Post Graduate Program In Data Science with Generative AI

Program 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.

Post Graduate Program In Data Science with Generative AI

  1. 299000.00
Features
  • 12-Months Live Online Program
  • Guaranteed Job Placement Aid*
  • AI, Projects and Case Studies
  • Industry Based - Projects

Key Highlights

  • Live Online12-Months Live Online Program
  • Experienced FacultiesGuaranteed Job Placement Aid*
  • Students HandoutsAI, Projects and Case Studies
  • Solid FoundationIndustry Based - Projects
  • Solid Foundation06 -Months Internship Experience
  • Solid FoundationExpert Experienced Trainers
  • Solid Foundation1 Month Job Interviews Preparation
  • Solid Foundation1:1 Doubt Session

Program Objective

  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)

Our Certificates

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Post Graduate Program In Data Science with Generative AI

Master real-world applications of data science and learn to create analytical models that drive business success. Designed for recent graduates and professionals, this job-assurance program equips you with the skills needed to build a thriving career in data science and analytics. Gain hands-on experience in applying data science to real-world business challenges and prepare to excel as a professional in this rapidly growing field.

Job Assistance

Our program comes with a job assurance that offers you a chance to be placed at over 250 top-tier partner organisations hiring machine learning and artificial intelligence professionals.

Comprehensive Career Support

We offer end-to-end career services, including personalized resume building, professional profile optimization, expert career guidance, job assurance training, and one-on-one counseling sessions to help you secure your ideal role with confidence.

Real-world Projects

Implement what you’ve learned with over 25 real-world projects and case studies specially formulated by industry experts to make you job-ready.


Cutting-Edge Data Science Concepts

Learn the practical applications of concepts like Exploratory Data Analysis, Data Visualization, Machine Learning, Generative AI, Computer Vision, Natural Language Processing, and Time Series Forecasting.

Experience Live, Interactive Learning

Engage in real-time, interactive sessions led by industry experts, where you'll explore advanced data science concepts and their practical applications. Learn, collaborate, and gain hands-on experience to excel in the field.

Learn Through Hackathons

Participate in dynamic hackathons and real-world challenges, sharpening your data science and generative AI skills. Solve complex problems, collaborate with peers, and build a portfolio of projects that showcase your expertise to future employers.


What roles can a person trained in Data Science?

Senior Data Scientist

Understand the issues and create models based on the data gathered, and also manage a team of data scientists.

AI Expert

Build strategies on frameworks and technologies to develop AI solutions and help the organization prosper.

Machine Learning Expert

With the help of several machine learning tools and technologies, build statistical models with huge chunks of business data.

Generative AI Professional

Design and deploy advanced generative models to create innovative solutions, automate processes, and enhance decision-making. Harness the power of AI to drive creativity and transform business outcomes.

Data Analyst Specialist

Analyze and interpret complex datasets to uncover actionable insights, create dynamic visualizations, and deliver data-driven solutions to optimize business performance.

Senior Business Analyst

Extract data from the respective sources to perform business analysis, and generate reports, dashboards, and metrics to monitor the company's performance.

Skills to Master

Python Programming
Data Visualization
Exploratory Data Analysis
Artificial Intelligence
Computer Vision
Natural Language Processing
Data Wrangling and Manipulation
Data Base Management Systems
Data Story Telling
Machine Learning
Prediction algorithms
Generative AI
Time Series Forecasting
Model Building and Fine Tuning
Data Structures & Algorithms

Specializations & Electives Overview


Banking, Finance, & Insurance: Dive into projects like Churn Analysis, Risk-Reward Assessment, Stock Market Analysis, and Fraud Detection to grasp fundamental concepts in BFSI.

Ecommerce & Marketing: Explore Customer Lifetime Analysis, Ad Campaign Evaluation, Market Basket Analysis, and Dynamic Pricing strategies in this field.

Healthcare & Pharmacy: Explore Payer and Provider Analytics, as well as Analytics applied in the Pharmaceutical Industry to gain insights into essential Healthcare principles.

HR & Operations: Engage in Attrition Analysis, Promotion Assessment, Productivity Evaluation, and Resource Optimization to comprehend core BFSI principles in a different context.

Top Data Science Case Studies


Transforming Entertainment with AI-Powered Recommendations

Data science and generative AI have revolutionized the entertainment industry by enabling personalized content delivery. Advanced algorithms analyze user preferences, behavior, and viewing patterns to provide tailored recommendations, boosting user engagement and satisfaction. Generative AI further enhances the experience by creating dynamic content, such as personalized trailers or thumbnails, based on individual tastes.

Revolutionizing Conversational AI

Generative AI is setting new standards in conversational applications by powering advanced chatbots and virtual assistants. These AI models can understand and respond to complex queries, draft content, and provide real-time support across industries like customer service, healthcare, and education. By mimicking natural language, generative AI enhances user interaction and streamlines communication.

Accelerating Innovation in Healthcare with AI

Generative AI and data science are transforming healthcare by streamlining processes like drug discovery and patient care. Predictive models analyze vast datasets to identify patterns, forecast outbreaks, and optimize treatment plans. Generative AI creates synthetic data for training models, simulates molecular interactions, and aids in developing life-saving therapies faster and more efficiently.

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250+

Global Partners

$122,000 PA

Average CTC

$250,000 PA

Highest CTC

50%

Average Salary Hike

Program Fee

$4499.00 US Dollar


13156.00 Dirham


₹299000.00 + 18% GST


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