indexing, Slicing, and Methods
List Operations: append(), extend(), insert(), remove(), pop(), clear()
List Searching and Sorting: index(), count(), sort(), reverse()
List Copying and Nested Lists
List Comprehensions and Conditional Comprehensions
Tuples: Creation, Indexing, Slicing, Packing, and Unpacking
Tuple Methods: count() and index()
Sets: Creation, Uniqueness, and Set Operations
Set Methods and Mathematical Set Operations
Frozensets: Concepts and Operations
Dictionaries: Keys, Values, Items, and Methods
Dictionary Methods: get(), update(), pop(), setdefault(), and items()
Dictionary Iteration and Dictionary Comprehensions
Nested Collections and Collection Unpacking
Mutable vs. Immutable Objects
Collection Conversion and Common Collection Operations

Introduction to Functions and Modular Programming
Defining and Calling Functions Using def
Parameters, Arguments, and Function Calls
Positional, Keyword, and Default Arguments
Variable-Length Arguments: *args and **kwargs
Return Statements and Returning Multiple Values
Local and Global Variables
Variable Scope and the LEGB Rule
Lambda Functions
Recursive Functions and Recursion Examples
map(), filter(), and reduce() Functions
Docstrings and Function Annotations
Function Documentation Using __doc__ and help()
Practical and Real-Time Function Examples

Introduction to Modules and Packages
Built-in and User-Defined Modules
Importing Modules Using import and from...import
Module Aliases Using as
math Module: Mathematical Functions and Constants
random Module: Random Numbers, choice(), choices(), sample(), and shuffle()
time Module: Time Values, sleep(), and Basic Time Operations
datetime Module: date, time, datetime, and timedelta
Date and Time Formatting, Parsing, Comparison, and Calculations
Creating Custom Modules and Package Structures
__name__ == "__main__" and Script Execution
Installing and Managing Packages Using pip
Reusing Code Through Modules and Packages

Introduction to Errors and Exceptions
Syntax Errors vs. Runtime Exceptions
Common Python Exceptions: ValueError, TypeError, NameError, IndexError, KeyError, ZeroDivisionError
try and except Blocks
Handling Specific and Multiple Exceptions
else and finally Blocks
Raising Exceptions Using raise
Custom Exception Classes: Introduction
Exception Handling in Real-Time Programs

Introduction to File Handling
Opening Files Using open()
File Modes: r, w, a, x, b, and +
Reading Files Using read(), readline(), and readlines()
Writing and Appending Data to Text Files
File Object Methods: write(), writelines(), seek(), and tell()
Using the with Statement for Safe File Handling
CSV File Handling Using the csv Module
JSON File Handling Using the json Module
File and Directory Operations
Handling File-Related Exceptions

Common Built-in Functions: len(), type(), id(), and help()
Numeric Functions: abs(), round(), pow(), sum(), min(), and max()
Sorting and Reversing: sorted() and reversed()
Boolean Testing Functions: any() and all()
Iteration Helpers: enumerate() and zip()
Object and Sequence Functions: isinstance(), range(), and slice()
Practical Examples Using Built-in Functions

Nested Functions and Enclosing Scope
First-Class Functions and Functions as Objects
Passing Functions as Arguments
Higher-Order Functions
Returning Functions from Other Functions
Closures and Variable Scope
Decorators and @ Decorator Syntax
Practical Applications of Function Composition and Reuse

Introduction to Regular Expressions and the re Module
Regex Patterns and Raw Strings
Pattern Matching Using match(), search(), and fullmatch()
Finding Matches Using findall() and finditer()
Text Replacement Using sub() and subn()
Splitting Text Using split()
Special Sequences: \d, \D, \s, \S, \w, and \W
Word Boundaries and Anchors: \b, \B, \A, and \Z
Character Sets, Ranges, and Negation
Quantifiers, Groups, and Alternation
Positive and Negative Lookaheads
Password Validation Using Regular Expressions
Email, Phone Number, and Text Validation Examples

Classes, Objects, and Constructors (__init__)
Instance, Class, and Static Methods
Encapsulation, Inheritance, and Polymorphism
Method Overriding, Abstraction, and Abstract Classes
Magic or Dunder Methods

First-Class Functions and Nested Functions
Closures and Function Decorators
Decorators with Arguments & Multiple Decorators
Practical Logging and Timing Examples

Iterables vs. Iterators (iter() and next())
Creating Custom Iterators
Generator Functions and the yield Keyword
Generator Expressions & Lazy Evaluation

Patterns, Metacharacters, and Character Classes
match(), search(), findall(), and sub() functions
Groups, Lookaheads, Lookbehinds, and Word Boundaries
Email, Phone Number, and Password Validation

Comprehensions (List, Set, Dictionary)
Shallow Copy vs. Deep Copy
Context Managers, enumerate(), and zip()
Working with collections, itertools, and Type Hints

What is NumPy? & NumPy Arrays vs. Python Lists
Creating 1D and Multidimensional Arrays
Array Attributes (shape, size, ndim, dtype)

Indexing, Slicing, Reshaping, and Transposing Arrays
Joining, Splitting, Copying, and Viewing Arrays
Arithmetic Operations, Broadcasting, and ufuncs

Mathematical & Statistical Functions (Mean, Median, Std Dev, Variance)
Random Number Generation & Basic Matrix Operations
Linear Algebra Fundamentals

Series and DataFrames Creation
DataFrame Attributes, Methods, and Data Inspection

Reading and Writing CSV, Excel, and JSON Files
Setting and Resetting DataFrame Indexes

Using loc[] and iloc[] for Data Selection
Filtering, Sorting, Adding, and Renaming Columns
Vectorized Operations and Applying Functions

Identifying and Handling Missing Values
Removing Duplicates and Changing Data Types
String Operations and Handling Dates/Times

Aggregate Functions and groupby() Operations
pivot_table(), Merging, Joining, and Concatenating DataFrames

Importance of Visualization in Data Analysis
Introduction to pyplot, Figures, and Axes

Line, Bar, and Horizontal Bar Charts
Scatter Plots, Histograms, Pie Charts, and Box Plots

Titles, Axis Labels, Legends, and Grid Lines
Colors, Markers, Line Styles, and Annotations
Saving Charts as Image Files

Subplots, Multiple Axes, and Pandas DataFrame Plotting
Visualizing Trends and Handling Dates on Charts

Student Marks and Sales Trend Analysis
Product Comparisons and Salary Distribution Datasets

AI vs. Machine Learning vs. Deep Learning
Large Language Models (LLMs) & Token Context Windows
Applications, Limitations, and Ethical Usage

Writing Clear and Effective Prompts
Role-Based, Zero-Shot, and Few-Shot Prompting
Structured Output Generation and Refinement

ChatGPT, Google Gemini, and Microsoft Copilot Fundamentals
AI-Assisted Python Code Generation & Debugging
Generating SQL Queries and Testing Documentation

• Introduction to statistics
• Population and sample
• Mean, median, and mode
• Range, variance, and standard deviation
• Percentiles and quartiles
• Probability fundamentals and distributions

• Correlation and covariance
• Sampling techniques
• Hypothesis testing
• Confidence intervals
• Introduction to regression analysis
• Statistical interpretation for business decisions

Database Fundamentals and SQL

• Introduction to relational databases and SQL Server setup
• Creating databases and tables
• Data types, constraints, primary keys, and foreign keys
• SELECT, WHERE, DISTINCT, and ORDER BY
• Filtering with comparison and logical operators
• INSERT, UPDATE, and DELETE
• Aggregate functions, GROUP BY, and HAVING
• String, date, and mathematical functions

Advanced SQL and Analytics

• INNER, LEFT, RIGHT, and FULL JOINs
• Subqueries and correlated subqueries
• Common Table Expressions (CTEs)
• Views and temporary tables
• Stored procedures and user-defined functions
• Window functions and ranking
• Indexes and query optimization
• Transactions and error handling
• Business reporting queries
• Connecting SQL Server with Python and Power BI

• Introduction to NoSQL databases
• Relational databases versus document databases
• MongoDB architecture and installation
• Databases, collections, and documents
• JSON and BSON fundamentals
• CRUD: Create, Read, Update, and Delete
• Filtering and querying documents
• Comparison and logical operators

• Projection, sorting, and limiting results
• Embedded documents and arrays
• Updating nested fields
• Aggregation pipeline fundamentals
• $match, $group, $project, $sort, and $lookup
• Indexes, query performance, and schema design
• Connecting MongoDB with Python
• Analyzing semi-structured customer and transaction data

Excel Fundamentals

• Excel interface and workbook management
• Data entry, formatting, rows, columns, and cell references
• Sorting, filtering, and basic formulas
• Conditional formatting and data validation

Advanced Excel and Reporting

• IF, nested IF, AND, and OR
• SUMIF, SUMIFS, COUNTIF, and COUNTIFS
• VLOOKUP, HLOOKUP, XLOOKUP, and INDEX-MATCH
• Text, date, logical, and statistical functions
• PivotTables and PivotCharts
• Advanced filtering and What-if analysis
• Goal Seek and Scenario Manager
• Dashboard creation, data cleaning, and reporting
• Power Query fundamentals
• Business and financial reporting

Data Preparation and Modeling

• Introduction to Business Intelligence and Power BI Desktop
• Connecting Excel, SQL Server, CSV, and other data sources
• Data transformation using Power Query
• Data modeling and relationships
• Star and snowflake schemas
• DAX fundamentals, calculated columns, and measures

Dashboards and Deployment

• Time intelligence
• KPIs and performance metrics
• Interactive charts, drill-down, drill-through, and tooltips
• Filters and slicers
• Dashboard and report development
• Row-Level Security (RLS)
• Publishing to Power BI Service
• Scheduled refresh, sharing, and business case studies

• Introduction to Tableau
• Connecting to different data sources
• Dimensions and measures
• Data preparation and joins
• Charts, graphs, and visual analytics
• Filters, groups, and sets

• Calculated fields
• Parameters and table calculations
• Interactive dashboards
• Story points
• Publishing and sharing dashboards
• Business reporting and visualization projects

Business Analysis Fundamentals

• Introduction to Business Analytics
• Descriptive, diagnostic, predictive, and prescriptive analytics
• Business requirements and KPI identification
• Business problem-solving and data-driven decisions
• Sales and revenue analysis
• Customer behavior and segmentation
• Marketing campaign analysis

Business Processes and Reporting

• Financial and profitability analysis
• Inventory, supply chain, and operations analytics
• Forecasting and trend analysis
• Business reporting and dashboard interpretation
• Agile fundamentals
• Jira and Confluence fundamentals
• Business requirements documentation
• Stakeholder communication and reporting

Fundamentals and Supervised Learning

• Introduction to Artificial Intelligence and Machine Learning
• Supervised and unsupervised learning
• Training and testing datasets
• Feature selection and feature engineering
• Data preprocessing and model evaluation
• Linear and Multiple Linear Regression
• Logistic Regression
• Decision Trees and Random Forest
• K-Nearest Neighbors (KNN)

Practical Machine Learning

• Classification and regression use cases
• Model performance metrics and validation
• Clustering fundamentals and K-Means
• Customer segmentation
• Dimensionality reduction fundamentals
• Anomaly detection basics
• Predictive analytics and sales forecasting
• Customer churn prediction
• Introduction to model deployment
• Business case studies using Python

AI Tools and Prompt Engineering

• Introduction to Generative AI and Large Language Models
• Using ChatGPT for data analysis
• Microsoft Copilot for productivity and reporting
• Google Gemini fundamentals
• Prompt engineering fundamentals
• Writing effective prompts for analytics tasks

AI-Assisted Analytics

• Generating Python code with AI tools
• Explaining SQL queries using AI
• Creating Excel formulas with AI
• Analyzing datasets with AI assistance
• Generating business summaries and report narratives
• Identifying trends and anomalies
• AI-assisted data cleaning and transformation
• Generating insights and recommendations
• Validating AI-generated results
• Responsible AI use, privacy, and data security

• Introduction to cloud computing
• Cloud service models: IaaS, PaaS, and SaaS
• Azure portal fundamentals
• Azure storage fundamentals
• Cloud data storage concepts

• Introduction to Azure SQL Database
• Introduction to Azure analytics services
• Cloud-based data processing concepts
• Data security and access management
• Cloud deployment fundamentals

• Introduction to workflow automation
• Creating automated cloud flows
• Creating scheduled flows
• Creating instant flows

• Automating Excel-based tasks
• Email and notification automation
• Approval workflows
• Connecting Microsoft 365 applications
• Automating routine business processes and reports

• Resume preparation for Data Analyst and Business Analyst roles
• LinkedIn profile optimization
• Data Analyst and Business Analyst interview questions
• SQL interview questions and exercises
• Python and Pandas interview questions

• Excel and Power BI interview questions
• Statistics and Machine Learning interview questions
• Business case studies
• Communication and presentation skills
• Explaining projects in interviews
• Mock interview practice

Project Deliverables

• 1. Sales Performance Analysis – Python, SQL Server, Excel, and Power BI
• 2. Customer Segmentation – Python, Pandas, and Business Analytics
• 3. Financial Performance Dashboard – Excel and Power BI
• 4. Retail Inventory Analysis – SQL Server and Power BI
• 5. Marketing Campaign Analysis – Excel, Python, and visualization

• 6. HR Analytics Dashboard – SQL Server, Excel, and Power BI
• 7. Customer Churn Prediction – Machine Learning
• 8. E-commerce Business Analysis – Python, SQL, and Tableau
• 9. Business KPI Dashboard – Power BI and DAX
• 10. Generative AI-Assisted Business Reporting – ChatGPT or Copilot

• Hands-on coding and analytics exercises
• Real-world datasets and business scenarios
• Module-wise assignments and assessments
• Project development and documentation

• Interview preparation and question banks
• Resume and portfolio guidance
• Industry-oriented practical training
• Placement support

Fee Structure & Batches

Group Online Fee You Save ₹15,000

₹15,000 ~~₹30,000~~

Classroom Fee You Save ₹25,000

₹25,000 ~~₹50,000~~

One-to-One Online You Save ₹75,000

₹75,000 ~~₹1,50,000~~

Syllabus and certificates are 100% identical across all batch options. Only the batch mode and pricing differ.

Book Free Demo Class Call +91 99636 89248

On Reviews — What Our Students Say

★★★★★

"I joined the Data & Business Analytics course at SivaSoft. The training helped me understand Excel, SQL, Power BI, and data analysis concepts in a simple way. The real-time examples and practical sessions made learning easier. The Generative AI topics were also very useful."