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Data Analytics & Data Science with GenAI.

Nine-month program covering Excel, SQL, Power BI, Tableau, Python, Machine Learning, Deep Learning, NLP, Generative AI, and AI-powered BI.

Learner rating

4.9/5

Course duration

9 Months

Learning modules

9 modules

Learning mode

Offline

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Program overview

Data Analytics & Data Science with GenAI program overview

Data Analytics & Data Science with GenAI is a 9 month industry focused program covering Excel, SQL, Power BI, Tableau, Python, statistics, Machine Learning, Deep Learning, NLP, Generative AI, AI agents, and AI-powered BI. Through monthly projects, guided assignments, and a final capstone, learners develop practical analytics and Data Science skills while building a strong portfolio and preparing for interviews and placement opportunities.

Hands-on practice with Excel, SQL, Power BI, Tableau, Python, Machine Learning, Deep Learning, NLP, and Generative AI

Nine monthly industry projects plus a complete analytics, Data Science, and GenAI capstone

End-to-end path from Business Intelligence dashboards to AI agents and AI-powered BI

Resume building, mock interviews, interview preparation, and job placement support

A structured learning journey

Data Analytics & Data Science with GenAI Curriculum

Explore the curriculum, build your understanding one module at a time, and put each new skill into practice.

01Month - 01 | Business Intelligence & Advanced ExcelMonth 1 · Weeks 1–4 · 12 topics
  • Business Intelligence fundamentals
  • Data, information, insights, and decision-making
  • Data Analyst, Business Analyst, and BI Analyst roles
  • Descriptive, diagnostic, predictive, and prescriptive analytics
  • Business KPIs and performance metrics
  • Excel formulas: VLOOKUP, XLOOKUP, INDEX-MATCH, IFERROR
  • Dynamic arrays: FILTER, UNIQUE, and SORT
  • Data cleaning and validation
  • PivotTables, charts, slicers, and timelines
  • Power Query fundamentals
  • MIS dashboard design and storytelling
  • Monthly project: Sales MIS Dashboard
02Month - 02 | SQL for Business IntelligenceMonth 2 · Weeks 5–8 · 13 topics
  • Relational database concepts
  • Tables, keys, and relationships
  • Fact and dimension tables
  • Database and table creation
  • SELECT, WHERE, ORDER BY, and DISTINCT
  • CASE statements and SQL functions
  • INSERT, UPDATE, DELETE, and DDL commands
  • Aggregations and GROUP BY
  • Joins and multi-table queries
  • Subqueries and Common Table Expressions
  • Window functions
  • Views and stored procedures
  • Monthly project: Retail Database Analytics
03Month - 03 | Power BI and Tableau PublicMonth 3 · Weeks 9–12 · 13 topics
  • Power BI interface and data connections
  • Power Query transformations
  • Data modelling and star schema
  • DAX measures and calculated columns
  • Time-intelligence calculations
  • KPI creation and report design
  • Interactive visuals, filters, and bookmarks
  • Power BI Service publishing
  • Row-Level Security and scheduled refresh
  • Tableau interface and data connections
  • Tableau calculated fields and parameters
  • Dashboards, stories, and publishing
  • Monthly project: Executive Power BI & Tableau Dashboard
04Month - 04 | Python for Data Analytics and Data ScienceMonth 4 · Weeks 13–16 · 15 topics
  • Python variables, data types, and operators
  • Conditional statements and loops
  • Functions, lambda functions, and error handling
  • Object-oriented programming fundamentals
  • NumPy arrays and operations
  • Pandas DataFrames
  • Data cleaning and transformation
  • Data merging, grouping, and aggregation
  • Exploratory Data Analysis
  • Descriptive statistics and probability
  • Hypothesis testing
  • Matplotlib, Seaborn, and Plotly
  • Python integration with Power BI
  • REST API data consumption
  • Monthly project: Customer Analytics & Exploratory Data Analysis
05Month - 05 | Machine LearningMonth 5 · Weeks 17–20 · 16 topics
  • Machine-learning concepts and workflows
  • Supervised and unsupervised learning
  • Data preprocessing
  • Feature scaling and encoding
  • Train-test splitting
  • Linear, polynomial, Ridge, and Lasso regression
  • Logistic regression, KNN, and Naive Bayes
  • Decision trees
  • Random Forest
  • AdaBoost, Gradient Boosting, XGBoost, and LightGBM
  • K-Means, Hierarchical Clustering, and DBSCAN
  • Principal Component Analysis
  • Model evaluation and cross-validation
  • Hyperparameter tuning
  • Basic model deployment using Flask or Streamlit
  • Monthly project: Customer Segmentation & Churn Prediction
06Month - 06 | Deep LearningMonth 6 · Weeks 21–24 · 15 topics
  • Neural-network fundamentals
  • Perceptrons and activation functions
  • Forward propagation and backpropagation
  • Loss functions and optimizers
  • TensorFlow and Keras
  • Building and training neural networks
  • Regularization and model tuning
  • Convolutional Neural Networks
  • Image classification
  • Recurrent Neural Networks
  • LSTM and GRU
  • Time-series forecasting
  • Transfer learning using ResNet and MobileNet
  • Fine-tuning pretrained models
  • Monthly project: Image Classification or Time-Series Forecasting
07Month - 07 | NLP and Generative AI FoundationsMonth 7 · Weeks 25–28 · 16 topics
  • Natural Language Processing fundamentals
  • Tokenization, stemming, and lemmatization
  • Stop-word removal
  • Bag of Words and TF-IDF
  • Word2Vec and GloVe embeddings
  • Sentiment analysis
  • Text classification using RNN and LSTM
  • Attention mechanisms
  • Transformer architecture
  • Generative AI and Large Language Models
  • Overview of GPT, Gemini, Claude, and BERT
  • Tokens, context windows, and temperature
  • AI ethics, bias, hallucination, and privacy
  • Zero-shot, one-shot, and few-shot prompting
  • Role prompting and reusable prompt templates
  • Monthly project: AI-Powered NLP Application
08Month - 08 | Advanced Generative AI, AI Agents and AI-Powered BIMonth 8 · Weeks 29–32 · 17 topics
  • OpenAI API integration using Python
  • Structured output and JSON parsing
  • LangChain fundamentals
  • Chains, prompts, and memory
  • Retrieval-Augmented Generation
  • Document question-answering systems
  • AI agent fundamentals
  • Task decomposition and multi-step workflows
  • Analytics chatbot development
  • Natural-language-to-SQL
  • AI-generated SQL queries
  • Copilot features in Power BI
  • AI-assisted Excel formulas
  • AI-generated executive summaries
  • AI-powered dashboard narratives
  • Industry use cases in retail, finance, HR, healthcare, and marketing
  • Monthly project: AI Business Assistant & AI-Powered BI
09Month - 09 | Integrated Capstone and Career PreparationMonth 9 · Weeks 33–36 · 16 topics
  • Selection of a real-world business problem
  • Problem framing and requirement analysis
  • Solution architecture and data sourcing
  • SQL data extraction
  • Python-based cleaning and exploratory analysis
  • Machine Learning or Deep Learning model development
  • NLP and Generative AI insight layer
  • AI chatbot, summarization, or insight generation
  • Power BI or Tableau dashboard
  • AI-generated executive summary
  • Integration and solution testing
  • GitHub and portfolio preparation
  • Resume and LinkedIn optimization
  • Technical and HR mock interviews
  • Professional project presentation
  • Final project: Full Data Analytics, Data Science & GenAI Capstone

Find out how this curriculum fits your career goals.

Tools & technologies

Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.

Excel
SQL Server
Power BI
Tableau
Python
Pandas
NumPy
Plotly
ChatGPT
OpenAI API
Gemini
Claude
Microsoft Copilot
LangChain
RAG
AI Agents
GitHub

Build practical skills with industry-relevant learning, expert guidance, and hands-on projects designed for modern teams.

Get the complete curriculum, tools, projects, and career support details in one brochure.

Build something that matters

6 Industry Projects

Every student completes 6 real-world projects — one per month — building a professional portfolio demonstrated during placement interviews.

Project 01

Sales MIS Dashboard

Purpose

Build an interactive Excel dashboard that analyses sales data using KPIs, PivotTables, charts, slicers, timelines, and Power Query.

Business Output

A management-ready MIS dashboard that provides a clear view of revenue, sales growth, regional performance, product performance, and overall business trends.

Project 02

Retail Database Analytics

Purpose

Design a relational retail database and use SQL queries, joins, subqueries, aggregations, window functions, views, and stored procedures to analyse business data.

Business Output

A reusable SQL reporting system that converts transactional retail data into accurate insights about sales, customers, products, stores, and inventory.

Project 03

Executive Power BI & Tableau Dashboard

Purpose

Combine SQL and Excel data, transform it with Power Query, build a data model, create DAX measures, and develop interactive dashboards in Power BI and Tableau.

Business Output

A secure executive dashboard that helps management monitor critical KPIs, compare performance, identify trends, and make informed business decisions.

Project 04

Customer Analytics & Exploratory Data Analysis

Purpose

Clean and analyse customer or e-commerce data using Python, Pandas, NumPy, statistics, Matplotlib, Seaborn, and Plotly.

Business Output

A comprehensive customer-insight report that reveals purchasing patterns, customer behaviour, product trends, correlations, and business opportunities.

Project 05

Customer Segmentation & Churn Prediction

Purpose

Segment customers based on their behaviour and develop Machine Learning models that predict which customers are likely to stop using the company’s products or services.

Business Output

A customer-retention solution that identifies high-risk customers, creates actionable customer segments, and supports targeted marketing and retention campaigns.

Project 06

Image Classification or Time-Series Forecasting

Purpose

Build a Deep Learning model to classify images using CNNs or forecast future sales and demand using LSTM-based time-series analysis.

Business Output

An intelligent prediction solution that automates image classification or improves sales forecasting, demand planning, inventory management, and resource allocation.

Project 07

AI-Powered NLP Application

Purpose

Develop an application that performs text preprocessing, sentiment analysis, summarization, document question-answering, or chatbot functionality using NLP and Generative AI.

Business Output

An AI-powered text intelligence solution that analyses customer feedback, summarizes large documents, automates information retrieval, and improves customer-service decisions.

Project 08

AI Business Assistant & AI-Powered BI

Purpose

Build an AI business assistant using an LLM API, LangChain, RAG, AI agents, Natural-Language-to-SQL, and AI-generated dashboard narratives.

Business Output

An intelligent business assistant that allows users to ask questions in natural language, retrieves information, generates SQL queries, prepares reports, and produces executive summaries.

Project 09

Full Data Analytics, Data Science & GenAI Capstone

Purpose

Develop an end-to-end solution that integrates SQL, Python, EDA, Machine Learning or Deep Learning, NLP, Generative AI, and Power BI or Tableau.

Business Output

A complete AI-powered decision-support platform that converts raw business data into dashboards, predictions, recommendations, conversational insights, and executive summaries.

Portfolio tip: document the problem, your approach, and the result so you can walk an interviewer through your decisions.

Learning process from career advice and enrolment through practical sessions, projects, interview preparation, and placement

Who should enroll

  • Beginners who want analytics and data science together
  • MBA, commerce, and management graduates targeting analyst or data science roles
  • Working professionals moving into data, BI, or AI-enabled roles
  • Excel users adding SQL, Power BI, Python, Machine Learning, Deep Learning, and GenAI
  • Students preparing for Data Analyst, BI Analyst, and Data Scientist careers

Learning modes

  • Classroom training at Thane center
  • Live online interactive sessions
  • Recorded sessions for missed classes
  • Free demo class before enrollment

Prerequisites

  • Basic computer familiarity
  • Analytical mindset and willingness to practice
  • No coding experience required

95% of our students successfully get placed after completing our programs.

Learn more about how we have been impacting thousands of careers.

  • Build BI dashboards and MIS reports with Excel, Power Query, Power BI, and Tableau
  • Query and model business data with SQL, including joins, CTEs, and window functions
  • Analyse data with Python, Pandas, statistics, and exploratory data analysis
  • Build, evaluate, and deploy Machine Learning and Deep Learning models
  • Apply NLP, Generative AI, RAG, and AI agents to business intelligence workflows
  • Present a complete analytics, Data Science, and GenAI capstone and prepare for interviews

Recognition that travels with you

Globally recognized certifications

Data Analytics & Data Science Certificate

Certification pathway

Stand out with proof of practical skills.

Build an analytics and Data Science portfolio with nine monthly projects, a complete capstone, resume and LinkedIn preparation, technical and HR mock interviews, and certification guidance from experienced mentors.

Data Analytics & Data Science Certificate certificate

Beyond the classroom

Career Assistance Program

Soft skill session

Resume building

Aptitude training

LinkedIn profile building

Mock interview

Job assistance

Placement Assistance

Dedicated placement support with industry connections and recruiter interactions.

Dedicated placement cell with job referrals
Resume & LinkedIn profile optimization
Mock interviews with industry experts
Interview call scheduling support
Career counseling until you get placed
Career and placement support for Data Analytics learners
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Access your complete learning experience through our all-in-one learning portal.

SkillUpAll-in-one
01Live Mentor Sessions
02AI-Powered Practice
03Progress Tracking
04Career support
One learning hub for your complete career journey

Your next opportunity starts here

Limited seats. High demand. Enroll today.

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Get industry-ready with Wraystech Academy

  • Enroll for one course and get access to the complete learning journey
  • In-depth curriculum with live mentor support
  • Integrated projects and industry-recognized certifications
  • Practice on real-world cases and career-ready assignments
  • Dedicated placement guidance and interview preparation
Career growth through practical learning

CAREER OUTCOME

Offer letter preview

Congratulations!

Your practical skills, projects, and career preparation can help you approach interviews with clarity and confidence.

Role-ready preparation

Portfolio, interview practice, and mentor feedback.

Career counsellor

READY

A little more clarity

Frequently asked questions

Answers to common questions about learning, projects, and getting started.

What is the Data Analytics & Data Science with GenAI program?

It is a 9 month industry-focused program covering Excel, SQL, Power BI, Tableau, Python, statistics, Machine Learning, Deep Learning, NLP, Generative AI, AI agents, and AI-powered BI through hands-on learning and monthly projects.

How long is the program?

The program duration is 9 months, including guided learning, practical assignments, monthly projects, a final capstone, and career preparation.

Who can join this program?

The program is suitable for students, graduates, working professionals, and beginners who want to build a career in Data Analytics, Business Intelligence, and Data Science.

Do I need prior experience in Data Analytics or Data Science?

No. The program starts with Excel and analytics fundamentals, then builds into Python, Machine Learning, Deep Learning, NLP, and Generative AI.

What tools and technologies will I learn?

You will learn Excel, SQL, Power BI, Tableau, Python, statistics, Machine Learning, Deep Learning, NLP, and Generative AI tools such as ChatGPT, OpenAI API, LangChain, RAG, and AI agents.

Is the program hands-on?

Yes. The program focuses on practical exercises, assignments, monthly projects, and real-world business scenarios to strengthen your analytics, Data Science, and AI skills.

Will I work on real-world projects?

Yes. You will complete a project each month and a final capstone covering Data Analytics, Data Science, and Generative AI, so you can demonstrate your skills through a professional portfolio.

Will I receive a certificate after completing the program?

Yes. Upon meeting the program requirements, you will receive a RaysTech Academy certificate.

Is an internship included in the program?

If included in your selected program, you will receive an opportunity to gain practical internship experience along with an internship certificate.

Will I get interview preparation?

Yes. The program includes interview preparation, mock interviews, and guidance to help you prepare for Data Analytics and Data Science roles.

Do you provide placement assistance?

Yes. We provide career guidance, resume support, interview preparation, and assistance with relevant job opportunities.

Will I get help with my resume and LinkedIn profile?

Yes. You will receive guidance on resume building and LinkedIn profile optimization to improve your professional profile.

What career opportunities can I pursue after the program?

You can explore roles such as Data Analyst, BI Analyst, Data Scientist, Machine Learning Associate, AI Data Analyst, and Generative AI Associate, depending on your skills and experience.

How are the sessions conducted?

The program includes guided learning and mentor support, with practical sessions designed to help you understand and apply the concepts.

How can I enroll in the program?

You can contact RaysTech Academy for enrollment details, batch schedules, fees, and the next available start date.

Your next chapter starts with a conversation.

Discuss your background, explore the course, and get help choosing a learning plan that fits your goals.

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