Diploma in Business & Data Analytics with Gen AI & ChatGPT

Diploma in Business & Data Analytics with Gen AI

Mode: Classroom Offline (Pune) / Live Virtual Online / Hybrid

4.9 (24,598) reviews

Statistics + Python + MySQL + Power BI + Machine Learning + Deep Learning + Time Series + LLM(Gen AI) + NumPy + Pandas + Plotly + PandasAI + Adv Excel + AI with Python + AI with SQL + AI with Power BI

Bonus Free Content 🔥

✅ Internship and career mentorship programs till placements

Note: Separate batch & extra sessions for NON-IT Professionals to build strong programming skills from scratch.

Our Promises

100% Job Guaranteed Support

100% Guaranteed Paid/Unpaid Internship

Unlimited Job Opportunities

No Technical Background Required. For IT & NON-IT

Program Flow & Highlights

Covers Python Basic & Advanced with NumPy, Pandas, Data Viz, Matplotlib, Seaborn, Plotly with Cufflinks, Exploratory Data Analysis (EDA) & PandasAI
AI with Python using Gen AI, ChatGPT 5, Gemini, Anaconda AI Assistant
Code Fast & Productivity Improvements Hacks using AI
Deveolop Dashboard using Power BI & Tableau
Foundation of statistics for data science
Become expert in Oracle MySQL Workbench & Mongo DB

Course Syllabus

Module 1: Python Basic & Advanced with AI

The Python Ecosystem & Setup
Scripting
Functional Programming
Conditional Statements and Loops
UDF Functions and Object Functions
File Handling with Python
Exceptional Handling
Object-Oriented Python
Advanced Topics
Decorators, Descriptors, Debugging
Framework & Regular expression
Jupyter Notebook, Google Colab
Python Libraries – NumPy, Pandas
Data Visualization
Seaborn, Matplotlib , Plotly, Cufflinks
EDA (Exploratory Data Analysis)
Data Analytics using PandasAI
Anaconda AI Assistant, Gemini AI
Python with Gen AI

Module 2: AI with MySql with Oracle Workbench

Introduction
- What is a Database?
- Importance of Databases in Real-World Applications
- What is SQL (Structured Query Language)?
- Understanding RDBMS (Relational Database Management Systems)
- Advantages of Using SQL
- Popular SQL Databases: MySQL, SQL Server, PostgreSQL, Oracle
- SQL vs NoSQL Databases
Installation
- Installing MySQL Workbench
- Setting up MySQL Workbench
- Understanding MySQL Workbench Interface
SQL Sublanguages
- Overview of SQL Language Components - DDL (Data Definition Language) - CREATE, ALTER, DROP, TRUNCATE
- DML (Data Manipulation Language) - INSERT, UPDATE, DELETE
- DCL (Data Control Language) - GRANT, REVOKE
- TCL (Transaction Control Language) - COMMIT, ROLLBACK, SAVEPOINT
- DQL (Data Query Language) - SELECT & Role and Importance of Each Sublanguage in SQL
Data Types and Constraints
- SQL Data Types (Numeric, Character, Date/Time)
- Choosing Appropriate Data Types
- SQL Constraints Overview - NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, DEFAULT, CHECK
- Applying Constraints While Creating Tables
- Modifying and Dropping Constraints
- Understanding Referential Integrity
SQL Clauses
- SELECT Statement usage for Fetching Data
- FROM, WHERE, ORDER BY, and DISTINCT Clauses
- Filtering Data using WHERE
- Sorting Data using ORDER BY
- Using Aliases for Tables and Columns
- Understanding LIMIT and OFFSET
Operators
- Understanding Operators
- Arithmetic Operators
- Comparison Operators
- Logical Operators
- Bitwise Operators
Group By & Having Clause
- Aggregating Data using GROUP BY
- Aggregate Functions: SUM, AVG, COUNT, MIN, MAX
- Filtering Aggregates using HAVING
- Combining WHERE and HAVING in Queries
- Grouping Multiple Columns
Conditionals
- Using Conditional Logic in SQL
- CASE WHEN THEN END Statements
- Using IF and IFNULL / COALESCE
- Handling NULL Values in Expressions
- Conditional Aggregation Examples
Built-In Functions
- String Functions
- Date & Time Functions
- Numeric Functions
UNION Operator
- Understanding UNION and UNION ALL
- Combining Results from Multiple Queries
- Rules for UNION Compatibility
- Removing Duplicates in UNION
- Practical Use Cases of UNION
Subquery
- Definition and Importance of Subqueries
- Types of Subqueries – (Single Row Subquery, Multiple Row Subquery, Correlated Subquery
- Nested Subqueries and Performance Optimization
Joins
- Understanding Relationships Between Tables & Types of Joins – (INNER JOIN. LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, SELF JOIN
- Combining Multiple Joins
- Real-Life Join Scenarios
Views
- Introduction to Views
- Creating and Managing Views
- Simple vs Complex Views
- Updatable Views and Limitations
- Benefits of Using Views for Security and Simplification
Stored Procedures & Functions
- What are Stored Procedures?
- Creating and Executing Stored Procedures
- Parameters (IN, OUT, INOUT)
- Conditional Logic in Procedures
- User Defined Functions (UDFs)
- Difference Between Procedures and Functions
Introduction to Generative AI
- What is Artificial Intelligence (AI)?
- Difference between AI, Machine Learning (ML), Deep Learning (DL), and Generative AI
- How Generative AI works (LLMs, prompt engineering basics)
- Importance of Generative AI in database management and SQL
- Real-world applications in analytics and automation
AI vs Generative AI
- Traditional AI vs Generative AI: Key Differences
- Capabilities of Generative AI
- Role of Large Language Models (LLMs) in SQL Query Generation
- How Gen AI Enhances Developer Productivity
Benefits of Combining SQL and AI
- Speed & Productivity, Better Learning & Understanding, Industry Demand
- Use of AI in Business Intelligence and Data Analysis
- Skill-building for Future Data and AI Professionals
Use Cases of Gen AI with SQL
- Auto-Generate Database Schema
- Insert Mock / Sample Data
- Generate Views, Procedures & Functions
- SQL Query Optimization
- Translate SQL Across Dialects
- Explain SQL in Plain English
- Add Comments Automatically
Gen AI Tools for SQL
- ChatGPT, Copilot for SQL
- Enterprise AI Tool
- Best Practices for Using AI Tools Responsibly
  
  Self-Learning Section / Assignments
  - Create a database schema & Sublanguages of SQL practice

Module 3: Mathematics & Statistics

Statistics for Data Science Intro
Central Tendency & Percentile
Euclidean Distance
Manhattan Distance
Normal Distribution & Its Properties
Sampling & Variables - Simple Random, Stratified, Systematic
Box Plot & Standardization
Types of probability in statistics - classical, empirical, subjective.
Hypothesis Testing
P-Value, Confidence interval and Steps in hypothesis testing
Question of Hypothesis Testing

Module 4: Time Series

Introduction to time series
Patterns in time series and stationarity
Model Building on Time series
ARIMA

Module 5: AI with Advanced Excel

Introduction

Overview of Excel as a Business Intelligence & Data Analysis Tool
Understanding the Excel Interface and Ribbon
Workbook vs Worksheet concepts
Customizing Excel Environment and Quick Access Toolbar
Importance of Excel Shortcuts for Productivity

Cell Referencing

Types of Cell References: Absolute, Relative, and Mixed
Practical Use Cases of Cell Referencing in Formulas
Named Ranges and their Advantages
Dynamic Named Ranges for Automation
Troubleshooting Cell Reference Errors

Formulas

Formula Syntax and Order of Precedence (PEMDAS)
Creating and Editing Formulas Efficiently
Using AutoSum, Formula Auditing, and Trace Tools
Handling Formula Errors (IFERROR, ISERROR, etc.)

Functions

Difference Between Formulas and Functions
Mathematical and Statistical Functions
Text Functions
Logical Functions
Date & Time Functions
Nesting Multiple Functions for Complex Calculations

Lookup Functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH)

VLOOKUP and Its Limitations
Introduction to XLOOKUP – Replacing VLOOKUP/HLOOKUP
Using INDEX-MATCH for Dynamic Lookups
Two-Way Lookup using INDEX + MATCH
Handling Lookup Errors and Improving Efficiency

Dynamic Array Functions (FILTER, SORT, UNIQUE)

Understanding Dynamic Arrays and Spill Behavior
Using FILTER to Extract Data Dynamically
Sorting Data with SORT and SORTBY
Removing Duplicates using UNIQUE
Combining Dynamic Functions for Live Reports

Conditional Formatting

Applying Rules to Highlight Data (Greater Than, Text Contains, etc.)
Using Formulas within Conditional Formatting
Creating Data Bars, Icon Sets, and Color Scales

What-If Analysis & Scenario Tools

Introduction to What-If Analysis
Using Goal Seek for Target-Based Calculations
Creating Multiple Scenarios with Scenario Manager
Data Tables (One-Variable & Two-Variable Analysis)
Applying Solver for Optimization Problems

Connecting Excel to External Data Sources

Importing Data from CSV, Web, Databases, and Power BI
Using Data Tab Connectors (SQL, OData, SharePoint, etc.)
Data Refresh and Scheduled Updates
Combining Multiple Data Sources into One Table
Understanding
Linked Data Types

Power Query

Introduction to Power Query (ETL in Excel)
Loading Data from Multiple Sources
Data Cleaning and Transformation (Remove Duplicates, Pivot/Unpivot)
Merging and Appending Queries
Using Parameters and Custom Columns
Introduction to M Language

Power Pivot

Overview of Data Modeling in Excel
Creating Relationships Between Tables
Using the Data Model to Build PivotTables
Introduction to DAX (Data Analysis Expressions) in Excel
Calculated Columns, Measures, and KPIs
Advantages of Power Pivot over Traditional PivotTables

Charting Techniques

Designing Professional Charts (Column, Line, Pie, Combo, etc.)
Using Advanced Charts (Waterfall, Histogram, Box Plot, Pareto)
Creating Interactive Charts with Form Controls
Dynamic Charts using Named Ranges and Drop-downs
Chart Formatting Tips for Dashboards

Dashboard Creation

Planning and Designing a Dashboard Layout
Using Form Controls (Drop-downs, Sliders, Checkboxes)
Integrating Charts, Tables, and KPIs
Using Slicers and Timelines for Interactivity
Automating Dashboard Updates with Power Query & Pivot
Dashboard Design Best Practices (Color, Layout, Readability)

Macros & VBA Automation

Introduction to Macros and Recording Basic Tasks
Understanding VBA Editor and Code Structure
Writing Simple VBA Scripts for Automation
Creating User Forms and Buttons
Error Handling and Debugging in VBA
Practical Examples: Report Automation, Data Cleanup, File Handling

Introduction to AI in Excel

Understanding the role of AI in modern Excel
Difference between Traditional Excel and AI-Powered Excel
Overview of AI features: Analyze Data, Copilot, and Smart Plugins
How AI enhances productivity, accuracy, and reporting speed

Analyze Data Feature using AI

What is the “Analyze Data” tool in Excel?
Exploring the Ideas Pane for instant insights
Automatically generating PivotTables, Charts, and Summaries
Using Natural Language Queries to ask questions about your data
Real-time examples: Sales summary, trend detection, and key drivers

Excel Copilot Integration using AI

Introduction to Microsoft 365 Copilot in Excel
Using Natural Language to:
- Write formulas
- Clean or transform data
- Create charts or tables automatically
Generating trend analysis, forecasts, and summaries using prompts
Reviewing, editing, and validating Copilot-generated content
Limitations and best practices for using Copilot responsibly

Generating VBA Scripts using AI

- Introduction to AI-assisted VBA generation
- Using AI Tools to automate repetitive Excel tasks
- Generating VBA code for:
  - Data cleaning and formatting
  - Report automation and file handling
  - Custom user forms or buttons
- Understanding and modifying AI-generated VBA scripts
- Debugging, validating, and improving code efficiency with AI suggestions
- Best practices and security considerations when using AI for automation

Module 6: AI with Power BI

Introduction

What is Business Intelligence (BI)?
Overview of Power BI and its ecosystem
Components of Power BI (Desktop, Service, Mobile)
Understanding data-driven decision-making

Installation & Get Data

Installing Power BI Desktop
Overview of Power BI Interface
Understanding Data Sources
Connecting to different data sources
Import vs. Direct Query mode
Data Refresh concepts

Power Query Editor

Introduction to Power Query (ETL Process)
Data Cleaning and Transformation
Removing duplicates, handling errors, and missing data
Column operations: Split, Merge, Replace, Fill
Data type conversion and formatting
Appending and Merging queries
Using Parameters in Power Query
Introduction to M language

Data Modeling

Importance of Data Modeling in BI
Understanding Relationships (One-to-One, One-to-Many, Many-to-Many)
Creating and Managing Relationships in Power BI
Star vs Snowflake Schema
Role of Fact and Dimension tables

Data Visualization

Introduction to Power BI Visuals
Creating Built in Visuals
Color and Conditional Formatting
Slicers, Filters, and Bookmarks
Drill Through, Drill Down, and Tooltip pages
Using Themes and Custom Visuals
Designing Interactive Reports
Report Design Best Practices and Storytelling with Data

DAX (Data Analysis Expressions)

Introduction to DAX
Calculated Columns vs. Measures
Aggregation Functions
Filter Functions
Logical Functions
Text Functions
Date & Time Functions
Time Intelligence Functions
Relationship Functions
Parent & Child Functions
Performance optimization and debugging DAX

Power BI Project (End-to-End Implementation)

Selecting a real-world dataset (Excel/SQL/Web)
Requirement gathering
Data import and cleaning using Power Query
Data model designing
Creating calculated columns and measures
Building interactive visuals and Reports
Adding narrative and filters for user interaction

Power BI Service Overview

Introduction to Power BI Service
Publishing and Sharing Reports from Power BI Desktop
Creating and Managing Workspaces
Building and Sharing Dashboards Online
App Creation and Distribution
Collaboration and Permissions in Power BI Service
Scheduling Data Refresh and Alerts

Introduction to AI in Power BI

Overview of AI capabilities in Power BI
Understanding how AI enhances analytics and insights
Types of AI features in Power BI

Forecasting using AI

Using Forecasting in Line Charts
Configuring Confidence Intervals and Forecast Length
Analyzing Seasonality and Trends
Comparing Actual vs. Predicted values
Best practices for forecasting accuracy

Anomaly Detection using AI

Enabling and Configuring Anomaly Detection
Understanding Sensitivity and Expected Range
Visualizing anomalies on time series data
Explaining anomaly causes and patterns

Explain the Variation using AI

Using “Explain the Increase/Decrease” feature
Understanding key drivers behind data change
Interpreting AI-generated insights
Turning insights into actionable decisions

AI Visuals

Smart Narrative Visual
Q&A Visual
Decomposition Tree Visual
Key Influencers Visual
Using AI visuals effectively in storytelling

Copilot Integration

Introduction to Power BI Copilot
Generating Reports and Summaries with Natural Language Prompts
Asking questions and creating visuals using Copilot
Enhancing productivity through AI assistance
Best practices and limitations of Copilot

DAX & M Code Generation using AI

Using Copilot to write and explain DAX formulas
Generating M queries in Power Query using AI suggestions
Validating and editing AI-generated code
Combining AI with human logic for optimized results

Module 7: Mongo DB

(Self Paced)

Understanding MongoDB

- NoSQL Databases
- JSON and BSON
- Vertical and Horizontal Scaling
- Data Types
- MongoDB Tools
- Collection and Database
- Schema Design and Modeling
- CRUD Operations in MongoDB
- Indexing and Aggregation
- Replication and Sharding
- MongoDB Cluster Operations

Module 8: Tableau

(Self Paced)

Introduction to Tableau
Installation of Tableau Desktop
Connectors & Interface of Tableau
Types of Data & Data Modeling
Data Modeling Practical
Tableau Joins (Theory + Practical)
Viz_01 - Bar & Column Chart
Viz_02 - Axis less Viz
Viz_03 - Line & Area Charts
Viz_04 - Variants of Column Charts, Maps & Custom Visualization (Donut Chart)
Viz_05 - Custom Charts - KPI & Waterfall
Viz_06 : Pareto & Statistical Chart
Group & Bins
Sets In Tableau
Filters : Dimension, Measure & Relative
Filters : Data Source, Contex
Calculated Fields
Parameter
Level of Details (LOD) Expressions
Project Workflow & Initial Setup
Final Dashboard Building

Module 9: Capstone Projects & Internship

Hands-on Capstone Projects
Internship Opportunity

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Free consulting from our expert

Gamaka Career Accelerator & Placements

Gamaka corporate skill development and placements.

Corporate Skill Assessment

Mock Interview (By External Professionals)

25 Projects / Case Studies

Resume Building

Social Media Profile Building (LinkedIn, Github))

Soft Skill Development

Our Process

1.

Get Trained

2.

Skill Assessments

3.

Interview Prepartion

4.

Apply for Jobs

5.

Get Internship

6.

Capstone Projects

Gen AI Examples Videos

Interview Questions Videos

Projects Videos

Course Fee

Diploma in Full Stack Data Science & AI (AWS Free)

12 Modules

50% OFF

₹ 1,40,000/- ₹ 69,500/-

2 Installments:

₹ 34,500/- (20 days gap)

Down Payment:

₹ 65,500/-

👉 Book in advance ₹500

👉 Book your seat now with just ₹500 advance payment!

✨ LIMITED SEATS – HURRY UP!

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Google Reviews

Aniket Umratkar

The Python course at Gamaka AI was a great learning experience. The curriculum was well-structured, covering everything from basic syntax to advanced concepts like data structures and OOP. The hands-on approach and real-world applications made learning engaging. The instructors were knowledgeable and provided clear explanations. Overall, a fantastic course for beginners and intermediate learners looking to build a strong foundation in Python!

Rachana Khokrale

The course is an incredible learning experience that exceeded my expectations in every way. The quality of course , expertise of the instructors and the flexibility of online platform made it a lot easier.

Pradnya Pilane

Had a great learning experience with Gamaka ai.. They provide best course structure for power bi and power bi advanced.. Best for non IT students to boost up their skill from the Scratch.

Devesh Mishra

I recently completed the Python module at Gamaka AI Institute in Pune, and I’m really happy with the training experience. The course was structured very well, and the trainer explained all the concepts clearly, making it easy to understand even complex topics. What I appreciated most is that the training is real-time and project-oriented, which helped me gain practical, hands-on experience. The trainer is knowledgeable, patient, and always ready to help.

Nikheil Pawar

I have enrolled for Data Science course in this institution. Till now I have covered 3 topics MYSQL, PYTHON AND POWER BI. The faculty is very supportive towards our goals for getting placed in good companies. The training is totally industry based. Every faculty explains the scenario based examples on the particular topics! Anyone who is looking for Data science or analytics related fields this is perfect institution!

Mayur Parthe

The Python course was well-structured and beginner-friendly. The concepts were explained in a simple and easy-to-understand manner, with plenty of hands-on examples. I appreciated the step-by-step guidance, interactive exercises, and real-world projects that helped reinforce learning. Overall, it was a great experience and a solid foundation for anyone starting their Python journey.

Deepesh Thakur

I enrolled in the Data Analytics course at Gamaka AI and had a great experience. The curriculum is well-structured, covering tools like Excel, SQL, Python, and Power BI with a strong focus on practical learning. The trainers are knowledgeable and supportive, and the hands-on projects really helped me apply what I learned. The placement support, including resume guidance and interview prep, is also very useful. Overall, it's a solid course for anyone looking to start a career in data analytics.

Career Success Stories

Explore Our Placement Opportunities.

These top-tier companies represent the pinnacle of career opportunities available through our placement services. Joining forces with industry leaders, we ensure your success by connecting you with the best possible placements.

Advantages of joining Gamaka AI

Our curriculum empowers you with the expertise needed to thrive in your career. Through systematic learning and practical industry projects, you'll adeptly address intricate challenges and remain at the forefront of the AI & ML field.

Instructor led online & classroom interactive sessions
One-To-One online problem-solving sessions
Complete Soft Copy of Notes & Latest Interview Preparation Set
Trainers are working IT professional with top IT MNC’s
100% Placement Assistance
Resume Building & Mock Interview Sessions

100% Hands-on Training with Live Projects/Case Studies
Internship & Course Completion Certificate
1 Year free subscriptions to portal for updated guides, notes, poc, projects & interview preparation set.
Extensive training programs with Recorded Sessions
24*7 Support on enquiry@gamakaai.com

Course Certificate

Upon successful completion of this data science course, you’ll earn a Certificate. The certificate adds the required weight in any portfolio.

Internship Certificate

This certificate will be issued to those pursuing internships with our development team or clients with whom we have tie-ups. Data Science Internship gives opportunity to learn from professionals, gain practical experience in this field, and build a robust professional network.

Take First Step Towards
Your IT Dream Today

What is Business & Data Analytics?

Business & Data Analytics (BDA) is a field that empowers businesses to make data-driven decisions by transforming raw data into actionable insights. It leverages techniques and tools from statistics, computer science, and domain expertise to unlock the hidden potential within data and answer critical business questions.

Data Acquisition
Data Analysis
Machine and Deep Learning
Data Visualization

BDA professionals collect data from various sources like databases, sales figures, customer surveys, and social media platforms. However, this data often requires cleaning, organizing, and formatting before analysis can begin.

Once the data is prepared, data scientists employ various statistical and analytical techniques to identify patterns, trends, and relationships within the data. This may involve techniques like hypothesis testing, correlation analysis, and regression modeling.

BDA professionals communicate their findings through compelling data visualizations. Tools like Tableau and Power BI help create charts, graphs, and other visual representations that effectively communicate insights to both technical and non-technical audiences within the organization.

Business & Data Analysts don’t just present data; they tell a story. They use data insights to explain trends, support recommendations, and persuade stakeholders to take action.

Impact of Data Science Across Industries

Business & Data Analytics (BDA) is revolutionizing various industries by transforming how they operate and make decisions. Here are some examples:

Finance

BDA helps with fraud detection, risk assessment, algorithmic trading, and personalized financial products.

Healthcare

BDA plays a role in medical diagnosis, drug discovery, patient care optimization, and personalized medicine.

Marketing

Businesses leverage BDA for customer segmentation, targeted advertising, campaign optimization, and measuring marketing effectiveness.

Retail

BDA aids in product recommendation, inventory management, supply chain optimization, and dynamic pricing strategies.

Manufacturing

Data science is used for fraud detection, risk assessment, and algorithmic trading.

Human Resources

BDA can help with talent acquisition, employee performance evaluation, and creating a more data-driven work environment.

Logistics

BDA optimizes delivery routes, predicts demand, and improves overall logistics efficiency.

Transportation

BDA optimizes delivery routes, predicts demand, and improves overall logistics efficiency.

Why Pursue a Data Science Career

High Demand

Business & Data Analytics is a booming field with high demand and growth. You’ll gain coveted skills, solve real business problems, and contribute to success. BDA offers lucrative salaries, career flexibility across industries, and the chance to continuously learn in an ever-evolving field. Launch your rewarding career in data today!

Who can take this course

This course caters to beginners with no prior coding experience (we’ll teach you Python!), career changers looking for a data field entry point, Subject Matter Experts (SMEs) seeking to leverage data analysis in their domain, and even busy working professionals with flexible online, offline, and hybrid learning options, including weekend batches.

Learning Outcomes

This program equips you with the skills to:

- Analyze & visualize data: Uncover insights and communicate them clearly.
- Master Python (and potentially R): Code for data manipulation.
- Understand Machine Learning (optional): Leverage algorithms for business applications.
- Make data-driven decisions: Solve real-world business problems.

Why Choose Gamaka AI

Hands-on Projects & Internship

Gain practical experience by real-world projects and participating in an internship.

Expert Instructors

Learn from data science professionals with real-world experience who will guide you through the program and answer your questions.

Industry Based Curriculum

Our curriculum is designed to equip you with the specific skills and knowledge that are in high demand by today’s data science employers.

100% Placement

We’ll equip you with the skills and support you need to land your dream data science job.

Real World Case Studies

Immerse yourself in real-world scenarios by exploring how data science is applied in various industries.

Extensive coverage on AI Tools

Use AI to clean, transform, and organize data efficiently. Saves time spent on repetitive data tasks.

Specializations & Advantages

1-on-1 Problem Solving Sessions

(softcopies of notes and interview prep guides)

Complete Course Materials

(softcopies of notes and interview prep guides)

Industry Expert Trainers

with experience at top MNCs

100% Placement Assistance

with resume building and mock interviews

Extensive Hands-on Learning

with real-world projects and case studies

1-Year Free Access to Learning Portal

for continuous learning

Call Us

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Free consulting from our expert

FAQs about the Course

Do I need prior experience in data analysis or programming?

No prior experience is necessary! Our program is designed for beginners and provides a strong foundation in programming (Python) along with the necessary data analysis skills.

What are the different learning formats available?

We offer flexible learning options to suit your schedule: online, offline (classroom), and hybrid. We also have special weekend batches for working professionals.

Is there any financial assistance available?

While we don't offer direct scholarships, we do have discounted program fees and flexible payment options, including installments and down payments.

Course Content

What modules are included in the program with Machine Learning?

The program with Machine Learning includes all the core modules like Statistics, Data Wrangling, Data Visualization, Programming, Business Intelligence, and Emerging Technologies. Additionally, it delves deeper into Machine Learning fundamentals and its applications in business contexts.

Can I take the program without the Machine Learning component?

Absolutely! We offer a separate diploma program without Machine Learning that focuses on core data analysis skills and business applications.

What programming languages will I learn?

The primary programming language taught is Python, a versatile and in-demand language for data analysis. Some programs may also offer an optional module on R programming.

Career & Internship

What kind of jobs can I get after completing this program?

This program equips you with skills for various data-related roles, such as Business Analyst, Data Analyst, Marketing Analyst, and Business Intelligence Analyst.

Is there an internship included in the program?

The program offers an optional 3-month internship opportunity to gain valuable industry experience and apply your acquired skills in a real-world setting.

What career support services do you offer?

We provide comprehensive career support, including resume building workshops, mock interviews, and access to our industry network to help you with job placement (subject to terms and conditions).