Nasscom Advanced DA Program with Gen AI

Immersive Learning Program

In today’s rapidly evolving digital landscape, data has become the new fuel driving innovation across industries. To harness its true power, the Nasscom Advanced Data Analytics Program with Generative AI is a comprehensive 190-hour training initiative by Emerging India Analytics that blends the power of traditional analytics with modern AI innovations. Designed for aspiring data professionals, this program covers core areas like Python programming, Statistics, Machine Learning, NLP, and Deep Learning, while also integrating practical training in SQL, Tableau, and Power BI for effective Data Analysis and Visualization. Learners will also explore cutting-edge domains such as Generative AI, Transformers, and LangChain—bridging the gap between Business Intelligence and Artificial Intelligence.

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Introduction

Nasscom Data Analytics Program with GEN AI

This 190-hours NASSCOM Advanced Data Analytics Program with Gen AI offers a structured curriculum aimed at building strong Analytical and AI-driven capabilities. It begins with core technical foundations, including SQL and Python, to ensure participants are well-versed in essential Data Handling skills. As the program progresses, learners explore Data Visualization tools such as Tableau and Power BI, followed by advanced topics in Machine Learning, Deep Learning, NLP, and Gen AI. This progression equips participants to interpret complex data and build intelligent systems.
Participants also gain practical experience in developing AI-powered applications using Transformer models, Hugging Face, and LangChain. The program emphasizes hands-on learning through real-world case studies and capstone projects, preparing individuals to meet real industry demands with confidence and skill.

Data Analytics Program with GEN AI

Tools

Python
NumPy
pandas
Matplotlib
seaborn
SQL
tableau
PowerBI
statistics
Scikit-learn
TensorFlow
Keras
Computer Vision
NLP
LLMs
BERT
transformers
midjourney
pytorch
OpenAI
Dalle2
Langchain

Program Structure

60-Hours Pre-Learning Module

Before you embark on the live academic session, get ready for the Program. You will get a series of online recorded tutorials to understand the structure of Data Science, to know about the fundamentals, which would enrich your future learning experience.

190-Hours Live Instructor-Led Training

You will get an execution-based learning experience on Python, Statistics, ML, DL, SQL, Tableau, PowerBI, CV, NLP, Reinforcement Learning, TensorFlow, Keras, NLTK, OpenCV, BERT, Hugging Face Transformers, OpenAI API, LangChain, DALL·E 2, Midjourney.

Access to Recorded Live Videos

Learning does not stop here. To support better understanding of concepts and skill mastery, recorded videos of live classes will be provided to learners. These videos will be accessible for up to 6 months after course completion.

Domain-specific Projects & Assignments

To master the skills acquired during the course, learners are required to complete and submit few projects within one month of course completion. For guidance, they can reach out to expert trainers during this period.

Curriculum

LEARN WITH A WORLD CLASS CURRICULUM

Module 1. Course Introduction
Day 01: Introduction to Artificial Intelligence & Machine Learning
  • Introduction to Artificial Intelligence
  • Machine Learning fundamentals
  • Types of Machine Learning
  • Applications and industry use cases
Module 2. Python For Data Science
Day 02: Introduction to Python, Why Python, Variables, Operators, Strings, Indexing
  • Python fundamentals
  • Variables and data types
  • Basic operators
  • String manipulation and indexing
Day 03: Data Structures, Functions, Creating Function, Calling a function, Function Parameter
  • Lists, dictionaries, tuples, and sets
  • Function definition and calling
  • Function parameters and return values
  • Function scope
Day 04: Lambda Function, Conditional Statement, Loops and it's Control Statement
  • Lambda functions
  • Conditional statements (if, elif, else)
  • Loops (for, while)
  • Loop control statements (break, continue, pass)
Day 05: NumPy, Pandas for Data Handling
  • NumPy arrays and operations
  • Pandas DataFrames and Series
  • Data manipulation with Pandas
  • Data cleaning and preprocessing
Day 06: Matplotlib, Seaborn for Data Visualization
  • Basic plotting with Matplotlib
  • Advanced visualizations with Seaborn
  • Customizing plots
  • Creating interactive visualizations
Module 3. Statistics For Data Science
Day 07: Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency
  • Fundamentals of statistics
  • Descriptive statistics
  • Population vs. sample
  • Measures of central tendency (mean, median, mode)
Day 08: Standard Deviation, Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis
  • Measures of dispersion
  • Interquartile range and outlier detection
  • Correlation and covariance
  • Distribution characteristics (skewness, kurtosis)
Day 09: Probability, Probability distributions, Central Limit Theorem, Binomial & Normal Distribution
  • Basic probability concepts
  • Common probability distributions
  • Central Limit Theorem
  • Binomial and normal distributions
Day 10: Type I & Type II Error, T-test, Z-test, Hypothesis Testing
  • Hypothesis testing fundamentals
  • Type I and Type II errors
  • T-tests and z-tests
  • Practical applications of hypothesis testing
Module 4. Mini Project
Day 11: Data Analysis & Visualization
  • Exploratory data analysis
  • Data visualization techniques
  • Insight extraction from data
  • Mini project implementation
Module 5. Machine Learning
Day 12: Introduction to ML, Types of variables, Encoding, Normalization, Standardization
  • Machine learning fundamentals
  • Feature types and encoding techniques
  • Data normalization and standardization
  • Preparing data for ML algorithms
Day 13: Linear Regression, Logistic Regression, SVM, KNN
  • Linear regression models
  • Logistic regression for classification
  • Support Vector Machines
  • K-Nearest Neighbors algorithm
Day 14: Naïve Bayes, Decision Tree, Random Forest, MSE, RMSE
  • Naïve Bayes classifiers
  • Decision trees
  • Random Forest ensemble method
  • Regression metrics (MSE, RMSE)
Day 15: R2 Score, F1-Score, Confusion Matrix, Classification Report, Accuracy
  • Regression evaluation metrics
  • Classification evaluation metrics
  • Confusion matrix analysis
  • Model performance assessment
Day 16: Ensemble Techniques, Xgboost, Unsupervised Machine Learning-Introduction
  • Ensemble learning techniques
  • Gradient boosting with XGBoost
  • Unsupervised learning concepts
  • Applications of unsupervised learning
Day 17: PCA, Clustering, k-Means Clustering and Hierarchical clustering
  • Principal Component Analysis
  • Clustering fundamentals
  • K-means clustering
  • Hierarchical clustering
Module 6. Deep Learning
Day 18: Introduction to Neural Network, Forward Propagation, Activation Function (Linear, Sigmoid)
  • Neural network fundamentals
  • Forward propagation process
  • Linear activation function
  • Sigmoid activation function
Day 19: Activation Function (Relu, Leaky Relu), Optimizers, GD, Stochastics Gradient Descent
  • ReLU and Leaky ReLU activation functions
  • Optimization algorithms
  • Gradient Descent
  • Stochastic Gradient Descent
Day 20: Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution
  • Mini-batch gradient descent
  • Adaptive gradient algorithm (Adagrad)
  • Convolutional neural networks concepts
  • Padding and pooling operations
Day 21: Checkpoints and Neural Networks Implementation
  • Model checkpointing
  • Practical neural network implementation
  • Deep learning frameworks
  • Training and evaluation processes
Day 22: Time Series Analysis-Introduction, Various components of the TSA
  • Time series concepts
  • Time series components (trend, seasonality, residuals)
  • Time series visualization
  • Stationarity and tests
Day 23: Decomposition Method(Additive and Multiplicative), ARMA, ARIMA
  • Time series decomposition methods
  • Autoregressive Moving Average (ARMA) models
  • Autoregressive Integrated Moving Average (ARIMA) models
  • Forecasting techniques
Module 7. Computer Vision
Day 24: Introduction to Image Processing, and OpenCV
  • Fundamentals of image processing
  • Introduction to OpenCV library
  • Image manipulation techniques
  • Basic image operations
Day 25: Feature Detection - Object Detection and Segmentation
  • Feature detection methods
  • Object detection fundamentals
  • Image segmentation techniques
  • Feature extraction and analysis
Day 26: Forward Propagation & Backward Propagation for CNN
  • Convolutional Neural Network basics
  • Forward propagation in CNN
  • Backpropagation for CNN
  • Optimizing CNN models
Day 27: CNN Architectures like AlexNet, VGGNet, InceptionNet, ResNet,Transfer Learning
  • Popular CNN architectures
  • AlexNet, VGGNet model design
  • InceptionNet and ResNet principles
  • Transfer learning for computer vision
Module 8. Natural Language Processing (NLP)
Day 28: Introduction to NLP, Introduction to Text Mining, & Applications
  • NLP fundamentals
  • Text mining concepts
  • Applications of NLP
  • NLP libraries and tools
Day 29: Text Processing using Python, Text Segmentation & Sentiment Analysis
  • Text preprocessing techniques
  • Tokenization and segmentation
  • Sentiment analysis methods
  • Python libraries for text processing
Day 30: Introduction to Topic Modeling, LDA, Name-Entity Recognition (NER)
  • Topic modeling concepts
  • Latent Dirichlet Allocation (LDA)
  • Named Entity Recognition
  • Applications of topic modeling and NER
Day 31: Understanding Transformers, BERT Introduction & its architecture, Text classification
  • Transformer architecture
  • BERT model fundamentals
  • Attention mechanisms
  • Text classification with transformers
Day 32: Advanced Text Mining(Keyword Extraction, TF-IDF, Word2Vec usage), Text Classification, Automatic Speech Recognition
  • Advanced keyword extraction techniques
  • TF-IDF and Word2Vec models
  • Text classification methods
  • Introduction to speech recognition
Module 9. Reinforcement Learning (RL)
Day 33: RL Framework, Component of RL Framework, Examples of Systems
  • Reinforcement learning fundamentals
  • Components of RL systems
  • Reward systems and state spaces
  • Real-world applications of RL
Day 34: Types of RL Systems, Q-Learning
  • Model-based vs. model-free RL
  • Value-based vs. policy-based RL
  • Q-learning algorithm
  • Implementing Q-learning
Module 10. Major Project
Day 35: Machine Learning, Deep Learning & NLP - based Predictive Modeling
  • Integrated ML, DL, and NLP approaches
  • End-to-end predictive modeling
  • Advanced model architecture design
  • Model deployment and scalability
Module 11. SQL
Day 36: Basic of Database & its Types, Data Types, Operators, Create and Insert
  • Database fundamentals
  • SQL data types
  • SQL operators
  • CREATE and INSERT statements
Day 37: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, In,Wildcard
  • Data manipulation commands
  • Data definition language (DDL)
  • SELECT statements and filtering
  • Wildcard operators
Day 38: Like, Clause, Constraint, Aggregation Function, Group by, Order by
  • LIKE operator and pattern matching
  • SQL constraints
  • Aggregation functions (SUM, AVG, COUNT, etc.)
  • GROUP BY and ORDER BY clauses
Day 39: Having, Joins, Case, Complex Queries, Doubt Clearing
  • HAVING clause
  • SQL JOINs (INNER, LEFT, RIGHT, FULL)
  • CASE statements
  • Advanced SQL queries
Module 12. Tableau
Day 40: Tableau Desktop, Tableau products
  • Tableau Desktop introduction
  • Tableau product ecosystem
  • Tableau interface navigation
  • Connecting to data sources
Day 41: Data import, Measures, Filters
  • Data importing techniques
  • Dimensions and measures
  • Filter types and applications
  • Data preparation in Tableau
Day 42: Data transformation, Marks, Dual Axis
  • Data transformation options
  • Using marks in visualizations
  • Dual axis charts
  • Advanced visualization techniques
Day 43: Manage worksheets, Data visualization, Dashboards
  • Worksheet management
  • Creating effective visualizations
  • Dashboard design principles
  • Interactive dashboard creation
Module 13. Power BI
Day 44: Power BI Platform, Process Flow
  • Power BI ecosystem
  • Power BI Desktop interface
  • Data workflow in Power BI
  • Connecting to data sources
Day 45: Features, Dataset, and Bias
  • Power BI key features
  • Dataset creation and management
  • Understanding data bias
  • Data preparation techniques
Day 46: Pivoting, Query Group, DAX Function
  • Data pivoting operations
  • Query organization
  • DAX function fundamentals
  • Creating calculated measures
Day 47: Formula, Charts, Reports and Dashboards
  • DAX formulas and expressions
  • Chart types and selection
  • Report creation best practices
  • Dashboard design and deployment
Module 14. Major Project
Day 48: Database to Dashboard: Project Implementation with SQL, Tableau & Power BI
  • End-to-end business intelligence project
  • Database design and implementation
  • Data visualization with Tableau and Power BI
  • Creating interactive business dashboards
Module 15. Introduction to Generative AI
Day 36: Introduction to AI, Hype vs. Reality, Business Applications, Ethical Considerations, Introduction to Generative AI, From Text Generation to Multimodal Models, Potential and Challenges
  • Introduction to generative AI
  • Reality vs. hype in AI applications
  • Business use cases for generative AI
  • Ethical considerations in AI
  • Multimodal generative AI models
  • Potential and challenges of generative AI
Module 16. Introduction to open source Huggingface transformers platform
Day 37: Introduction to open source Huggingface transformers platform, Review of NLP Basics & Text Pre-processing, Introduction to NLP Concepts: Language Representations, Tokenization, Part-of-Speech Tagging, Text Preprocessing
  • Huggingface transformers platform overview
  • NLP basics and concepts review
  • Language representation techniques
  • Tokenization and POS tagging
  • Text preprocessing pipelines
Day 38: Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal, Understanding key NLP Applications using Huggingface platform
  • Feature engineering for NLP
  • Text normalization techniques
  • Stemming and lemmatization
  • Stop word removal strategies
  • Key NLP applications with Huggingface
Day 39: Sentiment analysis, Sentence classification, Generating text, Extracting an answer from text
  • Sentiment analysis implementation
  • Text classification with transformers
  • Text generation techniques
  • Question answering systems
  • Information extraction methods
Module 17. Language Models and Transformer Models
Day 40: Understanding language models, Probability-based language models, Unsupervised learning language representations, Introduction to transformer models, What are transformers
  • Language model fundamentals
  • Statistical and probability-based language models
  • Unsupervised learning for language
  • Introduction to transformer architecture
  • How transformers revolutionized NLP
Day 41: Types of models: encoder—decoder, decoder only, Attention mechanism, Tasks that transformer models can do: translation, text summarization, Q&A, text generation, Zero shot, few shot text classification
  • Encoder-decoder architectures
  • Decoder-only models
  • Attention mechanisms in depth
  • Machine translation applications
  • Text summarization with transformers
  • Question answering systems
  • Zero-shot and few-shot learning
Module 18. Introduction to Large Language Models (LLMs)
Day 42: Introduction to Large Language Models (LLMs), - Structure of popular models - Types of Models: text to text, text to image, text to video, multimodal
  • Large Language Model foundations
  • Structure of popular LLM models
  • Text-to-text generation models
  • Text-to-image generation
  • Text-to-video capabilities
  • Multimodal AI systems
Day 43: Other types of Generative AI algorithms, - GANs ( Generative Adverserial Networks), - Variational Autoencoders (VAEs), Diffusion Models, Mixture of Experts, - Different models available currently for image ( DALL-E-2, Midjourney)
  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAEs)
  • Diffusion models for image generation
  • Mixture of Experts architecture
  • DALL-E-2 image generation
  • Midjourney and other image models
Day 44: Hands on practice of NLP tasks using Huggingface library and opensource language models such as Bloom for finetuning a LLM, zero and few shot classification, Applications of Generative AI in business
  • Fine-tuning LLMs with Huggingface
  • Using Bloom for NLP tasks
  • Zero-shot classification implementation
  • Few-shot learning applications
  • Business applications of generative AI
Module 19. Customer Insights & Applications
Day 45: Customer Insights & Sentiment Analysis - Personalized Marketing & Content Creation - Chatbots: Automating Customer Service and Support - Document Processing Automation
  • AI for customer insights extraction
  • Sentiment analysis in business
  • Personalized marketing with AI
  • Content generation for marketing
  • Building customer service chatbots
  • Document processing automation
Module 20. AI Application Stack
Day 46: AI Application Stack: Infrastructure & foundation layer - Overview of AI infrastructure: cloud platforms, GPU, and distributed computing - Setting up an AI environment for generative models - Infrastructure considerations for scalable AI applications - Retrieval augmentation generation or RAG
  • AI infrastructure components
  • Cloud platforms for AI development
  • GPU and distributed computing
  • Setting up environments for generative AI
  • Scaling AI applications
  • Retrieval Augmented Generation (RAG)
Module 21. LangChain, AI Ethics and the Future of Work
Day 47: Langchain, Applied use case for Gen AI – hands on exercise - Designing a custom chatbot - Data analytics using Gen AI model such as OpenAI API
  • LangChain framework introduction
  • Building applications with LangChain
  • Custom chatbot development
  • Integrating OpenAI API
  • Data analytics with generative AI
Day 48: Hallucination, Data Privacy, Ethics, Environmental Impact of AI & future of Work - Importance of data privacy in AI applications - Ethical considerations in AI development and Deployment - Environmental Impact and Sustainability in AI - Future of Work: How AI Will Reshape Roles and Responsibilities
  • AI hallucinations: causes and prevention
  • Data privacy in AI applications
  • Ethical AI development frameworks
  • Environmental impact of AI systems
  • Future of work in the AI era
  • AI's impact on roles and responsibilities
Module 22. Major Project
Day 49: Advanced Predictive and Gen AI Modeling using ML, NLP, and Large Language Models
  • End-to-end project implementation
  • Integrating ML, NLP, and LLM techniques
  • Building advanced predictive models
  • Generative AI applications
  • Project deployment and presentation

Skills You Will Possess

Data Manipulation
Data Wrangling
Data Cleaning
Data Visualization
Data Analysis
Exploratory Data Analysis (EDA)
Interactive Dashboards
Dashboard Charts & KPIs
Predictive Analytics
Face Recognition
Text Processing
Image Processing
Sentiment Analysis
Object Detection
Optical Character Recognition
Prompt Engineering
LLM Fine-Tuning
Text Generation
Image Generation
LangChain Agents

Program Benefits

Cutting Edge Curriculum

Hand crafted Course content made by Experts from various Industries. Learn through Practical case studies and multiple projects.

On the Go Learning

Online accessible E-learning Material, live interactive lectures, Industrial Graded Projects, Case Studies and Multiple Tests & Evaluations.

Build Solid Foundation

190 hours of live instructor-led lectures covering the most in-demand tools in Data Science, AI and Gen AI Analytics.

Industry Mentorship

Receive one-on-one guidance from industry experts and confidently begin your career in the field of Data Science, AI and Gen AI.

Recognized Certification

Earn a Government of India approved & globally recognized certificate by NASSCOM IT- ITes SSC by clearing assessment Exam.

Industry Certificate

Opportunity to earn Highest Industry Certificate of AI-Data Scientists (NSQF LEVEL 6) from SSC NASSCOM.

Course Certificates

Upon successful completion of the program and passing the final assessment, you will receive:

  • Course Completion Certificate from Emerging India Analytics
  • NASSCOM IT-ITeS Sector Skill Council Certification
  • Opportunity to earn AI Data Scientist (NSQF LEVEL 6) certification from SSC NASSCOM

These certifications are recognized by employers globally and validate your expertise in Data Analytics, & Gen AI.

Sample Certificate
Sample Certificate

Real World Projects

Projects will be a part of your Nasscom Advanced Data Analytics Program with Gen AI to solidify your learning. They ensure you have real-world experience in Data Analytics and Gen AI.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

Beginner

Real Estate Analytics

Real Estate Analytics will involve supervised learning with an ensemble of various regression algorithms where we will optimize the predictions based on the error rate.

Intermediate

Solar Power Efficiency

The project will encompass three target variables that we will predict using the supervised machine learning algorithms for regression problems and minimize the error by tuning the hyperparameters.

Advanced

Recommendation Engine

In the Recommendation Engine project, we will use singular value decomposition to draw out relevant recommendations for music and movie selections based on the historical data points.

Career Services By emergingindiagroup

Soft Skills

Learners will be closely mentored to develop key soft skills like communication, teamwork, and adaptability, enhancing their career path.

Interview Preparation

Participate in mock interviews and receive detailed feedback sessions with experienced industry experts.

Profile Building

Attend resume workshops and get your LinkedIn profile optimized for better professional visibility.

Placement Assistance

Placement opportunities become available upon clearing the Placement Readiness Test and meeting eligibility criteria.

Exclusive access

Get exclusive access to our dedicated job portal to apply for open positions. Partnering with a select few start-ups and product companies, we offer personalized mentorship and support to help you explore relevant job opportunities and advance your career.

Real World Projects

Projects will be a part of your Nasscom Advanced Data Analytics Program with Gen AI to solidify your learning. They ensure you have real-world experience in Data Analytics and Gen AI.

  • Practice 20+ Essential Tools
  • Designed by Industry Experts
  • Get Real-world Experience

Our Alumni Works At

Learners thought about us

"
It was a great experience with Emerging India Analytics. The course format and content was very good. The faculty, Ms Lakshmi is very knowledgeable. She know the subject very well and the way she conducted the sessions was very much satisfactory. Thank you so much for your services and wish you all the best. God Bless.
Yogesh Ranjan Ghavnalkar

Yogesh Ranjan Ghavnalkar

Learner

"
As a non-IT background student, I am very much satisfied with the live sessions/classes conducted by Emerging India Analytics. Special thanks to the instructor/trainer, the way he is teaching, from the basic fundamentals, that a student having zero knowledge in IT/CS & coding, can easily understand the subjects/topics.
Tushar Kanta Behera

Tushar Kanta Behera

Learner

"
Classes are progressing smoothly, doubts are consistently addressed, fostering a clear understanding. Positive atmosphere, engaged learning, and effective communication contribute to a successful academic experience.
Aadi Bhardwaj

Aadi Bhardwaj

Learner

"
Coming from non-IT background was initially worrisome but I took the bold step into this course. The tutors have been fantastic as well as the personal support team. Looking back at the journey so far, I will say it's worth the all-round commitment and I recommend this program without reservation.
Israel Samuel

Israel Samuel

Learner

Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

1

Submit Application

Tell us a bit about yourself and why you want to join this program

2

Application Review

An admission panel will shortlist candidates based on their application

3

Admission

Selected candidates will be notified within 1week.

Program Fees

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Starting from
₹9,999*
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Others Payment Options

We provide the following options for one-time payment.

Internet Banking

Credit / Debit Card

Total Admission Fees
₹65,000*
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FAQs

1. What is the duration of the Nasscom Advanced Data Analytics Program with Gen AI?
The Nasscom Advanced Data Analytics Program with Gen AI is a comprehensive 08-month course designed to cover a wide spectrum of Artificial Intelligence & Machine Learning concepts and tools.
2. What topics are covered in the course?
The course covers a wide range of topics including Python, Statistics, SQL, Tableau, Power BI, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Reinforcement Learning, LLMs & Generative AI.
3. Do I need any prior knowledge to enroll in this course?
No prior knowledge is required. The course is designed for both beginners and professionals, starting with foundational concepts and gradually progressing to advanced topics.
4. How are the classes conducted?
Classes are conducted through live interactive sessions led by experienced instructors. Recorded sessions are also provided for flexible learning and future reference.
5. Are there any hands-on projects included in the course?
Yes, the course includes real-world projects designed to ensure practical learning. These hands-on projects help reinforce your understanding and build industry-relevant experience.
6. Will I receive a certificate upon completion?
Yes, upon successful completion of the course and clearing the online exam, you will receive a NASSCOM certification, which is highly recognized across the industry.
7. What kind of support is available if I have questions or need help?
You’ll have access to dedicated doubt-clearing sessions, project-based classes, and a responsive support team to assist you with any queries or technical issues throughout the course.
8. Will I receive a certificate upon completion?
Yes, upon successful completion of the course and clearing the online exam, you will receive a NASSCOM certification, which is highly recognized across the industry.
9. Can I try a demo class before enrolling?
Yes, you can request a demo class to experience the teaching methodology and course structure before making a decision.
10. What if I miss a live class?
Don’t worry—every live session is recorded and made available to you, so you can review it at your convenience and stay on track.