SQL & Databases
Rukmini Deshpande
Nine years in reporting roles at a Coimbatore payments company, now manages a five-person analytics team. Her sessions focus on query performance and interview patterns.
Classroom & live online · Coimbatore
Data Analytics Training Course with Gen AI in Coimbatore
The Data Analytics Training Course with Gen AI in Coimbatore is a 5–6 month program that teaches you to clean data, write queries, build dashboards, and apply AI tools inside real analyst work.
Seats are open now at an offer fee of ₹17,500 + GST, reduced from the listed fee of ₹50,000 + GST. Classes run in batches of 18 learners, so every person gets trainer time. You finish the course with 12 portfolio projects, 6 recorded mock interviews, and a resume mapped to real Coimbatore hiring roles 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 Coimbatore?
Coimbatore pays data analysts 10–15% more than Tier-2 cities such as Indore, Jaipur, or Coimbatore, and analysts who use Gen AI tools clear tasks faster than those who do not.
The city holds four hiring pools that all run on data. IT services firms in Whitefield and Electronic City need analysts for client reporting. Product startups near Koramangala and HSR Layout track user funnels every week. Fintech teams review payment failures and fraud flags daily. E-commerce companies watch stock levels, returns, and delivery delays.
Gen AI changes the daily job, not the fundamentals. Analysts still verify numbers by hand. What changes is speed: ChatGPT drafts a SQL query, Copilot explains a broken formula, and Power BI Copilot writes a DAX measure in seconds. Hiring managers here now ask candidates how they check AI output for errors. This course trains both halves, so you can move fast and still defend your numbers in a review meeting.
Course Fee & Duration
The course fee is ₹17,500 + GST as a limited-seat offer, down from the listed fee of ₹50,000 + GST, and the course runs 5–6 months.
An EMI option starts at ₹2,999/month for learners who prefer to pay in parts. The offer applies to a fixed number of seats per batch, not to every batch of the year. Duration depends on the track you pick: weekday batches finish nearer 5 months, and weekend batches run closer to 6 months. Both tracks cover the same syllabus, the same 12 projects, and the same placement support. Nothing is trimmed from 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?
Join this course if you want an analyst job and can give 8–10 hours a week.
It fits final-year students, freshers from any degree stream, support and operations staff who already work in Excel, testers or developers moving into data roles, and career-break returners. You need basic maths and comfort with a laptop. You do not need a coding background. Non-engineering graduates make up close to half of every batch, so the pace is set for beginners, not for developers.
Curriculums for Data Analytics Training Courses in Coimbatore
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
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
Info@uncodemy.com | +91-9818366550 | www.uncodemy.com
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
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
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
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
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
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
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
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
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
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
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
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
At Uncodemy, we offer a comprehensive Data Analytics training course in Coimbatore 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
2. Data Science Primer and Statistics
3. Machine Learning
4. SQL
5. Excel
6. Tableau
7. Power BI
8. Artificial Intelligence (AI) & Generative AI
9. R Programming for Data Analytics
10. Data Visualization with ggplot2
11. Business Intelligence with Looker
12. MATLAB for Data Analysis
Uncodemy's Data Analytics training course in Coimbatore 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 Coimbatore, 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 working analysts teach this course, and each one still holds a full-time industry job.
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 starts in month three, not after the course ends.
Our team works with 180 hiring partners across Coimbatore, including IT services companies, product startups in Koramangala and HSR Layout, fintech firms, 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 Coimbatore (2026)
A fresher data analyst in Coimbatore earns ₹4.5–7 lakh per year in 2026, which sits 10–15% above pay for the same role in Tier-2 cities.
Pay rises fast with proof of work. Analysts with two to four years of experience and a dashboard portfolio typically reach ₹8–14 lakh, and senior analysts who own reporting for a business unit cross ₹18 lakh.
Sector matters as much as experience. Fintech and product startups pay at the top of each band but expect strong SQL and faster delivery. IT services firms pay in the middle and offer steadier hours plus client exposure. E-commerce sits between the two, with heavier weekend reporting cycles. Candidates who can show a verified Gen AI workflow in interviews report offers near the upper end of their band. Location within the city matters less than sector, though roles in Whitefield and Electronic City often include a shift or on-call allowance.
| 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 check online.
The certificate is issued after two conditions are met: 75% attendance and submission of all 12 graded projects. It lists the modules covered, the tools used, and your final project title, so it reads as evidence rather than a name on paper.
You also get guided preparation for two external exams — the Microsoft Power BI Data Analyst certification and the Tableau Desktop Specialist — with practice question sets and a revision week. External exam fees are paid directly to the exam body.
Data Analytics Job Trends in Coimbatore
Coimbatore posts more analytics openings than any other Indian city, and the job description itself has shifted in the last two years.
Three trends matter for anyone joining now.
Gen AI is becoming baseline
Job posts increasingly name prompt-based querying or Copilot use, and interviewers ask how you validate AI-written SQL. Nobody hires an analyst who pastes unchecked output into a report.
The tool list is consolidating
Power BI has gained ground in IT services and mid-size firms, while Tableau holds strong in product companies. SQL remains the non-negotiable screening skill in almost every first round.
Proof beats pedigree
Take-home tasks and live dashboard reviews are now common, which favours candidates with a public portfolio over candidates with only a certificate.
Demand is spread across 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 that serve overseas business units. That spread is why analyst openings here hold steady even when one sector slows down.
How This Course Is Different (USP)
Four things separate this course from a recorded video library.
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 Coimbatore 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 Whitefield 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 listed fee is ₹50,000 + GST. The current special offer fee is ₹17,500 + GST for a limited number of seats per batch. An EMI option starts at ₹2,999/month. There is no separate charge for placement support, project reviews, mock interviews, or your course completion certificate.
How long is the course?
The course runs 5–6 months. Weekday batches usually finish nearer 5 months, and weekend batches take closer to 6 months. Both cover the same six modules and the same 12 projects. Placement support continues for 12 months after you submit your final project.
Do I need coding experience to join?
No. The course starts with plain Python basics and assumes zero programming background. Close to half of each batch comes from non-engineering degrees, including commerce, arts, and science streams. You do need basic maths and regular practice time of 8–10 hours a week.
Is this classroom or online training?
Both options run. You can attend at the Coimbatore centre or join the same live batch online. Sessions are recorded either way, and you keep lifetime access. Online learners get the same project reviews, the same 6 mock interviews, and the same placement support.
What is the batch size?
Each batch is capped at 18 learners. The cap exists so trainers can review individual code and dashboards during class rather than only answering questions in a chat window. Weekday, weekend, and online batches all follow the same cap.
Will I get a job guarantee?
No, and any institute promising one is worth questioning. We provide 180 hiring partner connections, referrals where partners allow them, 6 recorded mock interviews, resume and portfolio work, and 12 months of interview scheduling support. Your results depend on practice and interview performance.
How many projects will I build?
You build 12 graded projects, one or two per module, plus a final capstone. Datasets include Coimbatore cab trips, food delivery orders, UPI payments, and retail sales. All 12 go into a public GitHub portfolio that recruiters can open during screening.
Which Gen AI tools does the course cover?
You work with ChatGPT, GitHub Copilot, Power BI Copilot, and Claude for document analysis. The focus is a verification workflow: prompt, test the output against known results, then fix. You also cover data privacy rules so client data never enters a public AI tool.
Can I pay in instalments?
Yes. An EMI option starts at ₹2,999/month through partner finance providers. You can also split the offer fee of ₹17,500 + GST across agreed milestones. Our counsellors explain both paths in full, including any processing charge, before you enrol.
What certificate do I receive?
You receive a course completion certificate with an online verification ID, issued after 75% attendance and all 12 project submissions. 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 session covers anything you missed. If you fall well behind, you can repeat one module with a later live batch, or repeat the full 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.