The Master's Program in Data Science at Coding Now Tech Institute takes you from data fundamentals to advanced AI and deployment. Across 270+ learning hours you build 25+ real-world projects and master the complete toolkit — Python, SQL, Statistics, Machine Learning, Deep Learning, Generative AI, Tableau/Power BI and Cloud — the full path from raw data to a deployed, job-ready AI product.
Program at a Glance
| Metric | Detail |
|---|---|
| Learning hours | 270+ |
| Projects | 25+ portfolio-ready, real-world builds |
| Tools covered | Python, SQL, Tableau, Power BI, TensorFlow/Keras, AWS & more |
| Modules | 9 structured modules |
| Mode | 100% online / offline classroom |
| Placement | 100% placement assistance |
What Is a Data Scientist?
A data scientist goes a step beyond the analyst: instead of only explaining what happened in the data, they build models that predict what happens next — and increasingly, systems that reason and generate. That means combining statistics, programming, machine learning and, today, generative AI into one pipeline: understand the business question, gather and clean the data, build and evaluate a model, and ship it so other people or systems can actually use it.
It is a deep, broad role — you need to be comfortable with code, with numbers, and with the judgment to know which tool fits which problem. This program builds that breadth deliberately, module by module.
Why Become a Data Scientist?
- End-to-end ownership — data scientists work the full pipeline, from raw data to a deployed model, not just a slice of it.
- The fastest-growing skill stack — machine learning, deep learning and generative AI are the highest-demand skills in tech right now.
- Strong, fast-climbing compensation — the combination of statistics, coding and AI skills is scarce and well-paid.
- Cross-industry demand — finance, healthcare, retail, logistics and every AI-adjacent product team hires data scientists.
The Roles of a Data Scientist
- Frame a business problem as a data/modeling question, then gather and clean the data behind it.
- Explore data statistically to find signal, test hypotheses and validate assumptions.
- Build, tune and evaluate machine learning and deep learning models.
- Apply generative AI — prompting, RAG, LLM APIs — to build AI-powered features.
- Deploy models and dashboards so the business can actually act on them.
Tools & Technologies Covered
| Tool | What you'll use it for |
|---|---|
| Python | Data wrangling, analysis and modeling (NumPy, Pandas, Matplotlib, Seaborn) |
| SQL | Querying, joins, window functions, CTEs and query optimization |
| Scikit-learn & XGBoost | Classical machine learning — regression, trees, ensembles, clustering |
| TensorFlow / Keras | Neural networks, CNNs, RNNs/LSTMs and transfer learning |
| LLM APIs | Prompt engineering, RAG pipelines and building AI-powered apps |
| Tableau & Power BI | Dashboards, DAX and business-facing data storytelling |
| AWS & MLOps | Deploying models with Flask/FastAPI, Streamlit, EC2 and CI basics |
Curriculum — 9 Modules
Module 01 — Introduction to Data Science & AI
- What Is Data Science?
- History & Future of Data Science
- Data Science vs Business Intelligence vs AI
- The Data Science Lifecycle (CRISP-DM)
- Types of Analytics
- Introduction to AI & Generative AI
- Data Science Applications by Industry
- Ethics, Bias & Responsible AI
- Data Science Career Paths
Module 02 — SQL
- SQL DDL, DML, DCL, TCL & DQL
- SQL Aggregate Functions
- SQL Date & String Functions
- UNION, UNION ALL, INTERSECT & EXCEPT
- SQL Joins
- SQL Subqueries & CTEs
- SQL Views & Indexes
- SQL Window Functions
- SQL Query Optimization Basics
Module 03 — Python for Data Science
- Python Basics & Data Structures
- Conditional Statements, Loops & List Comprehensions
- Functions, Lambda & Scope
- File Handling in Python
- NumPy for Data Science
- Pandas for Data Science
- Matplotlib & Seaborn
Module 04 — Statistics for Data Science
- Descriptive Statistics, Skewness & Kurtosis
- Probability & Bayes' Theorem
- Probability Distributions
- Sampling & the Central Limit Theorem
- Hypothesis Testing
- Correlation, Covariance & Regression Analysis
- Bias-Variance, Overfitting & Underfitting
- Error Metrics
Module 05 — Machine Learning
- Introduction to Machine Learning
- Linear & Logistic Regression
- Decision Trees, Random Forest, KNN & Naive Bayes
- Support Vector Machines (SVM)
- XGBoost & AdaBoost
- K-Means & Hierarchical Clustering
- Principal Component Analysis (PCA)
- Model Evaluation
Module 06 — Deep Learning
- Neural Networks Introduction
- Forward & Backward Propagation
- TensorFlow & Keras Basics
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks, LSTMs & GRUs
- Transfer Learning
- Generative Adversarial Networks (GANs)
- Large Language Models (LLMs)
- Introduction to Agentic AI
Module 07 — Generative AI
- What Is Generative AI?
- Prompt Engineering for Data Analysis
- Using LLM APIs with Python
- Building a Text Summarizer
- Retrieval-Augmented Generation (RAG)
- PDF Q&A Bot with an LLM
- AI Chatbot for Data Insights
- Ethical AI: Bias, Hallucination & Privacy
Module 08 — Tableau & Power BI
- Tableau Interface, Charts & Maps
- Tableau Calculated Fields, Filters, Dashboards & Stories
- Power BI Desktop Interface
- Power Query Data Transformation
- Power BI Data Modeling & DAX Basics
- Power BI Visualizations & Slicers
Module 09 — Cloud & Deployment
- Introduction to Cloud Computing & AWS Core Services
- Deploying a Streamlit App on EC2
- Model Serving with Flask & FastAPI
- Introduction to MLOps
- GitHub for Code Versioning
- Deploy on Streamlit Cloud & Render
Real-World Projects
You build 25+ projects across industries, including:
- Finance — Credit Risk & Fraud Detection: train classification models to flag risky applicants and suspicious transactions.
- Retail — Customer Churn Prediction: use historical behaviour to predict who's about to leave, and why.
- Healthcare — Disease Prediction Model: apply supervised learning to structured patient data for early risk detection.
- Computer Vision — Image Classification with CNNs: build and train a convolutional network end to end.
- Generative AI — PDF Q&A Bot: build a retrieval-augmented chatbot that answers questions from a document set.
- Deployment — Dashboard-to-Cloud Pipeline: ship a trained model as a live Streamlit app on AWS.
Career Outcomes
Coding Now Tech Institute is one of India's most trusted project- and placement-driven learning platforms. Across programs the institute reports 1000+ students placed, 200+ hiring partners, a ₹34 LPA highest package and a 68% average salary hike. Every learner is assigned a Program Manager who supports you toward your career objective — from resume building and mock interviews to referrals.
Who Should Join
- Data analysts and Excel/SQL professionals ready to move into modeling and AI.
- Engineers and CS graduates who want a structured, project-driven path into data science.
- Working professionals from any background aiming for a machine learning or AI role.
Frequently Asked Questions
Do I need a maths or ML background to start?
No. The program builds statistics and Python from the ground up before introducing machine learning, deep learning and generative AI in sequence.
Is the placement support real?
Yes. You get 100% placement assistance with resume building, mock interviews, portfolio reviews and access to 200+ hiring partners.
Online or offline?
Both. Choose 100% online live classes or the classroom in Pitampura, Delhi — same curriculum and placement support.
How long does it take?
The full track is 270+ learning hours across 9 modules, delivered over a few months with flexible schedules.