NASSCOM Advanced Data Science & Gen AI Program

Immersive Learning Program

In the age of data-driven innovation and artificial intelligence, the ability to extract insights and build intelligent systems is a highly sought-after skill. The NASSCOM Advanced Data Science & Gen AI Program, offered by Emerging India Analytics is a comprehensive 220-hours industry-aligned training designed for aspiring Data Scientists, AI Engineers, and Gen AI Professionals. Developed by industry experts, this program provides comprehensive exposure to the full Data Science lifecycle and modern AI practices. The curriculum covers cutting-edge Generative AI tools and frameworks such as LLMs, Transformers, Hugging Face, and LangChain. Whether you're a beginner or a tech enthusiast looking to transition into AI and Big Data, this program equips you with the skills, tools, and confidence to analyze real-world data, build intelligent systems, and drive business transformation through AI innovation.

Contact Us Download Syllabus

OUR KNOWLEDGE PARTNERS

Introduction

Nasscom Advanced DS and Gen AI Program

This intensive 220-hours NASSCOM Advanced Data Science & Gen AI Program is crafted for aspiring data professionals looking to build a solid foundation in Data Science, Big Data, and the fast-growing field of Generative AI. The curriculum includes core programming in Python, Advanced Statistics, Machine Learning, Deep Learning, NLP, Computer Vision, and hands-on experience with Big Data tools like Hadoop, Hive, and Spark.
What sets this program apart is its extensive module on Generative AI, offering practical training with large language models (LLMs), Hugging Face Transformers, LangChain, and prompt engineering to develop real-world AI applications. Through practical labs, capstone projects, and expert mentorship, participants gain expertise in both traditional and cutting-edge AI technologies. Ideal for students, graduates, and professionals, this program prepares learners to advance their careers in AI-powered industries.

Data Science & Gen AI Program

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
Apache Hive
hadoop
ApacheHbase
ApachePig
apachespark
Apachesqoop
hadoopyarn
Apachehadoophdfs

Program Structure

80-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 DS, AI & Gen AI, to know about the fundamentals, which would enrich your future learning experience.

220-Hours Live Instructor-Led Training

You will gain hands-on learning in Python, Statistics, Machine Learning, Deep Learning, SQL, Power BI, Hadoop, Spark, NLP, Computer Vision, Reinforcement Learning, Transformers, OpenAI API, LangChain, DALL·E 2, and more emerging tools.

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 NLTK, Introduction to Text Mining, & Applications
  • NLP fundamentals
  • Text mining concepts
  • NLTK library introduction
  • Applications of NLP
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. Power BI
Day 40: Power BI Platform, Process Flow
  • Power BI ecosystem
  • Power BI Desktop interface
  • Data workflow in Power BI
  • Connecting to data sources
Day 41: Features, Dataset, and Bins
  • Power BI key features
  • Dataset creation and management
  • Creating and using bins
  • Data preparation techniques
Day 42: Pivoting, Query Group, DAX Function
  • Data pivoting operations
  • Query organization
  • DAX function fundamentals
  • Creating calculated measures
Day 43: Formula, Charts, Reports and Dashboards
  • DAX formulas and expressions
  • Chart types and selection
  • Report creation best practices
  • Dashboard design and deployment
Module 13. Major Project
Day 44: Database to Dashboard: Project Implementation with SQL, Tableau & Power BI
  • End-to-end business intelligence project
  • Database design and implementation
  • Data visualization with SQL, Tableau and Power BI
  • Creating interactive business dashboards
Module 14. Big Data
Day 45: Introduction to Big Data Analytics
  • Big Data fundamentals
  • The 5 V's of Big Data
  • Big Data ecosystem overview
  • Big Data use cases and applications
Day 46: Types of Data, Introduction to Bigdata (History,V's of Bigdata, Advantages and Disadvantages of BigData), Use of Bigdata in different sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Concept and challenges in parallel computing, Distributed Computing and use in Hadoop, Intro to Tools in Hadoop, Life cycle of Bigdata Analytics
  • Big Data types and characteristics
  • History and evolution of Big Data
  • Advantages and challenges of Big Data
  • Introduction to Apache Hadoop
  • Horizontal vs. vertical scaling
  • Parallel and distributed computing concepts
  • Big Data tools and ecosystem
  • Big Data analytics lifecycle
Day 47: On Premises Installation Oracle Virtual Box and setup of VM & Ubuntu, Basic Linux command, Download and Installation of Hadoop, Introduction to Hadoop, Core components of Hadoop, Hadoop working, Principle, HDFS Architecture
  • Oracle VirtualBox installation
  • Setting up Ubuntu virtual machine
  • Basic Linux commands
  • Hadoop installation and configuration
  • Core components of Hadoop
  • HDFS architecture and principles
Day 48: VM creation on Cloud (AZURE), Configuration & Insight to Single Node, Hadoop Deployment(bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site), Format HDFS Namenode
  • Creating virtual machines on Azure
  • Single node Hadoop configuration
  • Hadoop configuration files and settings
  • Setting up HDFS, YARN, and MapReduce
  • Formatting the HDFS NameNode
Day 49: Introduction to Hadoop commands, HDFS file operations, its use cases, and troubleshooting
  • Hadoop shell commands
  • HDFS file system operations
  • Working with HDFS data
  • Use cases for HDFS operations
  • Troubleshooting common HDFS issues
Day 50: Introduction to MapReduce, It's use case & Architecture, MapReduce Implementation
  • MapReduce programming paradigm
  • MapReduce architecture
  • Map and Reduce phases
  • Implementing MapReduce jobs
  • MapReduce workflow execution
Day 51: Introduction to Hive, It's architecture, Hive Installation, and hands-on with database
  • Apache Hive introduction
  • Hive architecture components
  • Data warehousing with Hive
  • Installing and configuring Hive
  • Creating and working with Hive databases
Day 52: Hive Query Language (HQL) and SQL operations
  • HiveQL syntax and structure
  • Data definition languages in Hive
  • Data manipulation in Hive
  • Complex queries and operations
  • Joins and aggregations in Hive
Day 53: Introduction to Sqoop, Use Cases & Architecture, Installation of Sqoop, Sqoop Commands
  • Apache Sqoop introduction
  • Data transfer use cases
  • Sqoop architecture components
  • Installing and configuring Sqoop
  • Importing and exporting data with Sqoop
Day 54: Introduction to Hbase, It's architecture & components, Installation of Hbase, HBase Querying
  • Apache HBase introduction
  • NoSQL database concepts
  • HBase architecture and data model
  • Installing and configuring HBase
  • HBase shell commands and querying
Day 55: Installation of Spark, Spark vs Hadoop, Spark Components, PySpark Querying, Real-time Use Cases
  • Apache Spark introduction
  • Spark vs. Hadoop comparison
  • Spark ecosystem components
  • Installing and configuring Spark
  • PySpark programming basics
  • Real-time data processing use cases
Day 56: Introduction to Apache Pig, Pig Installation, Pig Querying
  • Apache Pig introduction
  • Pig architecture and components
  • Pig Latin scripting language
  • Installing and configuring Pig
  • Data processing with Pig
Day 57: Introduction to Apache Oozie, Types of Oozie Jobs, Hands-on with Oozie
  • Apache Oozie workflow scheduler
  • Oozie architecture and components
  • Types of Oozie workflows
  • Configuring and running Oozie jobs
  • Workflow coordination with Oozie
Day 58: Introduction to Apache Flume, It's use cases & architecture, Flume Installation & Configuration
  • Apache Flume introduction
  • Flume architecture (sources, channels, sinks)
  • Use cases for log data collection
  • Installing and configuring Flume
  • Creating Flume agents
  • Setting up data flow pipelines
Module 15. Project
Day 59: Build a Big Data Analytics pipeline using Hadoop ecosystem tools (HDFS, Sqoop, Hive, HBase, Pig, Spark, Flume, and Oozie) to ingest, process, and analyze large-scale data.
  • End-to-end Big Data project implementation
  • Data ingestion from various sources
  • Data processing using Hadoop tools
  • Big Data analytics and visualization
  • Project deployment and presentation
Module 16. Introduction to Generative AI
Day 60: 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 concepts
  • 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 17. Introduction to open source Huggingface transformers platform
Day 61: 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 62: 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 63: 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 18. Language Models and Transformer Models
Day 64: Understanding language models, Probability-based language models, Unsupervised learning language representations, Introduction to transformer models, What are transformer models
  • Language model fundamentals
  • Statistical and probability-based language models
  • Unsupervised learning for language
  • Introduction to transformer architecture
  • How transformers revolutionized NLP
Day 65: 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 19. Introduction to Large Language Models (LLMs)
Day 66: 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 67: Other types of Generative AI algorithms, GANs (Generative Adversarial 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 68: 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
Day 69: 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
Day 70: 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 20. LangChain, AI Ethics and the Future of Work
Day 71: 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 72: Hallucination, Data Privacy, Ethics, and 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, The 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 21. Major Project
Day 73: Advanced Predictive and Gen AI Modeling using ML, NLP, and Large Language Models
  • End-to-end comprehensive project implementation
  • Integrating ML, DL, NLP, and LLM techniques
  • Advanced predictive modeling
  • 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
Big Data Processing
Hadoop Ecosystem
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

220 hours of live instructor-led lectures covering the most in-demand tools in Data Science, BI, AI, Big Data 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 Science and AI.

Sample Certificate
Sample Certificate

Real World Projects

Projects will be a part of your NASSCOM Advanced Data Science & Gen AI Program to solidify your learning. They ensure you have real-world experience in Data Science 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 Science & Gen AI to solidify your learning. They ensure you have real-world experience in Data Science 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

Our Loan Partners

Loan Partner 1 Loan Partner 2 Loan Partner 3

Zero Cost EMI options Available

from RBI Approved NBFCs

Starting from
₹9,999*
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
₹72,500*
Apply Now

FAQs

1. What is the duration of the NASSCOM Advanced Data Science & Gen AI Program?
The NASSCOM Advanced Data Science & Gen AI Program is a comprehensive 09-month course designed to cover a wide spectrum of Data Science, Artificial Intelligence, Big Data & 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, Natural Language Processing (NLP), Computer Vision, Reinforcement Learning, and Generative AI. It also offers hands-on experience with Big Data tools such as Hadoop, Hive, and Spark. Additionally, the program provides practical training in working with large language models (LLMs), Hugging Face Transformers, and LangChain.
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.