Data Science Training

Complete Data Science Training Course with AI in Mumbai: Python, Generative AI, Agentic AI, Advanced Statistics, Machine Learning, NLP and Deep Learning.

What you'll learn

Learn Python for data analysis, automation, and AI development
Learn to use libraries like NumPy, Pandas, Matplotlib, and more.
Work with GPT models like OpenAI GPT, Grok, and LLaMA to build intelligent AI-powered applications.
Create LLM-based applications using LangChain and LangGraph.
Design autonomous AI Agents capable of planning, decision-making, tool usage, and task execution.
Use Hugging Face transformers, datasets, and pre-trained models to accelerate AI and NLP development.
Work with real datasets to clean, analyze, and visualize data, extract insights, and prepare data for Machine Learning models.

Understand and implement Deep Learning concepts using neural networks, backpropagation, and advanced architectures.
Explore large datasets using data visualization tools like Matplotlib and Seaborn.
Learn text preprocessing, embeddings, sentiment analysis, transformers, and NLP pipelines for real-world applications.
Build image classification and computer vision models using Convolutional Neural Networks (CNN).
Build chatbots, AI assistants, document analysis systems, recommendation engines, and RAG-based applications.
Work on end-to-end Data Science, Machine Learning, and Generative AI projects to build a strong job-ready portfolio.

Our Training Process

Practical Session

Assignment

Projects

Be Job Ready

Interview Preparation

Resume Building

Key Highlights

100% Job Placement Guarantee
Personalised career coach
90% Practical Training
Official Certification
Live Capstone Projects
Study material
Instant doubt solving
Case studies and Projects

Data Science Training

Python Fundamentals

Python Basics: Syntax, Variables, Data Types
Input and Output Operations
Operators and Expressions
Conditional Statements
Loops
Data Structures: Lists, Tuples, Sets, Dictionaries
Comprehensions and Iterations
Hands-On Exercises: Scripts, condition checks, sorting data, dictionary

File Handling & Data Wrangling with NumPy

Reading & Writing Files
CSV Parsing
Error Handling
NumPy Arrays & Operations
Indexing, Slicing, Reshaping
Mathematical & Aggregation Functions
Multi-Dimensional Arrays
Hands-On Exercises: File parsing, CSV analytics, NumPy statistics

Data Analysis with Pandas

Pandas DataFrames & Series
Loading Data (CSV, Excel, SQL)
Filtering, Sorting, Grouping
Handling Missing Data
Data Cleaning & Transformation
Date & Time Handling
Merging & Joining DataFrames
Hands-On Exercises: Sales analysis, data cleaning, merging datasets

Exploratory Data Analysis & Visualization

Descriptive Statistics
Matplotlib & Seaborn Visuals
Correlation Analysis
Outlier Detection
Advanced Visualizations
Multi-Panel Charts
Hands-On Exercises: EDA reports, heatmaps, dashboards

Advanced Data Management & Databases

SQLAlchemy Basics
ORM Queries
SQL to Pandas Integration
Writing Data Back to Databases
Views, Stored Procedures, Optimization
Hands-On Exercises: Database analytics & reporting

Statistics for Data Analytics

Univariate, Bivariate & Multivariate Analysis
Distributions & Probability
Hypothesis Testing
Confidence Intervals
Correlation & Covariance
Inferential Statistics
Hands-On Exercises: Statistical analysis using real datasets

Introduction to Machine Learning

ML Concepts & Use Cases
Supervised vs Unsupervised Learning
Regression & Classification
Forecasting
Hands-On Exercises: Churn analysis & fraud analysis

ML with Scikit-Learn

Regression modeling
Feature scaling
Model evaluation
K-Means clustering
Hands-On: House price prediction, model optimization

Supervised & Unsupervised ML

Linear & logistic regression
Decision trees & random forests
SVM, KNN
PCA & LDA
Hands-On: Stock prediction, customer segmentation

Deep Learning Foundations

Neural networks
TensorFlow & PyTorch setup
Tensors & computation graphs
Basics of building and training models
Hands-On: Build basic neural networks, Visualize computation graphs in TensorFlow

Deep Neural Architectures

Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Transfer learning
Hands-On: ANN, CNN & RNN implementations

Advanced Neural Networks

Long Short-Term Memory Networks (LSTM)
Graph Neural Networks (GNN)
Advanced Recurrent Architectures: Bidirectional RNN, GRU
Hyperparameter tuning
Hands-On: Time-series & graph models

NLP & Text Analytics

Introduction to NLP concepts
Text preprocessing
Tokenization & stemming
NLTK
Text classification
Sentiment analysis
Hands-On: Spam & sentiment models, Preprocess a text dataset using NLTK, Build a text classification model, Experiment with a text generation model

Foundations of Generative AI

Evolution of Generative AI: From ML to LLMs
Transformer architecture: Attention, encoder-decoder, self-attention
Types of LLMs: GPT, BERT, T5, Falcon, Claude
Closed-source vs Open-source models

LLMs, Prompt Engineering & OpenAI
Integration

Prompt engineering
Understanding Chat, Completion, and Instruction-tuned models
Prompt Engineering techniques: Few-shot, zero-shot, chain-of-thought
OpenAI API: Usage, tokens, parameters
API integration with Python
Fine-Tuning & Customizing LLMs
Instruction tuning using domain-specific data
Use case: Creating a healthcare/legal/HR domain assistant
Best practices & deployment considerations

RAG Applications

RAG architecture
Text chunking, embedding models, similarity search
Use cases: Document Q&A, PDF bots, private chatbots
Combining LLMs with external knowledge

Vector Databases & Embeddings

What is a Vector DB
Choosing the right Vector DB (FAISS vs Pinecone vs Weaviate)
How embedding models work
Storing, indexing, and retrieving large document

Build AI Agents using LangChains

What is an AI Agent?
LangChain Agent architecture
Adding tools: search, calculator, DB access, email
Memory integration: BufferMemory, SummaryMemory
Build chatbots that reason and act

Multi-Agent Systems & LangGraph

Introduction to LangGraph for stateful workflows
Nodes, edges, state sharing
Multi-agent collaboration (e.g., PM → Dev → Analyst agents)
Conditional workflows, retries, and dynamic decision-making
Use cases: Autonomous workflows, research agents, AI coworkers

Deploying Generative AI & Agentic
Applications

FastAPI / LangServe to deploy your solution as an API
Build UIs using Streamlit / Gradio
Deployment platforms: Vercel, AWS, Streamlit Cloud
Logging, monitoring, human-in-the-loop workflows

MLOps with GCP

Introduction to MLOps
MLOps vs. DevOps
SDLC Basics
What is Cloud Computing
GCP Introduction

Version Control System

Git Essentials
Configuring Git
Branching
Git Workflow
Repo
Git Commands
Tracking and managing changes to code
Source Code Management
Tracking and Saving Changes in Files

CI/CD for ML Models

Introduction to CI/CD
CI/CD Challenges
CI/CD Implementation in ML
Popular DevOps Tools

Docker & Kubernetes for ML Deployment

Docker Architecture
Docker for Machine Learning
Continuous Deployment
Writing a Dockerfile to Create an Image
Installing Docker Compose
Configuring Local Registry
Container Orchestration
Application Deployment
Kubernetes Core Concepts

MLOPS with AWS SageMaker

Uploading datasets and training models in the cloud
Hosting real-time and batch endpoints
Autoscaling, monitoring, and billing control

End-to-End GenAI & RAG Projects for Data Scientists

Intelligent Agentic Ai Personal Assistant
Customer Support App
AI Resume Analyser
Product Recommendation System for Ecommerce
Social Media Sentiment Analysis and Trend Prediction/li>
Automated Model Deployment and Monitoring for
Customer Churn

MySQL

Introduction to MySQL
Inserting data
Crud commands
String functions
Basic database terminology
Mysql constraints
Aggregate functions
MySQL stored procedure – I
MySQL stored procedure – II
For detailed course module click here

Tableau

Tableau basics
Maps, scatterplots & your first dashboard
Joining and blending data, plus: dual axis charts
Table calculations, advanced dashboards, storytelling
Advanced data preparation
For detailed course module click here

Power BI

Introduction
Connecting & shaping data with Power BI desktop
Creating table relationships & data models in Power BI
Analyzing data with dax calculations in Power BI
Visualizing data with Power BI reports
Artificial Intelligence (AI) visuals
For detailed course module click here

Skills you will gain

Data Science Machine Learning Data Analytics Training Python R Programming Deep Learning Power BI MySQL NLP Tableau Training PowerBI Training Course Generative AI

Course Certification

Become a Certified Data Scientist with TryCatch Classes and enhance your career prospects to the next level.

This certificate serves as an official badge of your successful Data Science training course completion, highlighting your expertise

Students Reviews

I joined TryCatch in 2018. Since then I learn Android app development, Python, ML from TryCatch Classes and Currently I’m working as an Assistant Manager. Before joining, I struggled with structured learning and lacked mentorship. Their practical training made it easier to crack job opportunities. To anyone unsure about joining TryCatch — just go for it. You won’t regret it.

Manan Shah

Assistant Manager

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Software engineer
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Jane Doe

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Genuine reviews for our Data Science Training

Darshan

Data Scientist

First and foremost, I was thoroughly impressed with the quality of education, it was on point of my expectations. They were able to break down complex concepts into easily digestible pieces, and their explanations were always clear and concise. Also every faculty present there is the industry expert himself.I also appreciated the emphasis that Try Catch Tech placed on hands-on learning. Rather than simply lecturing about the topic, the classes provided ample opportunities to put theory into practice. This approach not only helped me understand the concepts better, but it also gave me a sense of confidence when working with these technologies.Another aspect of Try Catch Tech that stood out to me was the supportive community that surrounded the classes. From fellow students to instructors, everyone was incredibly encouraging and willing to help each other out. This made for a collaborative learning environment that I truly appreciated. And their support is so much that even after completing my course I was always welcomed to sit and study there in the classes.Overall, I would highly recommend Try Catch Tech to anyone looking to learn anything from the course they offer. Personally I did python and machine learning which was a great experience for me. The quality of instruction, hands-on learning opportunities, and supportive community make it an excellent choice for both beginners and experienced programmers alike. Thank you, Try Catch Tech, for a wonderful learning experience!

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Sudarshan Paranjape

Data Scientist

I completed TryCatch Group’s Data Science course, and it was an enlightening journey! The comprehensive curriculum covered essential topics, and the instructors were knowledgeable and approachable. Hands-on projects boosted my confidence, and provided ample resources. The supportive team promptly addressed queries, and industry case studies enriched the experience. The mentorship provided by Mohnish sir and Mehul sir was truly exceptional! Their expertise and passion for data science shone through in every aspect of the course. Highly recommended for all skill levels, this course lays a strong foundation for a successful data science career. Thank you, TryCatch Group, for this amazing learning opportunity!

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Mansi Rathod

Data Scientist

I wanted to go to USA to do my Masters in Data Science. So I joined Trycatch and completed the Data Science course at TryCatch. Anyone who’s willing to pursue Data Science in the future but has no to little knowledge about it, this class will definitely help you enhance your knowledge. Mehul and Mohnish being knowledgeable, will be there to guide you whenever needed. Overall experience was really good so I would definitely recommend this course.

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Mansi Rathod

Data Scientist

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Jamil Shaikh

Data Scientist

I have done Data Science course from Trycatch Classes. I would recommend this course as it gives good idea about the field also the staff are friendly and helpful. This course has helped me to understand my subjects easily.

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Google

Jamil Shaikh

Data Scientist

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Sujwal Shetty

Data Scientist

I had taken Python+ ML course here. The overall experience was very good, I got the internal switch to Data Science domain which i was looking for in my organisation. The faculty had great knowledge within the subject.

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Ranjan Prajapati

Data Scientist

The Alteryx and MySQL stack course here is fantastic. I loved how the trainers explained complex concepts in such a simple way. Working on live projects really boosted my confidence. Now I can build workflows and write SQL queries like a pro. Big thumbs up to the team!

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Google

Software Developer

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Software Developer

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Software Developer

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Student's Portfolio

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Mansi Sanghani

UX-UI Designer

Success Stories

Frequently Asked Questions (FAQs)

What is the duration of the course?

Total duration of Data Science training course is approximately 6-8 months depending on the course you select. Throughout the course, you will receive hands-on practical training in each technology, with a focus on completing real-world projects.

Is there 100% Placement Guarantee after the course is over?

Yes, we provide 100% placement guarantee in our Data Science training course in Mumbai.

Are there any prerequisites before starting Data Science Training?

Anyone can learn this Data Science training course.

Who teaches Data Science ?

At TryCatch, our team consists of seasoned experts with over 15+ years of experience. A skilled Data Scientist will be guiding students, encouraging them to ask questions without hesitation, and enabling us to effortlessly address all your inquiries.

Is the course Online or Offline?

This Data Science course is available offline & online both. You may choose whatever is feasible for you.
Offline course can be done at our Borivali Branch in Mumbai.
Online Live Course can be done on Zoom or Google Meet.

Who can learn Data Science?

This course is designed for everyone, even if you’re studying Commerce, Arts, or Mechanical subjects, or if you’re still in school. It doesn’t matter what your background is, you can definitely learn this course.

Do I need prior experience in Data Science?

No, prior experience is not required. This Data Science training course in Mumbai, India is designed to cater to both beginners and those with some background in Data Science.

What software and tools do I need for this course?

All the tools required for this training will be installed during the course.

Will I receive a certificate upon course completion?

Upon completion of the course, you will receive an official global Data Science certificate. This certificate serves as an official badge of your successful course completion, highlighting your expertise.

Can I interact with instructors and ask questions during the course?

Absolutely! Our instructors are always available to answer all your questions and solve your doubts.

Are there any real-world projects or case studies in the course?

Yes, we incorporate real-world live projects and case studies into the course to help you apply what you’ve learned in practical scenarios.

Is there a money-back guarantee if I’m not satisfied with the course?

We offer a satisfaction guarantee. If you are not satisfied with the course within a specified timeframe, you can request a refund.