Live Instructor-Led Training

Data Science Training

Python + Data Analytics + Machine Learning + Deep Learning + Generative AI

Our Data Science Training takes you from Python, statistics, SQL and data visualization to machine learning, deep learning, NLP, Transformers and Generative AI through live instructor-led training and project-based learning.

  • 100 Hours Live Training
  • 50 Hours Project Work
  • 10+ Real-World Projects
  • 100% Placement Assistance + 6-Month Internship
Attend Free Live Demo → Chat with a Training Advisor →
150+ Hours of Learning Project-Based Learning Placement Assistance & Internship
Attend a Live Demo Book Free Demo Experience the training before you enrol See How We Teach Watch Training Videos Watch Data Science Training Day 1 Explore the Curriculum Download Syllabus View the complete Data Science course content Have a Question? Chat on WhatsApp Speak with our Data Science training team
What You'll Learn

Build End-to-End Data Science & AI Skills

Progress from Python, data analysis and statistics to SQL, Power BI, machine learning, deep learning, NLP and Generative AI through live training and project-based learning.

Python & Data Analysis

Build a strong Python foundation and learn to prepare, manipulate, explore and visualize data for practical Data Science use cases.

Python NumPy Pandas Matplotlib Data Analysis

Statistics & Exploratory Data Analysis

Understand statistical concepts and use exploratory data analysis to discover patterns, relationships and insights in real datasets.

Statistics EDA Visualization Distributions Hypothesis Testing

SQL & Power BI

Query and analyze structured data with SQL and turn business data into meaningful reports and interactive dashboards using Power BI.

SQL Data Queries Power BI Dashboards Business Insights

Machine Learning & Model Deployment

Build predictive machine learning models, evaluate their performance and understand the workflow for taking models toward deployment.

Regression Classification Clustering scikit-learn MLOps

Deep Learning, Computer Vision & NLP

Progress into neural networks, computer vision and natural language processing, including modern Transformer-based AI concepts.

PyTorch ANN & RNN Computer Vision NLP Transformers

Generative AI, LLMs & RAG

Learn modern GenAI concepts and build a Document Intelligence / RAG application that lets users query PDFs using natural language.

LLMs Prompt Engineering Embeddings Vector Databases RAG LangChain
From Data Science foundations to modern Generative AI applications

The complete 15-module curriculum combines 100 hours of live training, 50 hours of project work and 10+ real-world projects.

View Complete Curriculum ↓
See How We Teach

Watch Our Data Science Training Videos

Get a feel for the teaching approach before you enrol. Watch the first three Data Science training sessions and see how concepts are explained through instructor-led teaching, practical examples and step-by-step learning.

Day 1 Demo Session

Data Science Training — Day 1

Start your Data Science learning journey with our live instructor-led Day 1 training session and experience the teaching approach, explanations and practical learning style.

Day 2 Demo Session

Data Science Training — Day 2

Continue with Day 2 of the Data Science training and see how concepts are developed step by step through practical, instructor-led learning.

Day 3 Demo Session

Data Science Training — Day 3

Watch Day 3 of the Data Science training to experience the continuation of the course, practical explanations and instructor-led learning approach.

Videos are helpful. A live session is even better. Attend a free live demo to interact with the trainer and experience the Data Science training approach directly.
Attend Free Live Demo →
LEARN BY BUILDING

Practical Data Science Training, Not Just Theory

Apply what you learn through project work across the course—from data analysis and machine learning to deep learning, NLP and Generative AI applications.

10+ Real-World Projects
50 Hours Project Work
Project-Based Learning
Trainer Guidance

Data Analysis & Visualization Projects

Work with datasets using Python, NumPy, Pandas, statistics and visualization techniques to clean, explore, analyze and communicate meaningful insights.

Machine Learning Projects

Apply machine learning techniques to practical datasets, build predictive models and work through model training, evaluation and deployment-oriented workflows.

Deep Learning, Computer Vision & NLP Projects

Build practical AI skills with PyTorch, neural networks, computer vision, natural language processing and Transformer-based concepts.

Build a Document Intelligence / RAG Application

Upload PDFs → Process Documents → Create Embeddings → Retrieve Relevant Information → Query Documents Using Natural Language → Extract Required Information Using an LLM.

THE GOAL

By the end of the training, you should be able to move beyond individual concepts and apply Data Science, Machine Learning, Deep Learning, NLP and Generative AI skills through practical project work.

YOUR TRAINER

Learn Data Science with an Experienced
Industry Professional

Learn through practical, implementation-focused training designed around the skills used in real Data Science and AI projects.

Live online Data Science training at UnoGeeks
Live Instructor-Led Training Interactive online sessions with practical demonstrations

Learn from Data Science, AI & Gen AI Experience

The training is led by Mr. Satish P, bringing 20+ years of Data Science, AI/ML & Gen AI and training experience, with a teaching approach focused on practical skills rather than theoretical concepts alone.

The training is structured around implementation rather than feature-by-feature demonstrations, helping learners understand how data analysis, machine learning, deep learning, NLP and Generative AI come together in practical Data Science solutions.

Enterprise Implementation Experience Implementation-focused guidance across Data Science project scenarios
Machine Learning & AI Applications Practical guidance across data analysis, machine learning and AI workflows
Scenario-Based Teaching Concepts explained through practical use cases rather than isolated theory
Modern Data Science & AI Stack Coverage extends from Python and analytics to deep learning and Generative AI
20+ Years Enterprise IT &
Training Experience
100+ Batches Live Training
Delivered
2,000+ Professionals Trained Across
Technologies
HOW THE TRAINING WORKS

A Practical Learning Journey from
Demo to Real-World Projects

Start with a live demo, learn directly from the trainer, practice Data Science hands-on and progressively apply what you learn through structured exercises, project work and real-world AI applications.

01

Attend a Live Demo

Experience the teaching approach and understand the course before you enrol.

02

Learn Live with the Trainer

Attend interactive instructor-led sessions covering concepts, demonstrations and practical project scenarios.

03

Practice Hands-On

Work hands-on with Python, data analysis, SQL, machine learning, deep learning, NLP and Generative AI exercises.

04

Apply What You Learn

Bring together Data Science, Machine Learning, Deep Learning, NLP and Generative AI concepts through practical projects and end-to-end exercises.

READY TO EXPERIENCE THE TRAINING? Attend a live demo before deciding to enrol.
Attend Free Live Demo Chat with a Training Advisor
Complete Course Curriculum

Data Science Training Curriculum

Explore the complete 15-module learning path from Python, statistics, SQL and Power BI to Machine Learning, MLOps, Deep Learning, NLP, Transformers and Generative AI.

See How We TeachWatch Training Video →Watch Data Science Training Day 1
Explore the CurriculumDownload Syllabus →View complete Data Science course content
Have a Question?Chat on WhatsApp →Speak with our Data Science training team
Complete Curriculum15 Detailed Modules
Training Structure100 Live Hours + 50 Project Hours
Practical Learning10+ Real-World Projects
01
Learning Track

Data Science Foundations

What is Python and why Data Science requires Python
Python installation and Jupyter Notebook
Python syntax, identifiers and operators
Variables, data types and strings
Lists, sets, tuples and dictionaries
Conditional statements and looping constructs
Functions, lambda, map, filter and reduce
Modules, packages and standard libraries
NumPy, SciPy, Pandas, Matplotlib and Scikit-Learn
Reading CSV, Excel and JSON files
Subsetting and modifying Pandas DataFrames
Sorting, concatenating and SQL-like joins in Pandas
Aggregating and summarizing DataFrames
Preprocessing time-series data
Data visualization with Matplotlib
Data visualization with Seaborn
Machine Learning lifecycle
Problem statement and hypothesis generation
Exploratory Data Analysis and data insights
Descriptive and inferential statistics
Probability and conditional probability
Distributions, skewness and kurtosis
Missing values and outlier treatment
Central Limit Theorem and confidence intervals
Correlation and bivariate analysis
Hypothesis testing: Z-Test, T-Test and Chi-Squared Test
Multivariate analysis
Relational databases, RDBMS and MySQL
Importing and exploring data with SQL
Single-table data analysis
Sales trends and hypothesis-driven analysis
Window functions and percentage change
Joins and multi-table analysis
CRUD operations
Converting raw data into a structured relational dataset
Power BI and data visualization fundamentals
Power BI interface, charts and graphs
Building your first dashboard
Data inspection, cleaning and transformation
Relationships and data models
DAX functions, calculated columns and measures
Filter and aggregation functions
Building customized reports and dashboards
Sales and marketing dashboard project
02
Learning Track

Machine Learning & MLOps

Build your first predictive model
Problem statement and prediction methodology
Benchmark models and model evaluation
Introduction to Machine Learning and types of ML
ML workflow and dataset preparation
Missing value and outlier treatment
Supervised and unsupervised dataset preparation
KNN algorithm and model building
Evaluation metrics: Accuracy, Precision, Recall, F1, AU-ROC, RMSE and R²
Train-test split and cross validation
Simple and multiple Linear Regression
Logistic Regression
Feature selection and scaling
Generalized Linear Models
Decision Trees for classification and regression
Decision Tree hyperparameters
Handling imbalanced datasets
Overfitting and underfitting
L1, L2 and Elastic Net regularization
Support Vector Machine fundamentals
SVM kernels and hyperparameters
Implementing Support Vector Machine
Naïve Bayes fundamentals
Conditional probability and Bayes Theorem
Types of Naïve Bayes
Implementing Naïve Bayes
Ensemble Learning and Bagging
Random Forest and Out-of-Bag score
AdaBoost and Gradient Boosting Machines
XGBoost, LightBoost and CatBoost
Hyperparameter tuning
Grid Search CV and Random Search CV
Bayesian Optimization
K-Means clustering
Finding the optimal K value
Hierarchical Clustering Analysis (HCA)
DBSCAN clustering
Real-world clustering applications
Retail Demand Prediction project
Time Series Forecasting project
Recommender Systems project
Overview of MLOps modules
Challenges in the ML workflow
Level 0 architecture
Real-time and batch-time prediction
Model deployment in Streamlit
Data drift and concept drift
Level 1 MLOps architecture
Introduction to cloud platforms and ML frameworks
AWS services and Amazon SageMaker
03
Learning Track

Deep Learning, NLP & Generative AI

Introduction to Deep Learning
Neurons, scalars and vectors
Building a neuron with PyTorch
Forward propagation and loss functions
Gradient descent and backpropagation
Common optimization techniques
Building Deep Neural Networks
Regression and classification models in PyTorch
Multi-classification project
Introduction and applications of Computer Vision
MLP, CNN and Transfer Learning
Emergency Vehicle Classification dataset
Pre-trained models: LeNet, AlexNet and VGG-16
PyTorch DataLoaders
Inception networks
ResNet and DenseNet architectures
Introduction to Object Detection
IoU and mean Average Precision
R-CNN, Fast R-CNN and Faster R-CNN
Emergency vs Non-Emergency Classification project
Object Detection project
Introduction to NLP and NLP libraries
Text preprocessing techniques
TF-IDF and N-grams
Building a basic review classification model
ANN for NLP
Recurrent Neural Networks (RNN)
Word embeddings
GRU and LSTM models
Seq2Seq and Encoder-Decoder models
Attention mechanism
Transformers
BERT, GPT and fine-tuning pretrained models
Headline extraction using T5
ANN and RNN projects
Understanding Large Language Models (LLMs)
Prompt Engineering Basics
Embeddings and Semantic Search
Vector Databases Fundamentals
Retrieval-Augmented Generation (RAG) Basics
GenAI Project: Build a Document Intelligence / RAG Application
Upload PDFs → Process Documents → Create Embeddings → Retrieve Relevant Information
Query Documents Using Natural Language → Extract Required Information Using an LLM
Want to see how these topics are taught?Attend a live Data Science demo and experience the training approach before enrolling.
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Experience the Training

Attend a Free Data Science Live Demo

Experience the live training before enrolling, see the teaching approach and practical demonstrations, and ask your questions directly during the session.

Free Live Demo

Experience the Training Before You Enrol

Join a live Data Science demo to understand the teaching approach, see practical demonstrations and decide whether the training is right for you before enrolling.

Live Online Instructor-Led Demo
No Commitment Attend Before Enrolling
Get Demo Schedule on WhatsApp →

What to Expect in the Live Demo

Get a clear idea of the training experience before deciding to enrol.

  • See the Training Approach Experience the trainer's live teaching style, explanations and project-focused approach.
  • Watch Practical Data Science Demonstrations See Python, data analysis, machine learning and AI concepts explained through practical demonstrations and real-world project scenarios.
  • Ask Your Questions Live Clarify questions about the course, learning approach, prerequisites, practice and training coverage.
  • Understand the Complete Learning Path See how the training progresses from Python, statistics and data analysis to Machine Learning, Deep Learning, NLP and Generative AI.
Want to know the next available demo schedule?
Check Demo Availability →
Course Completion & Career Readiness

Build Skills You Can Demonstrate

Complete a structured Data Science learning path covering Python, Data Analytics, Machine Learning, Deep Learning, NLP and Generative AI, supported by practical project work that helps you demonstrate what you have learned.

DS
Core Learning Path

Data Science & Machine Learning

Build a strong foundation in data analysis and machine learning through instructor-led learning, hands-on exercises and project work.

Python Statistics & EDA SQL Power BI Machine Learning MLOps
AI
Advanced Learning Path

Deep Learning & Generative AI

Extend your Data Science skills into modern AI with PyTorch, computer vision, NLP, Transformers and practical Generative AI application development.

PyTorch Deep Learning Computer Vision NLP & Transformers LLMs RAG & LangChain
Practical project work is built into the learning journey The program combines 100 hours of live training with 50 hours of project work and 10+ real-world projects across Data Science, Machine Learning and AI.
Attend Free Live Demo →
Career & Job Preparation

Build Skills That Help You Become Job-Ready

Build practical Data Science and AI skills through hands-on learning and project work, and prepare to present your technical knowledge, problem-solving approach and project experience more effectively in interviews and job opportunities.

Resume Preparation

Learn how to structure and present your Data Science, Machine Learning and AI skills, tools and project experience clearly in your resume.

Interview Preparation

Discuss common Data Science and AI interview areas and learn how to explain Python, statistics, Machine Learning, Deep Learning and Generative AI concepts with confidence.

Real-World Project Scenarios

Build experience through 10+ real-world projects across Data Science, Machine Learning, Deep Learning, NLP and Generative AI.

Career Guidance

Understand the types of Data Science and AI roles to explore and how your analytics, Machine Learning, Deep Learning and Generative AI skills map to career opportunities.

See the training approach before you enrol Attend a live demo and understand how practical projects, Machine Learning and Generative AI are incorporated into the Data Science training.
Attend Free Live Demo →
Our Learner Community

Professionals From Leading Organizations

UnoGeeks has trained professionals working across global consulting, technology, banking and enterprise organizations.

Oracle
Deloitte
Accenture
EY
PwC
Infosys
TCS
Wells Fargo
100+ Organizations represented by our learners

Company names are shown only to indicate organizations where UnoGeeks learners have worked and do not imply endorsement, sponsorship or partnership.

Why Students Trust UnoGeeks

Learn with a Training Team Trusted by Thousands

Choosing the right trainer matters. UnoGeeks combines live instructor-led learning, hands-on project work and structured course content to help learners build practical Data Science and AI skills they can demonstrate beyond the classroom.

20,000+ Learners Trained Professionals trained across our technology programs.
750+ Google Reviews Public feedback shared by learners about UnoGeeks.
20+ Years of Experience Enterprise IT, consulting, implementation and training experience.
100+ Live Batches Instructor-led training batches delivered across technologies.
The UnoGeeks Approach

What Students Can Expect from the Training

Live Instructor-Led Learning Learn directly from the trainer through interactive online sessions, demonstrations and explanations.
Project-Based Training Apply Data Science and AI concepts through hands-on exercises, practical datasets and real-world project scenarios.
Data Science + Modern AI Progress from Python, analytics and Machine Learning to Deep Learning, NLP, Transformers and Generative AI.
Structured Learning Path Progress through a structured 15-module path with 100 live hours, 50 project hours, 10+ real-world projects and career preparation.
Experience the training before you decide Attend a free live Data Science demo and see the teaching and project-based learning approach for yourself.
Attend Free Live Demo →
Get in Touch

Talk to Our Training Team

Choose the contact option that works best for you. Our team can help with course details, upcoming batches, demo schedules and fee information.

WhatsApp +91 73960 33555 Batch dates, fees & demo details Call Us +91 73960 33555 Mon–Sat · 6 AM–10 PM IST Email Us info@unogeeks.com For detailed enquiries
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    Frequently Asked Questions

    Data Science Training FAQs

    Find answers about course coverage, prerequisites, live training, projects, Machine Learning, Deep Learning, Generative AI, placement assistance, internship and career-focused learning.

    UnoGeeks Data Science Training follows a 15-module learning path that progresses from core Data Science foundations to modern AI applications.

    Python, NumPy & Pandas
    EDA & Statistics
    SQL & Power BI
    Machine Learning
    MLOps & Deployment
    Deep Learning with PyTorch
    Computer Vision & NLP
    Transformers, GenAI, LLMs & RAG

    Yes. The program starts with Python and the foundations needed for Data Science before progressing into statistics, data analysis, machine learning and advanced AI topics. You do not need prior Data Science experience to begin. Python and statistics concepts required for the course are covered as part of the training.

    The program includes 150+ hours of learning: 100 hours of live instructor-led training plus 50 hours of project work. The project component is designed to help you apply concepts instead of learning them only at a theoretical level.

    Yes. The program includes 10+ real-world projects and project work across the learning journey. Projects reinforce data analysis, machine learning, deep learning, NLP and Generative AI skills rather than treating practical work as a separate afterthought.

    The course works across a modern Data Science and AI stack.

    Python
    NumPy & Pandas
    Matplotlib & Seaborn
    SQL
    Power BI
    scikit-learn
    PyTorch
    LangChain & LLM concepts

    Yes. Machine Learning is a major part of the curriculum. The learning path covers building your first ML model, foundational and advanced ML algorithms, unsupervised learning and MLOps from development to deployment.

    Yes. The advanced part of the program covers Deep Learning using PyTorch, applied Computer Vision and Natural Language Processing. It also introduces Transformer-based concepts as the learning path progresses toward modern Generative AI.

    Yes. The updated curriculum includes Large Language Models (LLMs), Prompt Engineering, Embeddings and Semantic Search, Vector Databases, Retrieval-Augmented Generation (RAG) and LangChain-based application concepts. This extends the course beyond traditional Data Science into practical modern AI development.

    One of the featured projects is a Document Intelligence / RAG Application. The workflow covers: Upload PDFs → Process Documents → Create Embeddings → Retrieve Relevant Information → Query Documents Using Natural Language → Extract Required Information Using an LLM.

    No prior Data Science experience is required. Basic Python and statistics knowledge can be helpful, but the required Python and statistics foundations are covered within the course itself. You should have a computer suitable for coding practice and a stable internet connection for live online sessions.

    Yes. UnoGeeks provides live instructor-led online Data Science training. Learners can attend interactive sessions remotely, follow trainer demonstrations, ask questions and complete hands-on exercises and project work.

    Yes. The Data Science program includes 100% Placement Assistance with 6 Months Internship. Career preparation supports learners with resume preparation, interview preparation, project discussion and guidance on presenting their Data Science and AI skills. Placement assistance is support for the job-search process and does not represent a guaranteed job offer.

    Depending on your prior experience, skills and the depth of knowledge you develop, the program can support preparation for roles such as:

    Data Analyst
    Data Scientist
    Machine Learning Engineer
    AI / GenAI-focused roles
    Career outcomes vary by experience, interview performance, market conditions and individual skill development.

    Yes. Learners who successfully complete the training can receive an UnoGeeks Data Science Course Completion Certificate. This is a UnoGeeks course-completion credential and should not be confused with a currently active NASSCOM certification or partnership.

    Yes. You can attend a free live Data Science demo before enrolling. It gives you an opportunity to experience the trainer, teaching approach and practical learning style and to clarify questions about course coverage, projects, batches and fees.

    Want to experience the training before enrolling? Attend a free live Data Science demo and see the trainer, course approach and project-based learning style for yourself.
    Attend Free Live Demo →