Nasscom Program of Mastering DS with AI & Gen AI

Immersive Learning Program

This NASSCOM Mastering Data Science with AI & Gen AI program is a comprehensive, industry-aligned certification offered by Emerging India Analytics. This 49-day immersive training equips learners with advanced knowledge and practical skills in Data Science, Artificial Intelligence, and the latest advancements in Generative AI. Combining theoretical foundations with hands-on projects and real-world applications, the program is designed for aspiring Data Scientists, AI professionals, and those seeking to upskill in this fast-growing domain. Participants will learn to develop intelligent systems and data-driven solutions that address modern business challenges while building a strong analytical and AI skill set.

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Introduction

Mastering Data Science with AI & Gen AI

This 150-hours intensive program, offered by Emerging India Analytics, provides a comprehensive learning experience in Data Science, Artificial Intelligence and Generative AI. Designed for both beginners and professionals, it follows a structured, project-based learning approach, blending core concepts with practical implementation.
Learners will gain exposure to high-demand tools and technologies and will receive an industry-recognized certification upon completion. The program ensures that participants are equipped with the skills required to build real-world AI solutions and stay ahead in the evolving tech landscape.

Data Science & AI Training

Tools

Python
Pandas
Scikit-learn
Tensorflow
Keras
NLP
OpenCV
huggingface
midjourney
pytorch
OpenAI
Dalle2
Langchain
LLMs
BERT

Program Structure

40-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.

150-Hours Live Instructor-Led Training

You will get an execution-based learning experience on Python, Statistics, ML, DL, 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 AI&ML and Gen AI
  • Introduction to Artificial Intelligence
  • Machine Learning fundamentals
  • Generative AI basics
  • AI industry applications
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, Standard Deviation
  • Fundamentals of statistics
  • Descriptive statistics
  • Population vs. sample
  • Measures of central tendency and standard deviation
Day 08: 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 and Poisson Distribution, Normal Distribution
  • Basic probability concepts
  • Common probability distributions
  • Central Limit Theorem
  • Binomial, Poisson, 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, Gradient Descent, 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, Introduction to BERT and its architecture, Text classification using BERT
  • Transformer architecture
  • BERT model fundamentals
  • Attention mechanisms
  • Text classification with BERT
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. 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 and challenges
  • Multimodal generative AI models
Module 12. Introduction to open source Huggingface transformers platform
Day 37: Introduction to open source Huggingface transformers platform, Review of NLP Basics & Text Preprocessing, Introduction to NLP Concepts: Language Representations, Tokenization, Part-of-Speech Tagging, Text Preprocessing
  • Huggingface transformers platform introduction
  • Core NLP concepts review
  • Text preprocessing pipelines
  • Language representations and tokenization
  • Part-of-speech tagging techniques
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
  • Practical NLP applications with Huggingface
Day 39: Sentiment analysis, Sentence classification, Generating text, Extracting an answer from text
  • Sentiment analysis implementation
  • Text classification techniques
  • Text generation with transformers
  • Question answering systems
  • Information extraction from text
Module 13. 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 representations
  • 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 transformer architectures
  • Decoder-only transformer models
  • Attention mechanisms in depth
  • Machine translation with transformers
  • Text summarization techniques
  • Question answering systems
  • Zero-shot and few-shot learning
Module 14. 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 architecture
  • Popular LLM models and their structures
  • 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 and image generation capabilities
  • Midjourney and other image generation systems
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 specific NLP tasks
  • Zero-shot classification techniques
  • Few-shot learning practical applications
  • Business applications of generative AI
  • Case studies of AI implementation
Module 15. AI Application Stack
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 contexts
  • Personalized marketing with AI
  • Content generation for marketing
  • Building customer service chatbots
  • Document processing and automation
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 16. 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 hallucination: causes and prevention
  • Data privacy in AI applications
  • Ethical AI development frameworks
  • Environmental impact of AI systems
  • Carbon footprint of large models
  • How AI will transform work
  • Future skills needed in AI era
Module 17. 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)
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
API Integration

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

150 hours of live instructor-led lectures covering the most in-demand tools in Data Science with Artificial Intelligence & Generative AI.

Industry Mentorship

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

Sample Certificate
Sample Certificate

Real World Projects

Projects will be a part of your Nasscom Program of Mastering Data Science with Artificial Intelligence & Generative AI to solidify your learning. They ensure you have real-world experience in Data Science, AI & GenAI.

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 Program of Mastering Data Science with Artificial Intelligence & Generation AI to solidify your learning. They ensure you have real-world experience in Data Science, AI & 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

Our Loan Partners

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Zero Cost EMI options Available

from RBI Approved NBFCs

Starting from
₹4,999*
Contact Us for more details

Others Payment Options

We provide the following options for one-time payment.

Internet Banking

Credit / Debit Card

Total Admission Fees
₹57,000*
Apply Now

FAQs

1. What is the duration of the Nasscom Program of Mastering DS with AI & Gen AI?
The Nasscom Program of Mastering DS with AI & Gen AI is a comprehensive 06-month course designed to cover a wide spectrum of Artificial Intelligence & Generative AI 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.