Data Science Course with Placement and Certification | Digicrome

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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.

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Contact Info

C-108, Second Floor, Block C, Sector 02, Noida, Uttar Pradesh 201301
01203133869
info@digicrome.com
Week Days: 10.30 to 19.00 Sunday: Closed

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Best Data Science Course in India

In Curriculum

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Become an industry-ready Certified Data Science professional through immersive learning of Data Analysis and Visualization, ML models, Forecasting & Predicting Models, NLP, Deep Learning.

In Collaboration With

Silver
Business
Partner

14-06--2025

Next Batch
starts on

11 Months

Live Online Classes

12 Months

Live
Internship

Online

Learning
Format

5000+

Career
Transformed

500+

Hiring
Partners

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Courses Features

Download Brochure

5000

Career Transformed

12 Months

Live Internship

11 Months

Live Online Classes

14-06--2025

Next Batch starts on

500+

Hiring Partners

What Our Program Offers?

Discover the key features and benefits you'll gain from joining our program

12 Month Internship Exp.

12 Month Internship Exp.

Live and Recorded Sessions

Live and Recorded Sessions

Pre-Placement Facility

Pre-Placement Facility

Totally Practical Exposer

Totally Practical Exposer

1:1 Doubt Sessions

1:1 Doubt Sessions

24*7 Career Support

24*7 Career Support

Lifetime LMS Portal

Lifetime LMS Portal

E-Learning Study Material

E-Learning Study Material

90% Placement Rate

90% Placement Rate

Designed by Recruiters

Designed by Recruiters

Interactive E-Learning Material

Interactive E-Learning Material

Global Career Opportunities

Global Career Opportunities

100% Placement Guarantee Aid

100% Placement Guarantee Aid

5-Star Reviews by Students

5-Star Reviews by Students

Globally Recognized Certifications

Globally Recognized Certifications

Personality Development Training

Personality Development Training

All Tools Covered

All Tools Covered

10k+ Learners Placed

10k+ Learners Placed

Get Interview Preparation

Get Interview Preparation

Master Latest Technologies

Master Latest Technologies

Trusted by world's best Organisations

About the Program

Our Post Graduate Program in Data Science with AI will make you the most efficient in the industry, regardless of whichever background you are coming from. You can start here from the beginning to the advanced level. This course is an 11-month data science certification and includes a 12-month internship in the curriculum. You will get hands-on experience with 25+ tools and techniques, including ML, Python, SQL, EDA, and Deep Learning through hands-on projects and under the guidance of seasoned experts.

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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.

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

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.

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

Connect with Experts and Advance Your Career

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

Course Overview

Considering a career in Data Science? Make a strong move by applying to our Online Data Science with AI Course – this PGP-DS (Post Graduate Program in Data Science) is an exclusive program structured by industry experts. Learn Python, Exploratory Data Analysis, Machine Learning, Deep Learning, and more.

Key Course Highlights

Well-rounded Curriculum: Access our carefully curated curriculum, providing a step-by-step learning process. Get a comprehensive understanding of AI, study the interconnected parts of data science and more. Access the Best Data Science Course, teaching all the must-knows—including Python, SQL, Machine Learning, Data Visualization and beyond.
Learning from the best: Get taught by seasoned industry experts moreover our lectures take place using advanced technology ensuring satisfactory experiences. Our program managers remain available for non-curricular queries and course management.
Hands-on Learning: The program includes assignments and projects, which help you test and apply your knowledge constructively. Instructors provide detailed feedback, driving active participation.
Networking Opportunities: Going online does not mean fewer chances to network —- we ensure you connect with peers, mentors, and professionals. You can email, have a voice or video call, whatever satisfies you.
Doubt Clearing Sessions: Whenever in doubt, clear it out with our 1:1 doubt-clearing sessions — be it managerial or related to study material. Reach us out any way: email, voice call, or video call.

Why Choose Digicrome's Data Science with AI Course?

There is a huge need for well-versed Data Science and AI professionals, yet the talent pool of proficient data scientists remains limited.

We have framed the course curriculum for students or professionals from various industries, moreover, we have helped in the career transformation of over 5000+ individuals and we stand among the Data Science Course with Placement.

Our Online Data Science Course is the way to go if you aim to be among the top of the industry. Don't wait—take a step ahead toward the career you’ll love with our Advanced Certification in Online Data Science with AI course, and get experienced through internships and hands-on projects.

Course Includes :

Price
160,000 + GST
US Price
$1846.00
Dubai Price
6779.00AED
Certifications
Yes
Language
English (US)

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Our Course Curriculum

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

1.1 Introduction to Excel for Data Science - Interface, Ribbon, Shortcuts, Navigation, Formatting Data, Number Formats, Conditional Formatting, Basic Data Entry, Sorting, and Filtering
1.2 Basic Mathematical Functions - SUM, AVERAGE, COUNT, MIN, MAX, ABS, ROUND, etc.
1.3 Data Cleaning & Preprocessing - Handling Missing Data, Removing Duplicates & Data Validation, and Text Functions (LEFT, RIGHT, MID, LEN, TRIM, UPPER, LOWER, PROPER, etc.)
1.4 Conditional Logic & Lookup Functions - IF, IFERROR, AND, OR, VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH, etc.
1.5 Basic Visualization - Bar Chart, Line Chart, Pie Chart, etc. 1.6 Introduction to AI-powered Excel Tools (Copilot, AI Insights, etc.)

2.1 Pivot Tables & Data Aggregation - Creating Pivot Tables & Pivot Charts, Advanced Filtering with Slicers & Timelines, Grouping Data, Calculated Fields & Custom Aggregations
2.2 Advanced Statistical & Logical Functions - COUNTIF, SUMIF, AVERAGEIF, STDEV, VAR, CORREL, RANK, PERCENTILE, FREQUENCY, TREND, LINEST, GROWTH
2.3 Automating Data Cleaning with Power Query - Importing Data from CSV, SQL, and Web. Merging & Appending Datasets. Cleaning & Transforming Data for Analysis.
2.4 AI Powered Data Analysis - AI Powered Insights, Automating AI-driven Data Cleaning, AI Generated Forecasting & Trend Analysis

3.1 Advanced Excel Visualizations - Dynamic Charts (Interactive Dropdown Charts), Waterfall Charts, Funnel Charts, Heatmaps. KPI Dashboards Using Custom Formatting
3.2 AI Powered Predictive Analytics - Time Series Forecasting using AI powered tools. AI-based Anomaly Detection & Outlier Identification
3.3 Dashboard Design & Storytelling - Using Slicers, Timelines, Interactive Charts, Creating KPI Dashboards.
3.4 Hands-On Project: Build an AI Powered Sales / Finance Dashboard

4.1 Introduction - What is VBA, Why VBA for Data Science, Understanding MACROS & Recording MACROS, VBA Editor, Modules, and Debugging
4.2 Basic & Advanced VBA Programming - Variables, Loops, Conditional Statements. Writing Custom Functions (UDFs), Automating Data Cleaning & Report Generation
4.3 AI & ML with Excel & VBA - Building AI Powered MACROS for Data Processing, Automating AI Predictions in Excel using VBA

5.1 Cleans newly imported sales/HR/financial data automatically
5.2 Run AI powered Analysis
5.3 Generate a custom AI-driven report with automated insights

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
3.1 Lists - Access, Change, Add, Remove, Loop, Sort, Copy, Join, List Methods, List Comprehension
3.2 Tuples - Access, Update, Unpack, Loop, Join, Tuple Methods
3.3 Sets - Access, Add, Remove, Loop, Join, Set Methods
3.4 Dictionary - Access, Change, Add, Remove, Loop, Copy, Nested Dictionaries, Dictionary Methods
4) Python Conditional Statements - IF, ELIF, ELSE
5) Loops in Python
5.1 For Loops - Introduction, break, pass, Nested Loops
5.2 While Loops - Break, Continue, Else, Conditional Statements in While Loops
6) Python Functions - Creating, Calling, Arguments, Arbitrary Arguments, Keyword Arguments, Arbitrary Keyword Arguments, Positional Arguments, Default Parameters, RETURN, LAMBDA.
7) Python Classes and Objects
7.1 Introduction - Creation, __init__(), __str__(), Object Methods, SELF Statement, Pass Statement
7.2 Inheritance - Creation, Parent Class, Child Class, __init__(), super(), Inheritance Methods
8) Miscellaneous - Datetime, RegEx, String Formatting, TRY/EXCEPT

1 NumPy - Numerical Python
1.1 Introduction - Arrays (Indexing, Dimensions, Slicing, Shape, Reshape, Iteration, Joins, Split, Search, Sort, Filter, Copy Vs View
1.2 Random - Introduction, Pseudo vs True Random, Shuffle and Permutations, Random Distributions (Poisson, Normal, Binomial, etc.)
1.3 Universal Functions - Simple Arithmetic, Rounding Decimals, Log, Summation, Product, Differences, LCM, GCD, Set Operations, Trigonometric and Hyperbolic Functions
2 Data Wrangling using PANDAS
2.1 Introduction to Pandas - Installation (using PIP) and Import
2.2 Pandas Series and DataFrames - Difference, Labels, Key/Value Objects
2.3 Pandas DataFrames Tutorial - View, Info, Description, Location of Rows and Columns, Named Indexes, CSV/EXCEL Files
2.4 Pandas DataFrames Advanced Tutorial - Cleaning of Data, Handling Null Values (Dropna/Fillna), Handling Duplicate Values, Handling Data in Wrong Format
2.5 Plotting of Data Using Pandas
3 Data Visualization Using Matplotlib and Seaborn
3.1 Introduction to Data Visualization - Installation (using PIP) and import of Matplotlib and Seaborn, Basics of Plotting (Labels, X Axis, Y Axis, Headings)
3.2 Matplotlib and Seaborn - Line Plots, Bar Graphs, Scatter plots, Pie Charts, Heatmaps for Correlation, etc.
3.3 Exploring Outliers and Data Distributions - Boxplots, Histograms, KDE Plot, Violin Plot, Pair Plot, etc.
3.4 Advanced Plotting Tutorial - Adding a third Axis on a 2D plot, Fonts, Styles, Subplots, Axis, Grids, Texts on Plots

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

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

3.1 Installation, Setup, Importing Files, Connecting SQL Databases and Cloud Services, Direct Query Methods in Power BI
3.2 Power Query - Data Cleaning and Transformation (Handling Missing Values and Duplicates, Formatting of Data)
3.3 Data Modeling - Calculated Columns, Managing Data Models, Creating Relationship between two tables
3.4 Basic and Advanced Data Analysis Expressions (DAX) Tutorial
3.5 Basic and Advanced Visualizations - (Basic - Bar Charts, Pie Charts, Matrices, Maps, etc.), (Advanced - Custom Visualizations, Slicers, Filters, Waterfall Charts, Funnel Charts, Gauge Charts, etc.)
3.6 Automated Quick Insights and AI Visuals, Dashboard Designs in Power BI
3.7 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 Descriptive Statistics - Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers), Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom), Skewness and Kurtosis
2.2 Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling
2.3 Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distribution and T-test, Binomial Distribution, Chi-Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution
2.4 Correlation Coefficient, Coefficient of Determination, Simple Linear Regression in Statistics

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

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

5.1 Introduction to Unsupervised Learning
5.2 Clustering Methods - KMeans, Hierarchical, Model-Based Clustering, DBSCAN Clustering, Anomaly Detection using Gaussian Mixture Models
5.3 Dimensionality Reduction Using Principal Component Analysis
5.4 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 Regularization

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 - Transposed Convolutions, Unsampling Techniques, Residual Connections (Skip Connections)
1.4 Types of CNNs - 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 - 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 Composition, 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)

1. NLP Fundamentals - Introduction to NLP (Definition & Importance), Applications (Chatbots, Sentiment Analysis, Machine Translation, etc.)
2. Text Processing - Tokenization (Word & Sentence), Stopword Removal, Stemming & Lemmatization, Part-of-Speech (POS) Tagging, Named Entity Recognition (NER), Text Normalization (Lowercasing, Removing Special Characters, etc.)
3. Word Representation (One Hot Encoding, Bag of Words, Term Frequency - Inverse Document Frequency [TF-IDF], Word Embeddings (Word2Vec, GloVe, FastText)
4. Popular NLP Models - BERT, GPT, T5, ZLNet, CLIP, Fine Tuning Pretrained Models
5. Text Generation - Autoregressive Models (GPT), Masked Language Models (BERT), Summarization (Extractive & Abstractive)
6. Speech-to-Text & Text-to-Speech - Automatic Speech Recognition (ASR), Tacotron, WaveNet, FastSpeech
7. Conversational AI & Chatbots - Rule-Based Chatbots, Retrieval-Based Chatbots, Generative Chatbots 8. Miscellaneous - Model Compression & Quantization, OpenAI API, Ethics & Bias in NLP (Fairness in AI & Reducing Bias in Language Models

1.1 Relevance of Generative AI - Dimensionality Reduction, Data Denoising, Anomaly Detection, Image Generation, Feature Extraction, Latent Space Manipulation, Data Generation
1.2 Training of Autoencoders, Architecture - Encoder, Decoder and Latent Space (Bottleneck)
1.3 Types of Autoencoders - Vanilla Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Convolutional Autoencoders, Variational Autoencoders, Contractive Autoencoders, Stacked Autoencoders, Adversarial Autoencoders
1.4 Advanced Architectures - Beta-VAE, Conditional Autoencoder, Sequence to Sequence Autoencoder, and Graph Autoencoder

2.1 Applications of GANs in Generative AI - Image Generation, Video Generation, Text to Image Synthesis, Music and Audio Generation, Style Transfer
2.2 Architecture - Generator, Discriminator, Adversarial Loss
2.3 Types of GANs - Vanilla GANs, Deep Convolutional GANs, Conditional GANs, Wasserstein GANs, Progressive Growing GANs, Cycle GANs, Style GANs, BigGANs, Pix2Pix.
2.4 Challenges - Mode Collapse, Non-Convergence, Vanishing Gradients
2.5 Advanced Concepts (Attention GANs, 3D GANs, Speech GANs, Multi-Model GANs), Metrics (Inception Score, Fréchet Inception Distance, Perceptual Path Length)
2.6 Metrics - Inception Score, Fréchet Inception Distance, Perceptual Path Length
2.7 Key Differences between Autoencoders and General Adversarial Networks

3.1 Generative AI in NLP - Text Generation (GPT Models), Code Generation (Codex - Github Copilot, Code Llama), Chatbots: RAG (Retrieval-Augmented Generation), Fine Tuning LLMs
3.2 Generative AI in Computer Vision - Text-to-Image (DALL-E, Midjourney, Stable Diffusion), Image Super-Resolution (ESRGAN), Deepfake Generation (Face Swap, Video Synthesis)
3.3 Generative AI in Music & Audio - WaveNet (Google's Speech Synthesis), Jukebox (Open AI), Audio-to-Text Models (Whisper, DeepSpeech)
3.4 Generative AI in Healthcare & Science - Drug Discovery (AlphaFold), Medical Image Generation (AI-assisted X-ray / MRI Processing)
3.5 Generative AI in Finance & Business - Synthetic Data Generation, AI-Powered Content Creation

1.1 Answering complex user queries using Retrieval-Augmented Generation (RAG).
1.2 Generating high-quality images using a prompt.
1.3 Deploying the application for real-world use

2.1 Text Query Answering Module (using RAG with Transformers).
2.2 Creative Writing Module (text generation using GPT or custom Transformer models).
2.3 Image Generation Module (using Diffusion Models like Stable Diffusion or DALL·E).
2.4 Unified Frontend Interface for multi-modal interaction.
2.5 Backend API for serving models.
2.6 Deployment: Cloud-based or on-premise.

3.1 Backend - FastAPI, Flask, Hugging Face Transformers, PyTorch, Tensorflow, OpenCV, Lancedb for Vector Search
3.2 Frontend - Gradio, Streamlight
3.3 Deployment - AWS, GCP, Azure, Docker, Kubernetes

4.1 A fully functional multi-modal AI assistant with text and image generation capabilities.
4.2 A deployed system accessible via a web interface.
4.3 A scalable architecture ready for real-world applications.

5.1 Mastery of Retrieval-Augmented Generation (RAG) for text generation.
5.2 Hands-on experience with text-to-image generation.
5.3 Ability to fine-tune transformer models for creative writing and specific tasks.
5.4 Development of full-stack AI applications with backend and frontend integration.
5.5 Deployment of models using Docker and cloud platforms.
5.6 Knowledge of scalable AI systems with Kubernetes.
5.7 Practical experience in data preprocessing for text and image tasks.
5.8 Use of evaluation metrics for assessing generative models.
5.9 Documentation of systems and API integration for real-world applications.
5.10 Exposure to AI ethics, deployment best practices, and model security.

1

Internship Program

Get Industry Ready

Identify Your Specialty

Learn Data Tools

Real Data Exposure

Learn to Manage Time

Strengthen Your Resume

Expand Your Network

Build a Strong Portfolio

Get Experts Feedback

Get Internship Certificate

2

Soft Skills Program

Effective Communication

SWOT Analysis (Self-Assessment)

Email Writing for Corporates

Group Discussion Techniques

Negotiation Skills

Resume Building & Vetting

Grooming Tips & Professional Etiquette

Interview Tips & Techniques

Mock Interviews

Time Management

Introduction to Soft Skills

Languages and Tools Covered

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.

Predictive Customer Analytics for Retail

Build a machine learning model to predict customer purchase behavior and segment high-value customers. This project helps businesses improve retention and personalize marketing.

Skills Required:
Python, Pandas, Scikit-learn, Logistic Regression, Clustering, SQL, Tableau/Power BI

Real-Time Sales Dashboard Using Tableau & Power BI

Create a dynamic dashboard using Tableau and Power BI that visualizes key performance indicators for an e-commerce company, including profit trends, top-performing products, and customer demographics.

Skills Required:
Data Wrangling, Data Visualization, Tableau, Power BI, DAX, SQL

AI Resume Screening System

Develop an AI-powered system that screens resumes against job descriptions using NLP and recommends top candidates. Ideal for automating HR workflows.

Skills Required:
Python, NLP (spaCy, NLTK), TF-IDF, Word Embeddings, Flask/Streamlit

Credit Risk Assessment Using Machine Learning

Build and train models that classify loan applicants as high or low risk based on historical financial data, using ensemble techniques and hyperparameter tuning.

Skills Required:
Logistic Regression, Random Forest, XGBoost, GridSearchCV, Pandas, Scikit-learn

Stock Market Price Forecasting Using Time Series

Implement time series models to forecast stock prices and identify trends using historical financial datasets, incorporating statistical modeling and Python libraries.

Skills Required:
ARIMA, Facebook Prophet, Python (Statsmodels, Pandas), Data Cleaning, Visualization

Deep Learning-Based Medical Image Classification

Train a CNN to classify X-ray or MRI images into diagnostic categories. This project demonstrates real-world applications of computer vision in healthcare.

Skills Required:
TensorFlow/Keras, CNNs, Image Preprocessing, Data Augmentation, Transfer Learning

Conversational AI Chatbot with RAG and GPT

Develop a multi-purpose chatbot using Retrieval-Augmented Generation (RAG) with Transformer models to answer complex queries with context-aware responses.

Skills Required:
Transformers (Hugging Face), GPT, Langchain, RAG, FastAPI, Vector Databases

Text Summarizer and Sentiment Analyzer for News Articles

Build an NLP pipeline to summarize news articles and classify their sentiment (positive, neutral, negative). Ideal for media monitoring or user review analysis.

Skills Required:
BERT, Text Summarization, Sentiment Analysis, Transformers, Python

Generative AI for Image Creation Using GANs

Train a Generative Adversarial Network to create realistic images or artistic styles. Explore applications in marketing, content creation, and design.

Skills Required:
TensorFlow, PyTorch, GANs (DCGAN, CycleGAN), Image Generation, CNNs

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Moments of Honour

In our EdTech journey of more than a decade, we have received numerous awards.
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Successpreneur Award 2023 being the best analytics EdTech business
Most Promising Digital Learning Platform 2023 for being one of the most promising digital learning platforms

Our Placed Learners In Different Big Firms

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Santosh Singh

Mansi Dwivedi

Asha Sandeep

Avantika Verma

Shivani Kiran

Manohar Ramchandra

Manohar Ramchandra

Mukesh Ghodeswar

Mukesh Ghodeswar

Ritika Sadhwani

Ritika Sadhwani

Tanuja Sharma

Sanchita Biswas

Sanchita Biswas

M YashWanth Kumar

M YashWanth Kumar

Bhavesh Baghele

Bhavesh Baghele

Ranjan Adhav

Chandan Giri

Himanshu Saxena

Himanshu Saxena

PANKAJ SINGH

Kapil Kumar

Ankur kumar

Nandini Verma

Sourav Bajaj

Bhavesh Baghele

Bhavesh Baghele

Rechal Waghmare

Rechal Waghmare

Aditi Gupta

Happy Learners

20,000+

Average Rating

4.8 ★

Average Salary Hike

80%

Average Package

₹ 8 LPA

Our Case Studies

Insights Of All The Learner Recent Learners

Create a Predictive Dashboard

Help businesses make informed decisions by creating a predictive dashboard that analyzes past data, forecasts future trends, and enables automatic pattern detection.

Interpret Data with Tableau

Use Tableau to create effective dashboards showing customer behavior, trends, or retail sales, helping any business clearly understand data and make quick, accurate decisions.

Social Media Sentiment Analysis

Analyze social media posts using NLP & set a pipeline for gathering, processing, and categorizing public sentiment as positive, negative, or neutral through custom text analysis.

Create Multi-Model AI Assistant

Develop a multi-modal AI assistant that engages through voice/text, identifies faces, and summarizes documents by incorporating natural language processing with computer vision.

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.

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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

1

Career Consultation

Assess eligibility

2

Personalized Guidance

Acceptance letter

3

Easy Registration

Pay booking amount

4

Start Upskilling

Access curriculum

5

Ongoing Support

Mentorship & guidance

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Our Course Comes with Offerings

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

Get Lifetime Access to LMS

Get Lifetime Access to LMS

With lifetime LMS access, study beyond the course duration, revisit lessons, revise concepts, and keep your knowledge fresh anytime you need it in your way.

Live Interactive Online Sessions

You get weekend classes held by industry experts as per your set schedule. These classes aren’t basic, here you can share ideas, clear doubts, collaborate with peers, and achieve a once-in-a-lifetime experience.

Live Interactive Online Sessions

Regular Evaluations for better learning

Regular Evaluations for better learning

We analyze your progress through continuous assessments and personalized feedback on how you're doing, helping you adjust your learning approach and improve. These evaluations test your knowledge and help you do better throughout the course and after.

Personalized Doubt Sessions

Any of your Doubts can be cleared out personally by your mentors every week; just reach out without hesitation to have a 1:1 session. We ensure no learner is left behind and no question troubles your mind.

Personalized Doubt Sessions

Hands-On Projects & Case Studies

Hands-On Projects & Case Studies

Whichever course you choose—be it Data Science, AI, Data Analytics, or any other—all the projects you get will be based on real problems, so you never feel unfamiliar with challenges you may face while working on job projects.

Focused Learning Tracks

Pick your course based on what you want to become and gain expertise. You will gain knowledge, tools, and understanding specific to your field, whether it’s tech, business, or creative areas.

Focused Learning Tracks

Interview Preparation

Interview Preparation

Get ready and confident for job interviews with expert counseling and mock interview rounds. Get placed fast as you learn to market your skills and rightly handle complicated questions.

Our FAQs

Imperative FAQs About Us!

Data Science creates meaning from various data types, while AI uses that information to improve itself and ease decision-making. Together, they help businesses achieve success, improve customer experiences, and stay ahead with well-informed decisions.

No, you can apply without programming knowledge. We teach everything from the beginning, starting with Python basics to advanced AI and Machine Learning.

No previous experience is required. Basic computer skills, English comprehension, and an interest in data are enough. Anyone, be it a 12th pass, a graduate, or a postgraduate from any field, can apply.

You’ll work on real-life projects like AI chatbots, image classification, sentiment analysis, recommendation systems, and deep learning models to help you develop understanding and showcase your skills.

Our Data Science and AI course can be completed within 11 months (200+ hours), and the course curriculum includes a 12-month internship starting from the 2nd month of your course.

Yes, you will receive a program completion certificate from Digicrome, along with specialized certificates from our course collaborators, Microsoft and IBM, confirming your Data Science & AI skills.

Yes, this is a 100% online course in Data Science and AI, taught in English, with LMS access, personal mentorship, and a flexible schedule.

The AI & Data Science sector is growing significantly, with over 2.3 million job openings by 2027 and merely 1.2 million professionals. There is a huge skill gap, making it the right time to upskill.

Digicrome throughout the course provides support through personal mentoring, weekly doubt sessions, live classes, resume & interview preparation, complete job assistance, lifetime LMS access, soft skills training, project guidance, and more.