DS 360 Brochure ( Complete Data Science ) W.E

1. SKILL STACK HUB STACK YOUR SKILLS, SCALE YOUR SUCCESS Become Data Scientist Duration: 52 Weeks ( Weekend batch) Master Data Science with Expert Mentorship Your Path to Analytics Excellence Starts Here

2. STACK YOUR SKILLS, SCALE YOUR SUCCESS 01 Introduction to Data Science CONTENTS 02 The Reach of Data Science 03 Program Structure and Highlights 04 Your Learning Journey 05 Modular Curriculum Overview 06 Tools and Technologies You’ll Learn Certification of Completion 07 08 Curious? Let’s Break It Down 09 Ready to Begin? Enroll Today www.skillstackhub.com

3. Introduction to Data Science Data Science is the discipline of extracting knowledge and insights from raw data using analytical and computational techniques. Let’s understand this through the most popular app of all time Swiggy We all love a good meal delivered right on time, Don’t We? Swiggy uses Data Science and AI-powered algorithms to predict what you're likely to crave next based on your past orders, delivery time preferences, cuisine choices, and even your location. With this data, Swiggy personalizes your homepage, optimizes delivery routes, and ensures your favourites are just a tap away. How Swiggy uses Data Science? Swiggy leverages Data Science to make your food experience smarter and faster. By applying Machine Learning, Predictive Modelling and Data Mining, Swiggy understands what you’re likely to order, when you’ll order it and which restaurant is best suited to serve you. With Data Science, Every order is not just delivered—IT’S PREDICTED.

4. This helps platforms like Swiggy identify food trends, forecast demand and make smarter Data Driven Decisions to improve user experience and delivery e?ciency. Data Scientists are key drivers in today’s Data-Centric World, Transforming complex information into actionable insights that power innovation and informed decisions. If decoding patterns and solving problems with Data excites you, Then this is your moment to transform information into impact and shape the future with Data Science.

5. The Reach of Data Science We are Living in the world of data Do you Know It’s estimated that downloading all the data available on the internet would take over 180 million years. A powerful reminder that data is the fuel of the digital age, and data scientists are in the driver’s seat. Leading organizations across industries from tech giants to healthcare and finance rely on Data Science professionals to uncover insights and power smarter decisions.

6. 15 LPA 1 Million By 2026, India is projected to need over 1 million data science professionals, driven by rapid digital transformation across industries. 92% Data Scientists in India earn an average of 10–15 LPA annually, as per the latest insights from Glassdoor and PayScale. In 2023, nearly 92% of organizations saw measurable returns from their data science and analytics e?orts, indicating that data-driven strategies are proving highly valuable across industries. 6200+ Startups India is home to over 6,200 AI-focused startups as of mid-2025, making it one of the world’s most vibrant innovation ecosystems in data analytics and AI. Indian Govt Initiative The Government of India launched NDAP to make public data accessible and promote data-driven decision-making across sectors. 182 Billion US Dollars By 2028, India’s analytics market is projected to reach ≈ US $182 billion, growing from about US $62.5 billion in 2023 In a world powered by data, Data Science isn't just a career, It's a Superpower. From predicting trends to solving real-world problems, Data Scientists are shaping the future across every industry. With DS 360, you're not just learning a skill, You're unlocking endless opportunities. ₹10-25 LPA Data Roles with their Average Salary ₹10-18 LPA ₹10-15 LPA ₹7-16 LPA ₹6-14 LPA ₹6-12 LPA ₹5-8 LPA Junior Analyst BI Analyst Data Engineer Big Data Engineer Data Analyst Analyst Consultant Data Scientist

7. Program Structure and Highlights Build Your Future 100% Project-Based Cohort Designed by Industry Leaders (LEARN→ ENGAGE→ APPLY→ PRESENT) to Make You 100% Job-Ready! Engage in a hands-on learning journey where you’ll tackle real-world projects, decode complex data problems and gain mastery through case studies, All guided by Top Industry Experts. Final Prep Sprint (PRESENT) Interview & Portfolio Bootcamp Mock interviews with rotating mentors (LeetCode-lite + case studies + behavioural rounds). Lightning-talk rehearsal: 5 slides in 5 minutes, streamed to mentors for critique. Skill Checkpoints (LEARN) Micro-Lessons + Quizzes 30 min video + ready-to-run notebook before each live class. 3–5 quiz questions Expert Sessions (ENGAGE) Data-Studio Workshops 2-hour live masterclasses, 3×/week, led by seasoned Data Science practitioners. Interactive polls, live coding demos and 15 min of “Open Mic” doubt clearing. Ultimate Project Showcase Capstone Demo-Day Collaborate on a full-scale analytics solution end-to-end. Earn badges and GitHub pull-request endorsements Collaboration Labs (APPLY) Peer Hackathons & Code-Rallies: Daily 15-min Code Rallies (After completion of Programming languages) Micro-Hackathon every 4th Friday (3h, teams of 3) on a surprise dataset Data Escape Room Puzzle chain each month, unlocking clues via SQL or Python code under the clock. 1:1 Mentorship – On-Demand, 24×7 Get round-the-clock access to top mentors for: Personalized learning roadmaps Code & portfolio reviews Interview prep deep dives Quick doubt resolution No waiting. No bottlenecks. Just expert support—whenever you need it.

8. Your Learning Journey Program Runway Module Kicko? & Onboarding Milestone 1 Module 1 Excel Essentials: From Spreadsheets to Superpowers From simple formulas to stunning charts—Excel isn't just a tool, it's your data playground. Learn how to organize, analyse, and visualize with ease. No jargon. Just real skills that stick Milestone 2 Learn Excel to truly excel. Module 2 The SQL Way: From Raw Data to Real Answers Learn how to talk to databases like a pro—write smart queries, master joins, and transform messy data into clear insights. It's not just code, it’s storytelling with data. SQL speaks for your data. Milestone 3 Module 3 Python Prodigy: From Basics to Brilliance Python isn’t just popular—it’s powerful. From cleaning data to building logic, it’s the go-to language for turning raw numbers into real insights. Simple to learn, yet limitless in what you can do. Milestone 4 Start coding in Python Module 4 Pandas Powerhouse: From Dataframes to Insights Think of Pandas as your data sidekick—organizing , cleaning, and crunching numbers without breaking a sweat. From messy spreadsheets to meaningful insights, Pandas helps you make sense of it all with just a few lines of code. Less wrangling, More understanding

9. Milestone 5 Module 5 Stats & Probability Prodigy: From Fundamentals to Forecasts Go beyond the buzzwords—learn how numbers actually make decisions. From probability to predictions, explore stats, machine learning, and AI in a way that clicks. Build models, test ideas, and apply them to real-world problems with confidence. Milestone 6 Turn data into decisions that matter. Module 6 Data Visualization Decoded: Basics to Brilliant Dashboards Bring your data to life—because numbers alone don’t tell the whole story. Learn how to craft clear, compelling visuals using tools like Power BI and Tableau. From dashboards to data storytelling, make every chart count. Turn raw data into visual impact. Milestone 7 Module 7 ML Maverick: From Basics to Smart Predictions Step into the world of Machine Learning—where data learns, adapts, and predicts. Understand the core types: Supervised, Unsupervised, and Reinforcement Learning. You’ll prep data, build models, and solve real problems through prediction , classification, and clustering. Milestone 8 No flu?—just hands-on ML that makes sense Module 8 Deep Learning Dynamo: Neural Networks to Real-World AI Dive into deep learning and uncover how machines truly “learn.” Build neural networks from scratch using PyTorch, explore CNNs for images, RNNs for sequences, and unlock the power of Transformers for language tasks and transfer learning. Bring real-world AI to life.

10. Milestone 9 Module 9 Natural Language Processing Learn how to clean, prep, and represent text so machines can truly understand it. From basic preprocessing to word embeddings, and all the way to using powerful Hugging Face models—you’ll build NLP solutions that read between the lines. Milestone 10 Turn raw text into smart insights Module 10 Computer Vision: Teaching Machines to See Start by prepping images—annotate, resize, and augment like a pro. Then dive into building models that can classify objects, detect patterns, and even segment images pixel by pixel. From raw images to real insights Milestone 11 Module 11 GenAI Genius: From Prompts to Possibilities Step into the world of Generative AI and learn how to guide powerful language models with smart prompting and fine-tuning. Discover how GenAI is transforming content, automating tasks, and unlocking new creative and business possibilities. No flu?—just hands-on ML that makes sense Capstone Project 4 Weeks Job Ready Launchpad Skill Stack Hub Placement Assistance

11. Modular Curriculum Overview Milestone 1 Excel Essentials: From Spreadsheets to Superpowers Duration: 3 Weeks Go beyond basic spreadsheets. Learn to use Excel’s formulas, pivot tables, and dashboards to turn raw data into smart, actionable insights. Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Basic Excel Advanced Excel Introduction to Excel for Data Analysis Working with Rows, Columns & Cell Formatting Sorting & Filtering Data Basic Formulas & Functions Relative and Absolute Cell Referencing Data Validation & Drop-down Lists Basic Charts & Visual Representations Introduction to Pivot Tables Basic Error Handling in Formulas Printing & Page Setup Advanced Formula Writing Techniques Lookup & Reference Functions Text Functions for Data Cleaning Advanced Pivot Tables & Pivot Charts Logical & Statistical Functions Excel Data Tables & What-If Analysis Working with Named Ranges & Dynamic Lists Automating Tasks with Macros (Intro to VBA) Creating Interactive Dashboards Importing & Exporting Data in Excel Excel & Business Intelligence Foundations Case Study & Project: Sales Performance Tracker for a Retail Chain Scenario: You're working with a retail company to help track monthly and regional sales performance. Deliverables: Clean and organize raw Excel data. Build interactive Pivot Tables and conditional formatting for KPIs. Design charts and dashboards showing trends in revenue, category sales, and store performance.

12. Milestone 2 The SQL Way: From Raw Data to Real Answers Duration: 5 Weeks Learn to design e?cient databases, write powerful SQL queries, and create systems that turn data into action. Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Basic SQL Advanced SQL Introduction to Databases & Setup Visualizing Data with ER Diagrams Data Structuring with Normalization Building Reliable Databases with ACID Exploring SQL Data Types Designing Tables with DDL Commands Managing Table Data with DML Controlling Access with DCL Handling Transactions with TCL Maintaining Data Accuracy with Constraints Mastering SQL Operators Powerful Querying with SQL Clauses Mastering SQL Joins Mathematical Functions in SQL Data Type Conversion Functions Essential Utility Functions Conditional Logic in SQL Date & Time Handling Numeric Processing Functions String Manipulation Functions Working with Subqueries Ranking & Window Functions Connecting SQL with Python SQL for Data Analytics Case Study & Project: E-Commerce Customer Insights via SQL Scenario: The marketing team wants insights into customer purchase behavior, top-selling products, and churn. Deliverables: Use JOINs across customers, orders, and product tables. Write queries for RFM segmentation, average purchase cycle, and high-value customer identification. Export result sets for visualization or further analysis.

13. Milestone 3 Python Prodigy: From Basics to Brilliance Duration: 5 Weeks Learn Python syntax, structures, and advanced features while strengthening your logic-building skills. Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Python Advanced Python Smart Coding with List Comprehensions Working with Files and Data Storage Debugging Like a Pro Object-Oriented Programming with Classes Quick Functions: Lambda, Map & Filter Text Pattern Matching with Regular Expressions Managing Libraries with PIP Automating Excel Tasks with Python Advanced Flow Control: Iterators & Generators Reusable Code with Decorators Object Serialization with Pickle Integrating JSON for Web & APIs Why Python? Setting Up Your Python IDE Writing Your First Program – Hello World Variables & Naming Rules Working with Strings Understanding Lists Introduction to Tuples Using Sets in Python Dictionaries – Key-Value Data Pairs Conditional Statements Loops – For & While Built-in Functions – Numbers & Math Creating Your Own Functions Modules and Packages Common Python Errors Python for Data Analysis Case Study & Project: Movie Revenue Analysis Using Python Scenario: You're hired to analyze movie trends—genre-wise earnings, IMDb ratings, and runtime distributions. Deliverables: Use Pandas and NumPy for data wrangling and descriptive analysis. Apply groupings, filtering, and statistical summaries Visualize outcomes using Matplotlib and Seaborn..

14. Milestone 4 Pandas Powerhouse: From Dataframes to Insights Duration: 4 Weeks Master data handling with Pandas "From working with Series and Data Frames to creating pivot tables and analyzing time-based data.” Live Sessions Projects Cheat Sheets Quizzes Module 1 Pandas Getting Started with Pandas Working with Series: One-Dimensional Data Exploring DataFrames: The Core Table Format Combining DataFrames: Merge, Join & Concat Grouping & Aggregating Data Creating Pivot Tables for Data Summaries Handling Date & Time Data Hands-on DataFrame Manipulation Practice

15. Milestone 5 Stats & Probability Prodigy: From Fundamentals to Forecasts Duration: 4 Weeks Learn probability, counting, and random variables Then apply them using NumPy, distributions, and real-world simulations. Dive into stats with hands-on hypothesis testing Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Statistics & Probability with Numpy- Basic Why Probability Matters in Data Science Understanding Samples, Outcomes & Events The Building Blocks: Axioms of Probability Bayes' Theorem & Total Probability Introduction to Random Variables Discrete Probability Distributions Expected Value & Variance: Measuring Uncertainty Continuous Probability Distributions Sampling from Distributions Simulation with NumPy Statistics & Probability with Numpy - Advanced Understanding Samples vs. Populations Central Limit Theorem Explained Chi-Square Distribution & Its Use Cases Estimating Parameters: Point vs. Interval Maximum Likelihood Estimation (MLE) Confidence Intervals with Unknown Variance Working Examples of Estimators Introduction to Hypothesis Testing One-Tailed & Two-Tailed Tests Types of Errors & Decision Making Time Series Forecasting Case Study & Project: Sales Forecasting for a Retail Chain Scenario: You're working with a retail chain to predict monthly sales and inventory requirements. Deliverables: Use ARIMA/SARIMA or Prophet to forecast future sales Compare model accuracy over training and validation sets. Visualize trend, seasonality, and forecast intervals.

16. Milestone 6 Data Visualization Decoded: Basics to Brilliant Dashboards Duration: 5 Weeks Create powerful visuals with charts and custom styling, then use Power BI and Tableau to build dashboards and apply data analytics Live Sessions Projects Cheat Sheets Quizzes Module 1 Data Visualization using Python Module 2 Data Visualization Using Power BI Module 3 Data Visualization Using Tableau Reading & Parsing Complex JSON Data Styling & Formatting Tabular Outputs Exploring Distributions with Histograms Summarizing with Box Plots Quick Recap: Visualization Basics Pie & Donut Charts for Proportional Data Stacked & Relative Bar Charts Stacked Area Plots for Trend Analysis Scatter Plots for Relationship Mapping Standard & Grouped Bar Plots Continuous Variable Comparison Plots Line Plots for Time Series Data Getting Started with Power BI Importing, Cleaning & Filtering Data Creating Basic Visualizations Designing Reports in Power BI Interactive Dashboards & Publishing Introduction to Tableau Interface Connecting & Managing Data Sources Visual Analytics with Tableau Applying Basic Predictive Features Building & Publishing Dashboards Data Visualization with Tableau Case Study & Project: Executive Sales Dashboard for a Global Company Scenario: The CEO needs an interactive Tableau dashboard to track global product sales and profits across regions. Deliverables: Connect and blend multiple data sources. Create KPIs, maps, and visual filters using Tableau. Add interactivity with actions and dashboards for category drilldowns.

17. Milestone 7 ML Maverick: From Fundamentals to Smart Predictions Duration: 9 Weeks Learn core ML types: Supervised, Unsupervised and reinforcement while prepping data and building models for prediction, classification and clustering Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Module 3 Introduction to Machine Learning Machine Learning: Data Collection Machine Learning: Data Cleaning & Pre-Processing Cleaning Data for Accuracy Transforming Data for Modeling Feature Engineering Essentials Handling Imbalanced Data Introduction to Machine Learning Types of Learning in ML Real-World Applications of ML The Machine Learning Workflow Types of Data: Structured vs. Unstructured Modern Data Collection Methods Ethics in Data Acquisition Module 4 Module 5 Machine Learning: Model Building (Supervised Learning) Getting Started with Supervised Learning Predicting with Linear Regression Binary & Multi-Class Classification with Logistic Regression Decision Trees for Transparent Modeling Support Vector Machines (SVM) Naive Bayes Classifier Boosting with XGBoost Instance-Based Learning with K-Nearest Neighbors (KNN) Forecasting with ARIMA loying models. Module 6 Machine Learning: Model Building (Unsupervised Learning) Machine Learning: Exploratory Data Analysis Visualizing Data E?ectively Analyzing Data at Multiple Levels Understanding Distributions & Relationships Detecting Patterns & Trends in Data Evaluating Feature Importance Ensemble Learning with Random Forests Introduction to Unsupervised Learning K-Means Clustering: Grouping Similar Data Hierarchical Clustering: Building Cluster Trees DBSCAN: Density-Based Clustering Principal Component Analysis (PCA): Dimensionality Reduction

18. Module 7 Module 8 Machine Learning: Model Evaluation Introduction to Reinforcement Learning Introduction to Reinforcement Learning Evaluating Classification Models Introduction to Model Evaluation Evaluating Regression Models Module 9 Module 10 Machine Learning: Model Deployment Machine Learning: Hyper Parameter Tuning Saving & Reusing Trained Models Preparing Models for Production Introduction to MLflow for Model Management Model Monitoring & Performance Tracking Introduction to Hyperparameter Tuning Randomized Search Optimization Grid Search Strategy Bayesian Optimization Cross-Validation Techniques Early Stopping in Model Training Machine Learning Foundations Case Study & Project: Credit Risk Prediction Model Scenario: A fintech company needs to predict whether a loan applicant will default or not. Deliverables: Clean and preprocess data (missing values, encoding, scaling). Train models like Logistic Regression and Decision Tree. Evaluate performance with accuracy, confusion matrix, and ROC curve. Advanced Machine Learning & Model Deployment Case Study & Project: Customer Lifetime Value Prediction with Model Deployment Scenario: An e-commerce company wants to forecast customer lifetime value (CLTV) and integrate predictions into a dashboard. Deliverables: Use regression models or XGBoost to estimate CLTV. Build and test API using Flask or FastAPI. Deploy model and expose results via Power BI or Tableau with REST API data pull.

19. Milestone 8 Deep Learning Dynamo: From Neural Nets to Real-World AI Duration: 3 Weeks Master deep learning using PyTorch Build neural networks, work with CNNs for images, RNNs and LSTMs for sequences and explore Transformers for NLP and transfer learning Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Module 3 Deep Learning with Pytorch: NN & ANN Why Neural Networks? Building Blocks of Neural Networks Feedforward Neural Networks Explained Gradient Descent & Learning Rules Training Neural Networks Avoiding Overfitting in Neural Models PyTorch Fundamentals Building the Training Pipeline Implementing Regression Models Working with Datasets in PyTorch Advanced Training Concepts Deep Learning with Pytorch: CNN Deep Learning with Pytorch: RNN Getting Started with CNNs Image Filters and Feature Extraction Understanding the Convolution Layer Pooling Layers for Dimensionality Reduction Understanding RNN Architecture Language Modeling with RNNs Text Generation with RNNs Limitations of Traditional RNNs Module 4 Module 5 Deep Learning with Pytorch: LSTM Deep Learning with Pytorch: Transformers & GAN Introduction to Transformer Models Understanding Self-Attention Mechanism Encoder Module: Context Extraction Decoder Module: Generating Output Sequences Sequence-to-Sequence Modeling Transfer Learning with Pre-trained Models Enhancing Memory in RNNs: Introduction to LSTM Real-World Applications of LSTM Models Challenges and Limitations of LSTM

20. Milestone 9 Natural Language Processing (Text Processing) Duration: 4 Weeks Prep text with preprocessing and embeddings, then use Hugging Face models to tackle advanced NLP tasks easily and accurately. Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Module 3 Natural Language Processing (Text Processing) Natural Language Processing (Text Processing) Natural Language Processing (Text Processing) Breaking Text into Tokens Text Normalization Techniques Removing Irrelevant Words Root Word Extraction: Stemming & Lemmatization Text Representation with Bag- of-Words TF-IDF: Term Frequency–Inverse Document Frequency Word Embeddings for Semantic Understanding Sentence-Level Embeddings Text Classification with Transformers Language Translation Made Easy Contextual Question Answering Summarizing Long-Form Content Creative Text Generation Natural Language Processing (NLP) Case Study & Project: Sentiment Analysis of Product Reviews Scenario: A consumer electronics brand wants to analyze customer feedback from e-commerce reviews. Deliverables: Clean and tokenize review text using NLTK or spacy. Use TF-IDF and logistic regression to classify sentiment. Present findings with word clouds and sentiment distribution charts.

21. Milestone 10 Computer Vision: Image Pre-Processing Duration: 7 Weeks Learn image preprocessing with annotation, resizing, and augmentation, then build models for classification, detection, and segmentation to help machines understand visual data Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Module 3 Computer Vision: Image Pre-Processing Computer Vision: Image Classification Computer Vision: Object Detection Image Annotation Essentials Data Augmentation Strategies Image Normalization Techniques Resizing for Model Compatibility Foundations of Convolutional Neural Networks (CNNs) Deep Learning with Residual Networks (ResNet) Multi-Scale Learning with Inception Networks Lightweight Models with MobileNets Scalable and E?cient Models with E?cientNet Region-Based Detection with Faster R-CNN Real-Time Detection using YOLO Pixel-Level Detection with Mask R-CNN Single Shot Detection with SSD

22. Milestone 11 GenAI Genius: From Prompts to Possibilities Duration: 3 Weeks Learn to work with Large Language Models using prompt engineering and fine-tuning. Explore how Generative AI powers real-world applications in content creation, automation, and beyond. Live Sessions Projects Cheat Sheets Quizzes Module 1 Module 2 Module 3 Large Language Models (LLMs) & Generative AI Prompt Engineering Fine-Tuning Large Language Models AWS Cloud Essentials Key Services for ML Workflows Access Management with IAM (Identity and Access Management) Automating Infrastructure with AWS Cloud Formation Serverless Execution with AWS Lambda Simplified App Hosting with AWS Elastic Beanstalk Introduction to Amazon Sage Maker Data Preparation & Model Selection Model Training & Evaluation in Sage Maker Deploying Machine Learning Models Module 4 Computer Vision: Image Segmentation Introduction to Semantic Segmentation

23. Tools and Technologies You’ll Learn Skills & Software X Excel ML Machine Learning Cloud Platforms X Excel Data Visualization

24. Certification of Completion CERTIFICATE Complete this Data Science program and score a Certificate of Completion. Show o? your skills, impress employers, and unlock new opportunities. Finish, flaunt and get ahead! A+ SKILL STACK HUB STACK YOUR SKILLS, SCALE YOUR SUCCESS CERTIFICATE OF COMPLETION This is to certify Dhruv Kapoor Has successfully Completed with honors DS 360: Complete Data Science Program From........................to......................... July 2024 July 2025 we found him/her pretty active and confident in assigned projects. 16-07-25 Issued On: ...................... Reg. ID: .......................... SSHDS36023 Aishwarya Srivastava Director Verify your certificate www.skillstachub.com/verify-certificate Grade A+ Above 75%, Grade A 65%-75%, Grade B+ 55%-65%, Grade B Below 55%

25. CURIOUS? We know, what’s in your MIND! Why should i Stack Hub? Is Data Scientist a promising career choice? choose Skill What is Data Science? How does the program work? Who are my Mentors? Am i going to support ? What exactly will I learn? get career Are there any success stories from past? How to enroll? Will I get a certificate to enhance my Portfolio?

26. Ready to Begin? Enroll Today You are one step away from your dream career! Enroll Today Still unsure? Call Now to Book Demo Class +91 78383 76383 For any other details, Feel free to reach out...... We are here to help!