Online Data Science and Generative AI Course with Certification

About Us

The argument in favor of using filler text goes something like this: If you use real content in the Consulting Process, anytime you reach a review point you’ll end up reviewing and negotiating the content itself and not the design.

Consultation

About the Program

Our Post Graduate Program in Data Science with Generative AI will make you the most efficient in the industry, transforming your ability to extract observations and create contemporary solutions, regardless of your background. You can start here from basic data science to advanced generative AI methods. This inclusive data science certification includes an extensive internship in the educational program. You will get hands-on knowledge with differing cutting-edge tools and methods, containing machine learning, Python, SQL, deep learning, prompt engineering, and large language models (LLMs) through practical projects and under the counseling of experienced masters.

Practice as You Learn

Our curriculum is made specifically for practical exposure as it includes real-life problems, strictly making sure that what you study is directly aligned

Learn from Expert Professionals

Every session is conducted by well-experienced mentors who’ve built successful careers by working in the field and understand the real work

Interactive Learning with Practical Work

There has always been a lack of practical exposure in education. Here, we address it and make sure you learn practically at every step

Flexible Learning for Busy Schedules

No time to study at a fixed hour? We understand. Our live classes happen on weekends, with the option to choose either a morning or evening batch.

Connect with Experts and Advance Your Career

Digicrome supports and encourages students to connect with industry experts via webinars, Q&A sessions, and networking events.

Placement-Focused Training

We put your placement first by giving you marketable skills. We guarantee that you are not only studying but also completely equipped

Lifetime Access to Learning Materials

Once you're part of the course, you don't have to worry about missing anything or forgetting concepts. Digicrome offers lifetime course access

Industry Projects for Practical Learning

Job roles become easier when you understand the flow of tasks, and you're more likely to get hired when you have a portfolio.

Earn with Skills – Freelance Ready

Want to work in the tech field on your terms? We provide training that helps you market your skills, find the right clients, and manage projects

Course Overview

Fast-track your career in Data Science with our exclusive Advanced Certification Online Data Science and Generative AI program - the PGP-DS (Post Graduate Program in Data Science). Developed by industry leaders, this comprehensive course covers Python, Exploratory Data Analysis, Machine Learning, Deep Learning, and more, providing hands-on exposure to essential technologies.

Key Features:

Comprehensive Curriculum: Our meticulously designed curriculum ensures a seamless progression, introducing learners to interconnected facets of Data Science, including Python, SQL, Tableau, Machine Learning, Deep Learning, Exploratory Data Analysis, Data Visualization, and Artificial Intelligence.

Expert Faculty: Delivered by industry leaders, our immersive lectures leverage advanced technological tools for a rich learning experience. Dedicated program managers are available to address non-curricular queries and manage aspects of the course.

Hands-on Learning: The program integrates assignments and projects, allowing you to test and apply newly acquired knowledge constructively. Instructors provide detailed feedback, fostering active participation.

Networking Opportunities: Our platform facilitates learner networking, connecting you with peers, mentors, and industry professionals. Communication channels include email, voice calls, and video calls, enhancing the online learning experience.

Flexible Support: One-on-one doubt-clearing sessions, coupled with email, voice call, and video call support, ensure a smooth learning journey. The dedicated support system covers study material-related doubts and managerial queries.

Why Choose Digicrome's Data Science and Generative Course?

Digicrome addresses the demand for highly skilled Data Science and Generative AI professionals. Tailored for young professionals and individuals from various industries, our program offers a competitive edge in the ever-evolving field of Data Science and Generative Artificial Intelligence.

Embark on a transformative journey with Digicrome's Advanced Certification Online Data Science and Generative AI course. Gain a competitive edge in the ever-evolving field of Data Science and Artificial Intelligence. Enrich your career prospects and become an expert Data Science professional today.

Course Includes :

Price
299,000 + GST
US Price
$4499.00
Dubai Price
13156.00AED
Certifications
Yes
Language
English (US)

BOOK NOW

Our Course Curriculum

100% Trusted And Golden Opportunities With Key Features That will Help You To Transform Your Career

1. Introduction to Python - print(), input(), Comments, Variables, Built-in Data Types
2. Basics of Python - Strings and its methods, Python Booleans, Operators (Arithmetic, Logical, Comparison, Assignment, Identity, Memebership, Bitwise), Slicing and Indexing
3. Python Data Structures
4. Python Conditional Statements - IF, ELIF, ELSE
5. Loops in Python
6. Python Functions - Creating, Calling, Arguments, Arbitrary Arguments, Keyword Arguments, Arbitrary Keyword Arguments, Positional Arguments, Default Parameters, RETURN, LAMBDA.
7. Python Classes and Objects
8. Miscellaneous - Datetime, RegEx, String Formatting, TRY/EXCEPT

1. NumPy - Numerical Python
2. Data Wrangling using PANDAS
3. Data Visualization Using Matplotlib and Seaborn
4. Web Scraping using Beautiful Soup

1. Introduction to Statistics - Data Types: Numeric (Continuous, Discrete), Categorical (Binary, Ordinal, Nominal), Rectangular Data, NonRectangular Data
2. Descriptive Statistics
3. Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling
4. Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distrubtion and T-test, Binomial Distribution, ChiSquare Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution
5. A/B Testing (Treatment Group, Control Group), Hypothesis Testing (Type 1 Error, Type 2 Error, Significance Value (Alpha)), Permutation Tests, Degrees of Freedom, and Statistical Significance using P-values
6. Correlation Coffecient, Coefficient of Determination, Simple Linear Regression in Statistics

1.1 Introduction to Excel - Formatting, Insertion, Basic Functions (SUM, AVG, etc.)
1.2 Pivot Tables and LOOKUP Functions ( VLOOKUP, HLOOKUP, XLOOKUP, etc.)
1.3 Logical and Statistical Functions
1.4 Chart Data Techniques
1.5 Date/Time, Text, Math Functions
1.6 Advanced Filtering and Sorting
1.7 Summarizing, Importing and Exporting Data from Databases and Web

2.1 Introduction to Databases - (What are Databases), (What is MySQL), (What is RDBMS), (RDBMS v/s NoSQL)
2.2 Data Base Workflows - Understanding Entity Relationship Diagram, Understanding Normalization (1NF, 2NF, 3NF, BCNF)
2.3 Structured Query Language (SQL) - CRUD Operations
2.4 Data Aggregation Functions - (GroupBy), (OrderBy), (HAVING), (COUNT, SUM, MIN, MAX, AVG)
2.5 Joins in SQL - Primary Key and Foreign Key, Constraints, Set Operations, DML - Savepoint, Rollback

3.1 Installation, Setup, Importing CSV and Excel Files, Connecting SQL Databases and Cloud Services
3.2 Data Cleaning and Wrangling - Handling Missing Values, Handling Duplicates, Formatting of Data, Joins in Tableau
3.3 Basic Visualizations - Bar Chart, Line Chart, Pie Chart, Scatter Plot, Geographical Data Visualization on Maps, Dashboards in Tableau
3.4 Advanced Visualizations - Heat Maps, Tree Maps, Boxplots, Histograms, Parameters for interactivity and flexibility, Calculated Fields to create new metrics and dimensions
3.5 Analytics and Statistical Tools - Trend Lines and Forecasting, Clustering Techniques and Distribution Analysis
3.6 Filters, Highlighters, Actions to create interactive dashboards, Dashboard Designs
3.7 Case Study - Real World use case of Tableau for Data Science

4.1 Installation, Setup, Importing Files, Connecting SQL Databases and Cloud Services, Direct Query Methods in Power BI
4.2 Power Query - Data Cleaning and Transformation (Handling Missing Values and Duplicates, Formatting of Data)
4.3 Data Modeling - Calculated Columns, Managing Data Models, Creating Relationship between two tables
4.4 Basic and Advanced Data Analysis Expressions (DAX) Tutorial
4.5 Basic and Advanced Visualizations - (Basic - Bar Charts, Pie Charts, Matrices, Maps, etc.), (Advanced - Custom Visualizations, Slicers, Filters, Waterfall Charts, Funnel Charts, Gauge Charts, etc.)
4.6 Automated Quick Insights and AI Visuals, Dashboard Designs in Power BI
4.7 Case Study - Real World use case of Power BI for Data Science

1.1 What is ML, Why ML, Types of ML, (Training, Validation, and Testing Set)
1.2 Train/Test Split, Preprocessing of Data (LabelEncoder, OneHotEncoder), Standardization of Data
1.3 Hyperparameters, Selection and Fine Tuning of Models, (Main Challenges - Overfitting, Underfitting, Poor Quality Data, Irrelavant Features, etc.)

2.1 Performance Metrics - Accuracy, Recall, Precision, F1 Score, Confusion Matrix, Classification Report, Precision/Recall Tradeoff, ROC Curve, AOC Curve
2.2 Classification Models - Gradient Descent and Stochastic Gradient Descent, Logistic Regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Decission Trees
2.3 Ensembling Methods - Bagging (Voting Classifer, Cross Validation, etc.), Boosting (XG Boost, Adaboost, etc.), Random Forest Classifer, Stacking
2.4 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Multilabel Classification, L1 and L2 Regularization for overfitting, Handling Class Imabalance
2.5 Classification Project - Real World Use Case

3.1 Introduction - Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Cost Function and Gradient Descent
3.2 Performance Metrics - Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.
3.3 Challenges - Heteroskedasticity, Non - Normality of Data, Multicollinearity of Data, etc.
3.4 Regression Models - Decision Tree Regressor, Support Vector Machine (SVM), K Nearest Neighbors (KNN)
3.5 Ensemble Models - Cross Validation, Voting Classifier, Random Forest, Bagging and Boosting Methods
3.6 Advanced Techniques - Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, L1 and L2 Regularization
3.7 Regression Project - Real World Use Case

4.1 Introduction to Unsupervised Learning

4.2 Clustering Methods - KMeans, Hierarchical, Model Based Clustering, DBSCAN Clustering, Anamoly Detection using Gaussian Mixture Models

4.3 Dimensionality Reduction using Principal Component Analysis

4.4 Building and Working of Recommendation Engines

1.1 Biological to Artificial Neurons

1.2 The perceptron

1.3 Multi-layer Perceptrons (MLPs)

1.4 Input Layer, Hidden Layers and Output layers

1.5 Weights and Biases

1.6 Regression MLPs

1.7 Classification MLPs

1.8 Activation functions and Optimizers

2.1 Building a Neural Network using Sequential API

2.2 Building a Neural Network using Functional API

2.3 Building a Neural Network using Subclassing API

2.4 Saving and Restoring a Model

2.5 Callbacks

3.1 Vanishing/Exploding Gradients Problem

3.2 Batch Normalization

3.3 Gradient Clipping

3.4 Transfer Learning - Using Pretrained Layers

3.5 Pretraining on Auxiliary Task

3.6 Faster Optimizers - RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient

3.7 Learning Rate Scheduling

4.1 How to choose number of hidden layers and number of Neurons

4.2 Learning Rate, Optimizer, Batch Size, Loss Functions and Activation Functions

4.3 L1 and L2 Regularization

4.4 Dropouts and Batch Normalization

4.5 Max Norm Regularizatio

1.1 Structure - How CNNs are different from Traditional Neural Networks

1.2 Building Blocks - Filters, Kernals, Feature Maps, Pooling (Max, Average, Global), Padding (Valid vs Same)

1.3 Architectural Designs for Generative AI - Transposed Convolutions, Unsampling Techniques, Residual Connections (Skip Connections)

1.4 Types of CNNs in Generative AI - Encoder-Decoder, UNet, VGG and ResNet Variants, Dilated Convolutions, Multi-Scale Convolutions, Attention Mechanisms, Conditional CNNs

1.5 Relevance - High Resolution Image Generation, Image Synthesis, Texture Synthesis, Video Generation

2.1 Core Concepts - Hidden State, Back Propagation through time, Challenges (Vanishing/Exploding Gradients, Short term Memory)

2.2 Basic Architectures (Simple and Deep RNNs), Advanced Architectures (Long Short-Term Memory, Gated Recurrent Units), Bidirectional RNNs, Sequence to Sequence Models)

2.3 RNN Variants for Generative AI - Attention Mechanisms in RNNs, Conditional RNNs, Hierarchical RNNs)

2.4 Incorporate Transformers, Hybrid Models (Combination of RNNs with CNNs and Attention Mechanisms for Generative AI)

2.5 Applications - Text Generation, Music Compostion, Speech Synthesis, Video Generation, Language Translation

3.1 Architecture - Encoder/Decoder Structure, Self Attention Mechanism, Positional Encoding, Residual Connections, Training of Transformer Models

3.2 Variants of Transformers - Encoder Only, Decoder Only, Encoder-Decoder, Vision Transformers, Multimodel Transformers, Efficient Transformers

3.3 Attention Mechanisms - Soft, Hard, Sparse, and Cross Attention Mechanisms

3.4 Fine Tuning and Transfer Learning - Prompt Engineering, Few-shot and Zero-shot Learning, LoRA (Low Rank Adaptation)

3.5 Transformer Models for Text Generation - BERT, GPT (2,3,4), BART, CLIP

3.6 Relevance for Generative AI - Autoregressive Modelling, Masked Language Modelling, Sequence to Sequence Models, Reinforcement Learning with Human Feedback (RLHF)

4.1 Relevance for Generative AI - Dimentionality Reduction, Data Denoising, Anomaly Detection, Image Generation, Feature Extraction, Latent Space Manipulation, Data Generation

4.2 Training of Autoencoders, Architecture - Encoder, Decoder and Latent Space (Bottleneck)

4.3 Types of Autoencoders - Vanilla Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Convolutional Autoencoders, Variational Autoencoders, Contractive Autoencoders, Stacked Autoencoders, Adversarial Autoencoders

4.4 Advance Architectures - Beta-VAE, Conditional Autoencoder, Seuqence to Sequence Autoencoder, and Graph Autoencoder

5.1 Applications of GANs in Generative AI - Image Generation, Video Generation, Text to Image Synthesis, Music and Audio Generation, Style Transfer

5.2 Architecture - Generator, Discriminator, Adversarial Loss

5.3 Types of GANs - Vanilla GANs, Deep Convolutional GANs, Conditional GANs, Wasserstein GANs, Progressive Growing GANs, Cycle GANs, Style GANs, BigGANs, Pix2Pix.

5.4 Challenges - Mode Collapse, Non-Convergence, Vanishing Gradients

5.5 Advance Concepts (Attention GANs, 3D GANs, Speech GANs, Multi-Model GANs), Metrics (Inception Score, Fréchet Inception Distance, Perceptual Path Length)

5.6 Metrics - Inception Score, Fréchet Inception Distance, Perceptual Path Length

5.7 Key differences between Autoencoders and General Adversarial Networks

1 Objective

2 Project Deliverables

3 Technologies Used

4 Expected Outcomes

5 Learning Outcomes

1 Professional Soft Skills

2 Final Exam

3 Final Capstone Project - Practical Exam (30 Days)

Internship Program

Industry-Ready Skills

Discover Your Strengths

Master Essential Tools

Hands-On Data

Effective Time Management

Resume Enhancement Support

Professional Network Building

Portfolio Development Guidance

Expert-Led Feedback

Recognized Internship Certification

SWOT-Based Self-Assessment

Effective Communication Skills

Negotiation & Persuasion Skills

Resume Writing & Review

Soft Skills Program

Introduction to Soft Skills

Professional Email Writing

Group Discussion Techniques

Professional Etiquette & Grooming

Time Management Essentials

Interview Preparation & Tips

Mock Interviews

Sample Projects You'll Build

Get hands-on experience with real-world inspired projects. These are some examples of what you'll build during the course.

AI-Powered Resume Analyzer for Recruiters

Develop a smart recruitment assistant that reads resumes, extracts key skills, compares them to job descriptions, and ranks the candidates automatically. The system uses LLMs to also generate professional summaries of top applicants, helping HR teams reduce screening time by over 60%.
Skills Required: Python, NLP, GPT-4/OpenAI API, Named Entity Recognition (NER), Streamlit, Prompt Engineering

Sales Forecasting with Time Series and AI Narratives

Build an end-to-end forecasting system for retail companies using historical sales data. The model not only predicts upcoming sales trends but also creates executive-friendly reports in natural language using GenAI—perfect for C-suite dashboards.
Skills Required: Time Series Modeling (ARIMA/Prophet), Pandas, Matplotlib, GPT-3/4 APIs, Data Wrangling, Prompt Crafting

Healthcare Chatbot for Symptom Analysis

Create a conversational AI tool that interacts with users to understand their symptoms and provides probable diagnosis suggestions based on verified medical datasets. It uses GenAI to explain the conditions in simple language and recommend doctor visits when necessary.
Skills Required: NLP, Transformers, Dialog Management, Hugging Face, Streamlit, LangChain, LLM Integration

AI-based Stock Market News Summarizer & Analyzer

Scrape real-time financial news and use sentiment analysis to evaluate the impact on stock performance. Then, use Generative AI to convert raw data into clean, digestible investment insights, summaries, and risk alerts for traders.
Skills Required: Web Scraping (BeautifulSoup/Selenium), Sentiment Analysis, Text Summarization, LangChain, GPT APIs, Python

Personalized Movie Recommendation System with Generative Reviews

Build a movie recommendation engine using collaborative filtering that not only suggests films based on user behavior but also generates custom reviews and movie summaries using LLMs to improve user engagement.
Skills Required: Recommendation Algorithms, Machine Learning, Matrix Factorization, Python, OpenAI API, Data Handling

Fake News Detector with AI-Generated Explanations

Train a classification model to detect fake news articles from real ones, and use Generative AI to explain why a particular article was flagged, breaking down language patterns, sources, and credibility metrics.
Skills Required: NLP, Supervised Learning, Scikit-learn, GPT-4 for Explanations, Tokenization, LangChain

E-Commerce Product Image Caption Generator

Combine computer vision and GenAI to generate detailed product descriptions from images, helping e-commerce businesses automate their product listing processes. The AI understands product types, styles, and features from images and writes marketing-friendly captions.
Skills Required: Deep Learning (CNNs), Vision-Language Models (BLIP, CLIP), Transformers, Image Preprocessing, Python

Smart City Traffic Flow Predictor with AI Reporting

Use IoT sensor data to analyze and predict peak traffic hours in metropolitan cities. The project integrates dashboards and uses GenAI to auto-generate policy recommendations, reducing congestion and improving route planning.
Skills Required: Time Series Analysis, Feature Engineering, Data Visualization (Plotly/Dash), GPT Reporting, Pandas

Customer Churn Prediction with LLM-Generated Recommendations

Build a model that predicts which customers are most likely to leave a service or subscription. Then, use LLMs to generate actionable, segment-specific retention strategies such as discounts, loyalty points, or personalized messages.
Skills Required: Classification Models, Logistic Regression, Data Cleaning, GPT-4 Integration, Customer Analytics

10. GenAI-Powered Legal Document Summarizer

Tackle the complexity of legal documents by developing a tool that extracts clauses, highlights key points, and summarizes entire contracts into human-readable formats—ideal for law firms, startups, and compliance teams.
Skills Required: Text Parsing, NLP, Transformers, LLM Summarization (LLM Chains), Domain-Specific Prompting, LangChain

TESTIMONIALS

What Students Say About
Digicrome Experience

Students love the hands-on learning, expert mentors, and real-world projects that make the Digicrome experience truly exceptional.

GET STARTED

Best Platform

“A very well-structured and well-balanced program content that you won't find in other programs or certification programs. Being a beginner in DS, I found the structure of the Executive PG Programme from Digicrome most helpful. They even teach you most of the prerequisites with prep sessions before you even start the course.

Vikas Kumar

AI Engineer

Nancy Chaudhary

“Everybody should be focusing on growing their business online right now. At DigiCrome, you will get to learn how maximum ROI, how to grow your business online, convert to sales and all that. , The best part about DigiCrome is its faculty who have previously worked in a renowned company like Google.

Nancy Chaudhary

NLP Engineer

Best Data Science Institute

“These guys are talented, incredible, and reliable. They always go above and beyond for us. Their work is fantastic, and they make a great team together. Highly recommend

Kushal Sharma

ML Engineer

Best Learning Platform

“Truly professional in my experience. I mean the whole team is keen to provide quality service and support. Highly recommended and trustworthy.

Hitender Solanki

AI Developer

Best Data Science Course

“Digicrome is a platform that provides professional assistance for your career development and a friendly environment when working with seniors as well. When it is about the counselling, it ensures to provide the correct and a successful path for the students

Priya Bala

Data Analyst

Best Institute for Data Science

“Awesome. Team is very talented and extremely helpful. Quality knowledge is provided. Recommended for anyone who is interested to make career in this field.

Chetan Kaushik

Data Scientist

Application Process for Digicrome

Our Acknowledged features offerings

Career Consultation

Assess eligibility

Personalized Guidance

Acceptance letter

Easy Registration

Pay booking amount

Start Upskilling

Access curriculum

Ongoing Support

Mentorship & guidance

Our Course Comes with Offerings

By Joining Our Program, Underlying Are The Key Featuers You Will Get

Get Lifetime Access to LMS

Your growth doesn’t end with the course—and neither does your access. With lifetime entry to our Learning Management System, you can revisit lessons, sharpen your skills, and stay up to date whenever you need. Learn at your pace, on your terms—because real learning is lifelong.

Live Interactive Online Sessions

" Make your weekends count with live sessions guided by experienced industry professionals. These aren’t just classes—they’re your space to ask, discuss, and truly understand. Connect with peers, solve real-world challenges together, and build confidence that lasts beyond the classroom. All scheduled keeping your time and growth in mind.

Regular Evaluations for better learning

We don’t just track your progress—we walk with you every step of the way. Through timely check-ins, constructive feedback, and personalised support, we help you understand what’s working and where to improve. It’s not about marks, it’s about momentum—so you keep moving forward with clarity and confidence.

Personalized Doubt Sessions

Every learner has unique challenges—and that’s why we offer one-on-one mentor support tailored just for you. From untangling tough topics to guiding your next steps, our mentors are here to listen, support, and keep you moving forward. Because real learning happens when someone’s genuinely there for you.

Hands-On Projects & Case Studies

Whichever path you choose—be it Data Science, AI, or Analytics—you won’t just study concepts, you’ll apply them. Through practical projects inspired by real industry scenarios, you’ll build the skills and confidence to solve challenges that companies face every day. It’s not just learning—it’s preparing for what comes next.

Focused Learning Tracks

Your career journey is unique—and your learning should reflect that. Choose a course that matches your goals, and dive deep into the skills that matter most in your domain. Whether you're drawn to tech, business, or the creative world, you'll gain focused expertise that sets you up for success, your way

Interview Preparation

We don’t just help you learn—we help you land the job. With personalised career guidance and realistic mock interviews, you’ll get the support you need to present your strengths, handle tough questions, and walk into every interview prepared and self-assured. Because your success is our goal, right from day one.

Our FAQs

Imperative FAQs About Us!

This is a 12-month online program that will equip learners with high-demand skills in data science and generative AI. The course includes live sessions, hands-on practice through projects, and a six-month internship. Moreover, it also includes placement assistance to help you get placed in a relevant role.

Anyone can apply. The prerequisite is only to be a graduate from any domain, with basic computer knowledge. It is not mandatory to be from a technical background. You can be from humanities, commerce, or maths and science; you will gain mastery starting from the basics and gradually move to advanced Data Science and Generative AI.

Yes, this course is for all levels, as herein a fresh graduate can gain new skills or a professional can expand existing skills. You'll begin with Excel, Python, SQL, then move to Machine Learning, Deep Learning, NLP, Time Series, Generative AI and more sub topics in deep.

You will be getting complete job assistance, including resume building, mock interviews, LinkedIn help, and personalized guidance. We will prepare you to get the right jobs of your preference.

Complete this course and you’ll have opportunities like data scientists, AI specialists, ML engineers, business analysts, and more, roles in demand across industries like e-commerce, health, and finance. The pay and growth are high, as there is a lack of skilled professionals, but high industry demand.

This course covers the modern Data Science industry demands. It teaches generative AI alongside and assures you receive expert-led live classes, projects, and career assistance. Moreover, it includes an internship for practical exposure and can be opted by both beginners and upskillers.

Popular Categories

Top Courses

Post Graduate Program In Data Science with Artificial Intelligence

Advanced Certification Program In Artificial Intelligence & Machine Learning

Advanced Certification Online Data Analytics and Machine Learning Course

Cart Items - 4

How Gamification is Changing the Way...

Learning is the Key soft skills and Professional

The Importance of Critical Thinking in Education

Learning is the Key soft skills and Professional

About Us

Contact Info

Post Graduate Program In Data Science with Generative AI

What Our Program Offers?

Trusted by world's best Organisations

About the Program

Practice as You Learn

Learn from Expert Professionals

Interactive Learning with Practical Work

Flexible Learning for Busy Schedules

Connect with Experts and Advance Your Career

Placement-Focused Training

Lifetime Access to Learning Materials

Industry Projects for Practical Learning

Earn with Skills – Freelance Ready

Course Overview

Course Includes :

Share Now

Our Course Curriculum

Fundamentals of Python Programming

Exploratory Data Analysis using Advanced Python Libraries

Practical Statistics for Data Scientists

Advanced Tutorial on Microsoft Excel

Structured Query Language

Tableau for Data Science

Power BI for Data Science

Introduction to Machine Learning

Classification Techniques

Regression Techniques

Unsupervised Learning

Introduction to Neural Networks

Implementation using Tensorflow and Keras

Training Deep Neural Networks

Fine Tuning Models

Types of Networks for Generative AI

Recurrent Neural Networks (RNNs)

Transformer Models

Autoencoders

General Adversarial Networks (GANs)

Capstone Project - Multi-Modal AI Assistant (MMAI)

Corporate Communication, Final Project, and Final Exam

Internship Program

Soft Skills Program

Languages and Tools Covered

Sample Projects You'll Build

AI-Powered Resume Analyzer for Recruiters

Sales Forecasting with Time Series and AI Narratives

Healthcare Chatbot for Symptom Analysis

AI-based Stock Market News Summarizer & Analyzer

Personalized Movie Recommendation System with Generative Reviews

Fake News Detector with AI-Generated Explanations

E-Commerce Product Image Caption Generator

Smart City Traffic Flow Predictor with AI Reporting

Customer Churn Prediction with LLM-Generated Recommendations

10. GenAI-Powered Legal Document Summarizer

Trusted by millions of learners around the Globe

Moments of Honour

Our Placed Learners In Different Big Firms

Mohd. Jubair Khan

NAMAN SHARMA

Abhinav Tiwari

Raushan Kumar

Santosh Singh

Mansi Dwivedi

Asha Sandeep

Avantika Verma

Shivani Kiran

Manohar Ramchandra

Mukesh Ghodeswar

Ritika Sadhwani

What Students Say About
Digicrome Experience