Data Science 360 Course - Data Analytics, ML & AI

Q: What is Data Science, and what does a Data Scientist do?

Data Science combines Python, statistics, mathematics, data analysis, and Machine Learning to extract insights and solve real-world problems using data. A Data Scientist works on data preparation, exploratory analysis, visualization, predictive modeling, model evaluation, and AI-based solutions. This course takes learners from Data Science fundamentals to Machine Learning, Deep Learning, Generative AI, RAG, Agentic AI, and LLM fine-tuning.

Q: Who should join this Data Science 360 course?

This course is suitable for beginners, college students, fresh graduates, working professionals, software developers, aspiring Data Scientists, Machine Learning enthusiasts, and learners transitioning into AI. It provides a structured learning path from Python, Statistics, and Data Analysis to Machine Learning, Deep Learning, Generative AI, RAG, Agentic AI, and LLM fine-tuning.

Q: Is this Data Science course suitable for beginners?

Yes. The course starts with Python fundamentals, file handling, Statistics, Probability, and Data Analysis before progressing to Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, RAG, Agentic AI, and LLM fine-tuning. This progressive structure allows beginners to build foundational skills before moving into advanced Data Science and AI concepts.

Q: Do I need prior programming or Data Science experience to join this course?

The course begins with Python fundamentals and gradually introduces Statistics, Data Analysis, SQL, Machine Learning, Deep Learning, and AI. Learners who are new to Data Science can follow the structured progression, while those with prior programming experience can strengthen their existing skills through advanced topics and practical projects.

Q: What skills will I learn in the Data Science 360 course?

You will learn Python, Statistics, Probability, NumPy, Pandas, SQL, MongoDB, Excel, Data Visualization, Tableau, Power BI, Machine Learning, Deep Learning, PyTorch, NLP, Computer Vision, Prompt Engineering, Hugging Face, RAG, Vector Databases, Agentic AI, LLMOps, and LLM fine-tuning. The curriculum progresses from foundational Data Science to advanced AI applications.

Q: What programming language will I learn for Data Science?

Python is the primary programming language used throughout the course. You will learn Python fundamentals and use it for data analysis, visualization, Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, RAG, Agentic AI, and LLM fine-tuning. Python is also widely used across modern Data Science and AI workflows.

Q: Will I learn Statistics and Mathematics for Data Science?

Yes. The course covers descriptive statistics, sampling, hypothesis testing, correlation, covariance, probability distributions, and Bayes' theorem. Mathematical foundations include vectors, matrices, linear transformations, eigenvalues, eigenvectors, matrix decomposition, and differentiation. These concepts help learners understand the mathematical foundations behind Data Science and Machine Learning.

Q: Will I learn SQL, databases, and data analysis?

Yes. The course covers SQL fundamentals, data retrieval, advanced SQL, SQL-based data cleaning, query optimization, and database integration. You will also work with MongoDB and Python-based database integration. The curriculum includes an analytical SQL project involving normalized tables, joins, CTEs, and window functions.

Q: Will I learn Python libraries such as NumPy and Pandas?

Yes. The course covers NumPy and Pandas as part of the Data Science foundation. You will use these libraries for numerical operations, data manipulation, data cleaning, analysis, and working with datasets before progressing to Machine Learning and advanced AI applications.

Q: Will I learn Machine Learning from scratch?

Yes. The Machine Learning curriculum starts with the fundamentals and progresses through Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, SVM, KNN, regularization, bias-variance tradeoff, clustering, dimensionality reduction, and ensemble learning including XGBoost. This provides a structured foundation for applying Machine Learning to practical problems.

Live Course

599k+ interested Geeks

The Data Science 360 Course Data Analytics, ML & AI is a 12-sprint program covering Python, statistics, SQL, Excel, Power BI, Tableau, machine learning, deep learning, Generative AI, RAG, Agentic AI, and LLM fine-tuning. Build hands-on projects in e-commerce analytics, churn prediction, customer segmentation, sentiment analysis, AI applications, RAG, multi-agent systems, and LLM fine-tuning to develop practical, industry-ready data science and AI skills.

Beginner to Advanced

Beginner FriendlyCourse CertificateIndustry ReadinessCapstone Projects

Master Python, ML & DS at the most affordable rate

Course Overview

24/7 AI Doubt Support

Get instant help anytime, anywhere

IBM & GeeksforGeeks Certification

Earn industry-recognized certification

Modern AI Toolkit

Work with the latest AI tools and technologies

Skills You'll Gain

Key Data Science concepts you'll build and master.

Exploratory Data Analysis

Statistical Hypothesis Testing

Database Management and SQL Optimisation

Data Visualisation and Dashboarding

Predictive Modeling and Machine Learning

Natural Language Processing

Deep Learning and Neural Architecture

Prompt Engineering

LLMOps

Retrieval Augmented Generation

Fine-tuning LLMs

Local LLMs

Multi-Modal Gen AI

Agentic AI and Multi-Agent Orchestration

Tech Stack You'll Learn

Tools and technologies you'll work with

Python

NumPy & Pandas

SQL, SQLite & SQLAlchemy

MongoDB

Tableau

Power BI

Scikit-Learn

PyTorch

Hugging Face

LangChain

Ollama

CrewAI

Gradio

Chainlit

LangGraph

LangSmith

Projects You'll Build

Real-world projects to apply your skills

Automated Data Pipeline & Forensic Log Analyzer

Parse dozens of corrupted server logs, quarantine the broken ones, and emit one clean analytics-ready file.

PythonOS/ShellFile Handling

Building, Cleaning and Analyzing an E-Commerce Dataset

Scrape real product listings, clean them with NumPy, then test whether two brands really dier on price.

SeleniumNumPyPandas

Analyzing the E-Commerce Dataset in SQL

Normalise the same data into SQLite, then answer it with window functions and CTEs.

SQLSQLitePython

Dashboarding the E-Commerce Dataset in Power BI

KPI cards, DAX measures, and slicers - built for a named audience with three questions to answer.

Power BIDAXData Visualization

Customer Churn & Revenue Prediction

A lifetime-value regressor and four churn classifiers, compared on the same confusion matrices.

Scikit-learnRegressionClassification

Customer Segmentation & Review Intelligence

K-Means validated with silhouette scores, PCA for the 2D view, then XGBoost on top of the segments.

K-MeansPCAXGBoost

Apple Grade Classifier

A CNN built from scratch in PyTorch, trained twice - raw versus augmented - and judged on recall, not accuracy.

PyTorchCNNComputer Vision

Review Sentiment Analysis

Bag-of-Words against 100-dim GloVe vectors, same RNN, same split - a fair head-to-head on F1.

GloVeRNNNLP

Learner Support Ticket Router

Zero-shot, few-shot, and unified prompts scored on the same held-out tickets, with Pydantic keeping the JSON valid.

Prompt EngineeringLangChainPydantic

Chat With a PDF That Has Charts

Two retrieval paths - text and diagrams - into one vector DB, running entirely offline through Ollama.

RAGVector DatabaseOllama

Ticket Response MultiAgent System

A CrewAI triage, resolution, and escalation crew - with guardrails against prompt injection and LangSmith on the trace.

CrewAILangSmithLLM Guardrails

CAPSTONE

Fine-tuning an LLM for Code Review

QLoRA with Unsloth, an alignment pass, then quantisation - with a real before-and-after on the same prompts.

QLoRAUnslothLLM Fine-Tuning

Course Content

01Sprint 1: Python and File Management Fundamentals

Python Fundamentals

Getting Started with Python
Conditionals and Flow Control
Data Structures
Function & OOP
Special Functions
Exceptions
Pythonic Style Guide

File Management System

Getting Started with Files
Recorded Project: Inventory Management System with Files
OS with Shell

Live Project Class: Automated Data Pipeline & Forensic Log Analyzer

02Sprint 2: Statistics, Probability, and Data Analysis with Python

Statistics & Probability

Descriptive Statistics
Sampling
Hypothesis Testing
Correlation and Covariance
Probability Distributions
Bayes Theorem

Data Collection via Web Scraping

Introduction to Web Scraping
Recorded Project: Book Scraper
Recorded Project: Wikipedia Scraper
Recorded Project: Youtube Scrapper
Recorded Project: Image Dataset Creation
Common Selenium Setup Issues

Data Analysis with NumPy

NumPy Arrays
Essential Array Operations
Working With Files
Data Cleaning and Analysis with NumPy
Visualization with NumPy

Data Analysis with Pandas

Principles of Exploratory Data Analysis
Data Analysis with Pandas

Data Visualization with Python

Data Visualization with Matplotlib
Data Visualization with Seaborn

Data Analysis Projects

Recorded Project: Article Content Analysis
Recorded Project: IMDB Movies Data Analysis
Recorded Project: The Bookwarm's Data Guide
Recorded Project: Cracking the Code: An Inside Look at Netflix's Content Strategy

Other Python Libraries for Data Analysis

Pandas AI
YData Profiling
Streamlit in One Video

Live Project Class: Building, Cleaning and Analyzing an E-Commerce Dataset

03Sprint 3: Data Analysis with Databases and Excel

Data Analysis with SQL

SQL-Fundamentals
Data Retrieval with SQL
Advanced SQL Techniques
Data Cleaning with SQL
Query Optimisation Techniques

Data Analysis with NoSQL

MongoDB Concepts: NoSQL Paradigm

Python Integration with Databases

Python Integration with Databases

Data Analysis with Excel

Exploring Data
Preparing Data
Analysing Data

Excel Projects

Recorded Project: Building a Netflix Analytics Dashboard Using Excel
Recorded Project: Healthcare Intelligence Dashboard
Recorded Project: End-to-End Retail Business Analytics Using Excel

Live Project Class: Analyzing the E-Commerce Dataset in SQL

04Sprint 4: Data Dashboarding and Business Intelligence

Principles of Dashboarding

Principles of Dashboarding

Dashboarding with Plotly Dash

Plotly Dash

Tableau

Introduction to Tableau
Data in Tableau
The Working Area in Tableau
Customizing Your Data
Working on Charts
Creating Your First Dashboard in Tableau

Power BI

Introduction to Power BI
Working with Data in Power BI
Visuals in Power BI
Creating Your First Dashboard in Power BI

Live Project Class: Dashboarding the E-Commerce Dataset in Power BI

Upcoming Batches

Batch

Mentor

STARTING FROM

TIMINGS

Testimonials

Learning will never become old, you must update yourself over time, and it will improve every aspect of life. Great thanks to GeeksforGeeks for quali...

Ram Sharma

Placed at The Bharat Groups

Before joining the Geeks for Geeks "Data Science live" course, I had only a basic knowledge of python. But after joining the live classes I acquired a...

ABDULLAH FAZILI

Placed at GeeksforGeeks

As a newbie in the field of Data Science, Python, and Machine Learning, this course was extremely helpful to me in a variety of ways. First, it was so...

Eshant Das

Placed at GeeksforGeeks

I found the course to be very informative and well-structured. The materials and resources provided were helpful and gave me a solid understanding of ...

Prateek Singh

Placed in Ericsson Global India Limited

This course helped me enhance my data analytics skills, enabling me to analyze large datasets effectively, derive meaningful insights, and make inform...

Sagar Patle

Got Placed at Quantity kiosk Technology

In this course I was able to understand all the concepts clearly and also I did projects which helped me to get hands on practice on language and tool...

Harsh

Placed at Marvell

Data Science 360 Course - Data Analytics, ML & AI

Course Overview

24/7 AI Doubt Support

IBM & GeeksforGeeks Certification

Modern AI Toolkit

Skills You'll Gain

Tech Stack You'll Learn

Projects You'll Build

Course Content

Upcoming Batches

Testimonials

Frequently Asked Questions

What is Data Science, and what does a Data Scientist do?

Who should join this Data Science 360 course?

Is this Data Science course suitable for beginners?

Do I need prior programming or Data Science experience to join this course?

What skills will I learn in the Data Science 360 course?

What programming language will I learn for Data Science?

Will I learn Statistics and Mathematics for Data Science?

Will I learn SQL, databases, and data analysis?

Will I learn Python libraries such as NumPy and Pandas?

Will I learn Machine Learning from scratch?

Does the course cover supervised and unsupervised Machine Learning?

Will I learn Deep Learning and Neural Networks?

Does the course cover NLP and Computer Vision?

Does the Data Science 360 course cover Generative AI and Large Language Models?

Will I learn Prompt Engineering, RAG, and AI Agents?

Will I learn how to fine-tune Large Language Models?

What projects will I build during this Data Science 360 course?

Can I build a Data Science and AI portfolio after completing this course?

What if I am in 1st, 2nd, 3rd, or final year of college? Will this course help me?

Will this course help if I am pursuing BCA, MCA, B.Tech, M.Tech, or another degree?

What roles can I target after completing this Data Science course?

What salary range can I target after completing this Data Science course?

How much time will it take to complete the Data Science 360 course?

How deeply does the Data Science 360 course cover Data Science, Machine Learning, and AI?

How is this Data Science 360 course different from other Data Science courses available online?

Is the Data Science 360 course enough to become a Data Scientist or AI/ML Engineer?

What are the key features of this Data Science 360 course?

Is there a refund option if I am not satisfied with the course?

Is there any number to contact for query?