SQL & Databases
Rukmini Deshpande
Nine years in reporting roles at a Vadodara payments company, now manages a five-person analytics team. Her sessions focus on query performance and interview patterns.
Classroom & live online · Vadodara
Data Analytics Training Course with Gen AI in Vadodara
This 5–6 month Data Analytics with Gen AI program in Vadodara teaches you to handle messy data, write efficient queries, build insightful dashboards, and apply AI tools inside real analyst workflows.
Enrolment is open now at a special offer price of ₹17,500 + GST, down from the published fee of ₹50,000 + GST. Each batch is capped at 18 learners to ensure personalised trainer attention. You complete the course with 12 portfolio‑ready projects, 6 recorded mock interview sessions, and a resume tailored to actual Vadodara hiring needs across IT services, product startups, fintech, and e‑commerce firms.
- 18 learners per batch
- 12 graded projects
- 14 tools
- 180 hiring partners
- 6 mock interviews
Why Learn Data Analytics with Gen AI in Vadodara?
Data analysts in Vadodara earn 10–15% higher salaries than peers in Indore, Nagpur, or Coimbatore, and those who incorporate Gen AI into their workflow complete analytical tasks significantly faster.
The city runs on four major hiring engines, all driven by data. IT services companies in Gorwa and the Makarpura GIDC need analysts to generate client reports. Product startups near Alkapuri and Gotri monitor user behaviour on a weekly basis. Fintech teams track payment failures and fraud signals daily. E‑commerce companies constantly review inventory, returns, and delivery timelines.
Gen AI doesn't change the core job—it accelerates it. You still verify every number manually, but now ChatGPT can draft a SQL skeleton, Copilot can decode a broken formula, and Power BI Copilot can write a DAX measure in seconds. Local hiring managers actively ask candidates how they validate AI‑generated outputs. This course covers both the speed and the safety checks, so you can deliver faster without sacrificing accuracy in review meetings.
Course Fee & Duration
The programme fees stand at ₹17,500 + GST under a limited‑seat offer, reduced from ₹50,000 + GST, and the course spans 5–6 months.
An EMI facility starting at ₹2,999/month is available for those who prefer staggered payments. The offer is valid only for a fixed number of seats in each batch, not across the entire year. Your completion timeline depends on the track you select: weekday batches generally finish closer to 5 months, while weekend batches extend to roughly 6 months. Both tracks cover the identical syllabus, the same 12 projects, and the full placement support—nothing is watered down in the weekend option.
| Item | Amount |
|---|---|
| Listed fee | ₹50,000 + GST |
| Special offer fee (limited seats) | ₹17,500 + GST |
| EMI option | Starting ₹2,999/month |
| Course duration | 5–6 months |
Who Should Join This Course?
This course is for anyone aiming for an analytics role and able to commit 8–10 hours weekly to practice.
It's a strong fit for final‑year students, fresh graduates from any discipline, operations or support staff who already use Excel daily, testers and junior developers looking to pivot into data, and professionals returning after a career break. You'll need basic numeracy and comfort with a laptop. No previous coding experience is assumed—nearly half of every batch comprises non‑engineering graduates, so the teaching pace is calibrated for beginners, not for experienced developers.
Curriculums for Data Analytics Training Courses in Vadodara
Data Analytics Curriculum
The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.
120+
Hours of Content
75+
Live Sessions
10+
Tools and Software
Set the Basics Right
At Uncodemy, we offer a comprehensive Data Analytics training course in Vadodara designed to equip students with the skills and knowledge required to excel in the field of data analysis. Our curriculum is meticulously crafted to cover all essential aspects of data analytics, ensuring that our students gain a deep understanding and practical experience.
Here’s a detailed overview of what you can expect from our Data Analytics course curriculum:
1. Python for Data Analytics
- Need for Programming
- Advantages of Programming
- Overview of Python
- Organizations using Python
- Python Applications in Various Domains
- Python Installation
- Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Method of Accepting User Input and eval Function
- Python - Files Input/Output Functions
- Lists and Related Operations
- Tuples and Related Operations
- Strings and Related Operations
- Sets and Related Operations
- Dictionaries and Related Operations
- User-Defined Functions
- Concept of Return Statement
- Concept of name=” main ”
- Function Parameters
- Different Types of Arguments
- Global Variables
- Global Keyword
- Variable Scope and Returning Values
- Lambda Functions
- Various Built-In Functions
- Introduction to Object-Oriented Concepts
- Built-In Class Attributes
- Public, Protected and Private Attributes, and Methods
- Class Variable and Instance Variable
- Constructor and Destructor
- Decorator in Python
- Core Object-Oriented Principles
- Inheritance and Its Types
- Method Resolution Order
- Overloading
- Overriding
- Getter and Setter Methods
- Inheritance-In-Class Case Study
- Standard Libraries
- Packages and Import Statements
- Topics : Working with Modules and Handling Exceptions
- Reload Function
- Important Modules in Python
- Sys Module
- Os Module
- Math Module
- Date-Time Module
- Random Module
- JSON Module
- Regular Expression
- Exception Handling
- Basics of Data Analysis
- NumPy - Arrays
- Operations on Arrays
- Indexing Slicing and Iterating
- NumPy ArrayAttributes
- Matrix Product
- NumPy Functions
- Functions
- Array Manipulation
- File Handling Using NumPy
- Array Creation and Logic Functions
- File Handling Using Numpy
- Introduction to pandas
- Data structures in pandas
- Series
- Data Frames
- Importing and Exporting Files in Python
- Basic Functionalities of a Data Object
- Merging of Data Objects
- Concatenation of Data Objects
- Types of Joins on Data Objects
- Data Cleaning using pandas
- Exploring Datasets
2. Data Science Primer and Statistics
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Application of Data Science
- Introduction
- Stages of Analytics
- CRISP DM Data Life Cycle
- Data Types
- Introduction to EDA
- First Business Moment Decision
- Second Business Moment Decision
- Third Business Moment Decision
- Fourth Business Moment Decision
- Correlation
- What is Feature
- Feature Engineering
- Feature Engineering Process
- Benefit
- Feature Engineering Techniques
- Basics Of Probability
- Discrete Probability Distributions
- Continuous Probability Distributions
- Central Limit Theorem
- Concepts Of Hypothesis Testing - I: Null And Alternate Hypothesis, Making A Decision, And Critical Value Method
- Concepts Of Hypothesis Testing - II: P-Value Method And Types Of Errors
- Industry Demonstration Of Hypothesis Testing: Two-Sample Mean And Proportion Test, A/B Testing
3. Machine Learning
- Simple Linear Regression
- Simple Linear Regression In Python
- Multiple Linear Regression
- Multiple Linear Regression In Python
- Industry Relevance Of Linear Regression
- Univariate Logistic Regression
- Multivariate Logistic Regression: Model Building And Evaluation
- Logistic Regression: Industry Applications
- Data mining classifier technique
- Application of KNN classifier
- Lazy learner classifier
- Altering hyperparameter(k) for better accuracy
- Black box
- SVM hyperplane
- Max margin hyperplane
- Kernel tricks for non linear spaces
- Rule based classification method
- Different nodes for develop decision trees
- Discretization
- Entropy
- Greedy approach
- Information gain
4. SQL
- Introduction to Databases
- How to create a Database instance on Cloud?
- Provision a Cloud hosted Database instance.
- What is SQL?
- Thinking About Your Data
- Relational vs. Transactional Models ER Diagram
- CREATE Table Statement and DROP tables
- UPDATE and DELETE Statements
- Retrieving Data with a SELECT Statement
- Creating Temporary Tables
- Adding Comments to SQL
- Basics of Filtering with SQL
- Advanced Filtering: IN, OR, and NOT
- Using Wildcards in SQL
- Sorting with ORDER BY
- Math Operations
- Aggregate Functions
- Grouping Data with SQL
- Using Subqueries
- Subquery Best Practices and Considerations
- Joining Tables
- Cartesian (Cross) Joins
- Inner Joins
- Aliases and Self Joins
- Advanced Joins: Left, Right, and Full Outer Joins
- Unions
- Working with Text Strings
- Working with Date and Time Strings
- Date and Time Strings Examples
- Case Statements
- Views
- Data Governance and Profiling
- Using SQL for Data Science
- How to access databases using Python?
- Writing code using DB-API
- Connecting to a database using DB API
- Create Database Credentials
- Connecting to a database instance
- Creating tables, loading, inserting, data and querying data
- Analysing data with Python
5. Excel
- Input data & handling large spreadsheets
- Tricks to get your work done faster
- Automating data analysis (Excel VLOOKUP, IF Function, ROUND and more)
- Transforming messy data into shape
- Cleaning, Processing and Organizing large data
- Spreadsheet design principles
- Drop-down lists in Excel and adding data validation to the cells.
- Creating Charts & Interactive reports with Excel Pivot Tables, PivotCharts, Slicers and Timelines
- Functions like: - COUNTIFS, COUNT, SUMIFS, AVERAGE and many more.
- Excel features: - Sort, Filter, Search & Replace Go to Special etc...
- Importing and Transforming data (with Power Query)
- Customize the Microsoft Excel interface
- Formatting correctly for professional reports.
- Commenting on cells.
- Automate data entry with Autofill and Flash-fill.
- Writing Excel formulas & referencing to other workbooks / worksheets.
- Printing options
- Charts beyond column and bar charts: - Pareto chart, Histogram, Treemap, Sunburst charts & more
- Introduction to Excel for Data Analytics
- Data Cleaning in Excel
- Formulas and Functions
- Lookup Functions
- Conditional Functions
- Sorting and Filtering
- Pivot Tables
- Pivot Charts
- Data Visualization
- Excel Dashboards
- Advanced Excel Analytics
6. Tableau
- Introduction to Data Visualization
- Tableau Introduction and Tableau Architecture
- Exploring Data using Tableau
- Working with Data using Tableau including Data Extraction and Blending
- Various Charts in Tableau(Basics to Advanced)
- Sorting-Quick Sort, Sort from Axis, Legends, Axis, Sort by Fields
- Filtering- Dimension Filters, Measure Filters, Date Filters, Tableau Context Filters
- Groups , Sets and Combined Sets
- Reference Lines, Bands and Distribution
- Parameters, Dynamic Parameters and Actions
- Forecasting-Exponential Smoothening Techniques
- Clustering
- Calculated Fields in Tableau, Quick Tables
- Tableau Mapping Features
- Tableau Dashboards, Dashboards Action and Stories
- Introduction to Tableau
- Tableau Interface and Navigation
- Connecting Data Sources
- Data Preparation in Tableau
- Dimensions and Measures
- Charts and Visualizations
- Filters and Sorting
- Calculated Fields
- Dashboards
- Interactive Dashboards
- Tableau Stories
- Publishing and Sharing Dashboards
7. Power BI
- Introduction to Power BI – Need, Importance
- Power BI – Advantages and Scalable Options
- Power BI Data Source Library and DW Files
- Business Analyst Tools, MS Cloud Tools
- Power BI Installation
- Power BI Desktop – Instalation, Usage
- Sample Reports and Visualization Controls
- Understanding Desktop & Mobile Editions
- Report Rendering Options and End User Access
- Report Design with Databse Tables
- Report Visuals, Fields and UI Options
- Reports with Multiple Pages and Advantages
- Pages with Multiple Visualizations. Data Access
- “GET DATA” Options and Report Fields, Filters
- Report View Options: Full, Fit Page, Width Scale
- Report Design using Databases & Queries
8. Artificial Intelligence (AI) & Generative AI
- What is Artificial Intelligence?
- History and Evolution of AI
- Types of AI: Narrow AI, General AI, Super AI
- AI vs Machine Learning vs Deep Learning
- Real-world Applications of AI
- AI in Business, Healthcare, Finance, and Retail
- Ethical Considerations in AI
- What is Generative AI?
- How Generative AI Works: Overview of GANs, VAEs, and Transformers
- Popular Generative AI Models: GPT, DALL-E, Stable Diffusion
- Prompt Engineering: Basics and Best Practices
- Use Cases of Generative AI: Content Creation, Code Generation, Image Synthesis
- Ethical and Bias Issues in Generative AI
- Introduction to Generative AI and Large Language Models (LLMs)
- Prompt Engineering fundamentals for data tasks
- Using ChatGPT/Claude for Exploratory Data Analysis (EDA)
- AI-assisted data cleaning and preprocessing
- Writing and debugging SQL queries using AI tools
- AI-assisted Python scripting for data analysis
- Using Copilot in Microsoft Excel for automation
- Power BI Copilot for report and dashboard generation
- AI-generated data visualizations and insights summaries
- Automating repetitive analytics tasks with AI
- Ethical use of AI in data analytics (bias, accuracy, data privacy)
- Case Study: Using AI to speed up a real-world analytics project
- Introduction to NLP
- Tokenization, Stemming, Lemmatization
- Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
- Transformers and Attention Mechanism
- Large Language Models (LLMs): GPT, BERT, LLaMA
- Fine-tuning LLMs for Specific Tasks
- Building Chatbots and Conversational AI
- Neural Networks: Perceptron, Activation Functions
- Feedforward Neural Networks (FNN)
- Convolutional Neural Networks (CNN) for Image Data
- Recurrent Neural Networks (RNN) and LSTMs for Sequential Data
- Autoencoders and Variational Autoencoders (VAEs)
- Introduction to GANs (Generative Adversarial Networks)
- Using TensorFlow / Keras for Deep Learning
- Building AI Models using Python (Scikit-learn, TensorFlow, PyTorch)
- Model Evaluation and Hyperparameter Tuning
- Deploying AI Models using Flask / FastAPI
- Introduction to MLOps
- AI Model Monitoring and Maintenance
- Using Cloud AI Services: AWS SageMaker, Azure AI, Google AI
- Building End-to-End AI Applications
- AI Fairness, Accountability, and Transparency
- Bias Detection and Mitigation in AI Models
- Explainable AI (XAI)
- Data Privacy and Security in AI
- Emerging Trends: Multimodal AI, Self-supervised Learning
- AI and the Future of Work
- Career Opportunities in AI and Gen AI
9. R Programming for Data Analytics
- Introduction to R
- R Programming Basics
- Variables and Data Types
- Operators and Expressions
- Conditional Statements
- Loops and Functions
- Data Structures in R
- Vectors, Lists and Matrices
- Data Frames and Factors
- Data Import and Export
- Data Manipulation using R
- Data Cleaning using R
10. Data Visualization with ggplot2
- Introduction to Data Visualization
- Introduction to ggplot2
- Basic Plots using ggplot2
- Bar Charts
- Histograms
- Box Plots
- Scatter Plots
- Line Charts
- Customizing ggplot2 Visualizations
- Themes, Labels and Annotations
- Multiple Plots and Facets
11. Business Intelligence with Looker
- Introduction to Business Intelligence
- Introduction to Looker
- Looker Interface and Navigation
- Connecting Data Sources
- Data Exploration in Looker
- Creating Reports and Dashboards
- Filters and Dimensions
- Measures and Visualizations
- Creating Interactive Dashboards
- LookML Basics
- Sharing and Scheduling Reports
12. MATLAB for Data Analysis
- Introduction to MATLAB
- MATLAB Environment and Interface
- Variables and Data Types
- Arrays and Matrices
- Operators and Expressions
- Conditional Statements and Loops
- Functions in MATLAB
- Data Import and Export
- Data Manipulation using MATLAB
- Data Visualization using MATLAB
- Statistical Analysis using MATLAB
Uncodemy's Data Analytics training course in Vadodara not only covers these fundamental topics but also includes hands-on experience with real-world data, ensuring that students are well-prepared for the demands of the industry. Our curriculum is designed to provide a balanced mix of theoretical knowledge and practical skills, making it one of the best data analytics courses available.
By choosing Uncodemy, you are opting for a program that is tailored to meet the needs of aspiring data analysts, offering a robust education in a field that is both dynamic and in high demand. Whether you are looking for offline or online data analytics courses in Vadodara, our training programs are structured to provide the best learning experience possible.
Tools & Technologies Covered
You work hands-on with 14 tools across the course:
- Languages: Python (pandas, NumPy, Matplotlib, Seaborn), SQL
- Databases: MySQL, PostgreSQL
- Reporting: Advanced Excel, Power Query, Tableau, Power BI with DAX
- Gen AI: ChatGPT, GitHub Copilot, Power BI Copilot, Claude for document analysis
- Workflow: Jupyter Notebook, Git and GitHub for version control
Every tool appears in at least one graded project, so nothing stays theory-only.
Trainers & Mentors
Four practising analysts deliver this programme, and every one of them continues to hold a full‑time industry position alongside teaching.
Swipe to see all four trainers →
Python & Statistics
Aftab Qureshi
Moved from mechanical engineering into analytics himself, which shapes how he explains code to non-programmers. He has trained more than 600 learners.
Tableau & Power BI
Sneha Balakrishnan
Built reporting systems for two e-commerce firms. She handles dashboard design and reviews every learner dashboard line by line.
Gen AI & Mock Interviews
Vivek Rathi
Works on internal AI tooling at a product startup and tests every prompt workflow himself before it reaches the classroom, so nothing taught here is untested theory.
Placement Assistance & Hiring Partners
Placement support kicks off in the third month of the programme, not after the final project submission.
We work with 180 hiring partners across Vadodara—IT services firms, product startups in Gotri and Alkapuri, fintech players, e‑commerce sellers, and analytics consultancies.
1. Resume rebuild
One page, project-led, matched to the exact keywords in analyst job posts.
2. Portfolio cleanup
LinkedIn and GitHub tidied so recruiters open your dashboards and query code in one click.
3. Six mock interviews
Two SQL rounds, one Python round, one dashboard walkthrough, one case study, one HR round — all recorded, all reviewed with written feedback.
4. Interview scheduling
Relevant openings by email and WhatsApp, with a referral where a partner allows it.
5. Salary negotiation
Coaching before you sign an offer, so you know what your band is worth.
12 months of cover
Support continues for 12 months after your final project. You can also rejoin any live batch once at no extra cost, which helps when you want to revise a module a week before an interview round.
Data Analyst Salary in Vadodara (2026)
In 2026, a fresh data analyst in Vadodara starts at ₹4.5–7 lakh per annum, which is 10–15% higher than comparable Tier‑2 city roles.
Earnings climb quickly with demonstrated output. Analysts who have 2‑4 years of experience and a solid dashboard portfolio typically reach ₹8‑14 lakh, while senior analysts who own reporting for an entire business unit can cross ₹18 lakh.
Sector plays a major role. Fintech and product startups pay top‑of‑band but demand strong SQL and quicker turnaround. IT services firms sit in the middle with more predictable hours and client exposure. E‑commerce falls between the two, with heavier weekend reporting cycles. Candidates who can demonstrate a verified Gen AI workflow in interviews often receive offers at the upper end of their salary band. Location within the city matters less, though roles in Gorwa and Makarpura GIDC sometimes include shift or on‑call allowances.
| Experience level | Annual salary range |
|---|---|
| Fresher (0–1 year) | ₹4.5–7 lakh |
| Junior analyst (1–2 years) | ₹6.5–9.5 lakh |
| Mid-level analyst (2–4 years) | ₹8–14 lakh |
| Senior analyst (5+ years) | ₹15–24 lakh |
| Lead / analytics manager (8+ years) | ₹26–38 lakh |
Certification Details
You receive an industry‑recognised course completion certificate with a unique verification ID that any recruiter can validate online.
The certificate is awarded after two conditions are fulfilled: 75% attendance and successful submission of all 12 graded projects. It clearly lists the modules you've covered, the tools you've used, and your final project title, making it a credible proof of competence rather than a generic document.
You also receive guided preparation for two external certifications—the Microsoft Power BI Data Analyst exam and the Tableau Desktop Specialist exam—with practice question banks and a dedicated revision week. Exam fees are paid directly to the certifying bodies.
Data Analytics Job Trends in Vadodara
Vadodara sees a high volume of analytics job postings compared to other Indian cities, and the role itself has evolved noticeably over the last two years.
Three key shifts are worth noting for anyone entering the field now.
Gen AI is becoming baseline
Job descriptions now frequently mention prompt‑based querying or Copilot usage, and interviewers ask how you validate AI‑generated SQL. Nobody hires an analyst who blindly pastes AI output into a report.
The toolset is consolidating
Power BI has gained significant ground in IT services and mid‑sized firms, while Tableau remains dominant in product companies. SQL continues to be the non‑negotiable screening skill in almost every first round.
Proof trumps qualifications
Take‑home assignments and live dashboard walkthroughs are increasingly common, favouring candidates with a public portfolio over those with just a certificate.
Demand is distributed across multiple sectors too. Fintech hires for risk and payment analytics, e‑commerce for supply chain and pricing, healthtech and edtech for user behaviour, and global capability centres for reporting teams serving overseas units. This sectoral spread is why analyst openings in Vadodara remain resilient even when one industry faces a slowdown.
How This Course Is Different (USP)
This course stands apart from pre‑recorded libraries in four key ways.
Batch size is capped at 18 learners, so trainers review your code, not just your attendance. Gen AI is a full module with a verification workflow, not a single bonus session bolted onto the end. All 12 projects use Vadodara datasets, which gives you local context to discuss in interviews.
And the doubt-clearing runs daily, not weekly: a one-hour open session every evening where you can bring a broken query or a failing script and leave with it working. You also keep lifetime access to session recordings and one free batch repeat, so revising a module before an interview costs you nothing extra.
Student Reviews / Success Stories
“I had six years in operations and no coding. The SQL problem sets were hard for the first month, then they clicked. My delivery dashboard project came up in both interview rounds. I joined a logistics team in Gorwa in month seven.”
“I applied for 40 analyst roles before this course and got two replies. The difference was the portfolio. Recruiters opened my GitHub. The Gen AI module also helped in interviews — I could explain how I check a generated query instead of just saying I use ChatGPT.”
“I was away from work for four years, so the weekend batch mattered — it fitted around school hours. The recorded mock interviews were uncomfortable to watch, and that is exactly why they helped. I fixed the same rambling answer three times before it sounded clear.”
Frequently Asked Questions
What is the fee for this course?
The standard fee is ₹50,000 + GST. However, a limited‑time offer brings it down to ₹17,500 + GST for a restricted number of seats per cohort. An EMI plan starting at ₹2,999/month is also available. Placement support, project reviews, mock interviews, and the final certificate are all included—no hidden charges.
How long is the course?
The programme lasts 5–6 months. Weekday batches typically wrap up around 5 months, while weekend batches extend to 6 months. Both tracks share the identical 12 projects and six core modules. You also get 12 months of placement assistance after submitting your final project.
Do I need coding experience to join?
No. We start from absolute basics in Python and assume zero prior programming knowledge. Around half of every batch comes from non‑engineering backgrounds (commerce, arts, science). You just need basic maths skills and the ability to dedicate 8–10 hours per week to practice.
Is this classroom or online training?
Both modes are available. You can attend in‑person at our Vadodara centre or join the same live sessions remotely. All sessions are recorded, and you retain lifetime access. Online learners receive the same project reviews, mock interviews, and placement support as classroom students.
What is the batch size?
We cap each batch at 18 learners. This ensures trainers can review individual code and dashboards during class, not just respond to chat queries. The limit applies to weekday, weekend, and online batches alike.
Will I get a job guarantee?
No, and any institute making that claim should be viewed with caution. We do offer 180 hiring partner connections, referrals where permitted, 6 recorded mock interviews, resume and portfolio support, and 12 months of interview scheduling assistance. Your success ultimately depends on your own practice and interview performance.
How many projects will I build?
You complete 12 graded projects (one or two per module) plus a final capstone. Datasets are drawn from Vadodara‑based scenarios—cab trips, food delivery orders, UPI transactions, and retail sales. All 12 projects are uploaded to a public GitHub portfolio that recruiters can review during screening.
Which Gen AI tools does the course cover?
You'll work with ChatGPT, GitHub Copilot, Power BI Copilot, and Claude for document analysis. The emphasis is on a verification workflow: prompt, cross‑check the output against known results, then correct. You'll also cover data privacy protocols to ensure client data never enters a public AI tool.
Can I pay in instalments?
Yes. An EMI option starting at ₹2,999/month is available through partner finance providers. You can also split the offer fee (₹17,500 + GST) across mutually agreed milestones. Our counsellors explain all options, including any processing charges, before you commit.
What certificate do I receive?
You receive a course completion certificate with a unique online verification ID, issued after 75% attendance and successful submission of all 12 projects. It lists your modules, tools, and capstone title. The course also prepares you for the Power BI Data Analyst and Tableau Desktop Specialist exams.
What if I miss a class or fall behind?
Every session is recorded and posted the same day. A daily one‑hour doubt‑clearing session covers anything you missed. If you fall significantly behind, you can repeat one module with a later batch, or repeat the entire batch once, at no additional fee.
Final CTA Section
Seats at the offer fee of ₹17,500 + GST, down from ₹50,000 + GST, are limited for each batch.
The EMI option starts at ₹2,999/month, and the course runs 5–6 months. Book a free demo class, sit through one live session, and check the trainers and batch size yourself before you pay anything.
