Data Science Training

Become a job-ready Data Scientist in 6–8 months — no coding or IT background required. TryCatch Classes’ Data Science course in Mumbai covers Python, Machine Learning, Deep Learning, Generative AI, SQL, Power BI, Tableau, and AWS Cloud through 245+ hours of hands-on training. Learn offline at our Borivali classroom or live online, build a portfolio of real industry projects, and get placement support until you’re hired — our highest fresher package so far is ₹8.5 LPA.

What you'll learn

Python programming from scratch — variables to OOP, NumPy, and Pandas, designed for students from non-IT backgrounds.
Statistics & Exploratory Data Analysis — the maths behind every model, taught with real datasets, Matplotlib, and Seaborn.
Machine Learning end to end — regression, classification, clustering, ensemble methods, and how to pick the right model for each business problem.
Deep Learning with TensorFlow & Keras — ANNs, CNNs, and RNNs, plus transfer learning on real image and text data.
Generative AI & LLMs — prompt engineering, LangChain, Hugging Face, and building applications with GPT and Gemini APIs.

NLP & Chatbots — tokenization to sentiment analysis, ending with a working chatbot you can demo in interviews.
SQL, Power BI & Tableau — query, model, and visualize data the way analytics teams do in industry.
AWS Cloud for Data Science — deploy your work with S3, EC2, and managed analytics services.
A hire-ready project portfolio — customer segmentation, a recommender system, and a chatbot, built as capstone projects.

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
Generative AI vs AI Agents vs Agentic AI
Open Source vs Closed Source models
Transformer architecture: Attention, encoder-decoder, self-attention
Learn the architecture of Transformers like GPT & BERT

LLMs, Prompt Engineering & OpenAI Integration

Prompt engineering
Understanding Chat, Completion, and Instruction-tuned models
Prompt Engineering techniques: Few-shot, zero-shot, chain-of-thought
Explore LLM providers: Gemini, Open AI, Hugging Face, LLAMA
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 Systems

Why RAG
Ingestion: PDF, DOCX, HTML, tables, scanned documents (OCR), metadata extraction
Chunking Strategies: fixed-size, recursive, semantic, parent-document, table-aware — and how to choose
Embedding Models: Dimensions
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)
Generate vector embeddings using OpenAI or Hugging Face
How embedding models work
Storing, indexing, and retrieving large document
FAISS, Chroma, Qdrant, Pinecone, Weaviate, ; selection criteria;
Index types (Flat, HNSW, IVF) and the recall/latency trade-off.
Similarity metrics (cosine, dot, L2)
Metadata filtering and namespace design
Retrieval quality: top-k tuning, hybrid search (BM25 + dense), re-ranking with cross-encoders / Cohere Rerank
Query rewriting, multi-query
Self-query, citation and source attribution

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 on Cloud

AWS APP Services
CI/CD Boards, Repos
GitHub Source Control
App Deployment EC2
Serverless Application
Store Docker Images
Kubernetes Services
ML Development & Deployment
Secrets Management
Object Storage
LLM Models in the cloud
RAG & Semantic Search
Log analysis
Application performance monitoring

Security

Authentication (API keys, JWT, OAuth basics)
secrets management (env vars, AWS Secrets Manager, never in Git)
Input validation
Output sanitisation
Rate limiting and quotas
CORS
Audit logging
Tenant isolation

Cost Optimisation

Token accounting
Prompt compression
Model routing (cheap model first, escalate on failure)
Response caching
Semantic caching
Batching
Streaming to reduce timeout waste
Context trimming
Human-in-the-loop review queues

End-to-End Capstone Projects for Data Scientists

Intelligent Agentic Ai Personal Assistant
Build an Enterprise RAG Knowledge Assistant and a Multi-Agent Research Platform
Customer Support App
AI Resume Analyser
Product Recommendation System for Ecommerce
Social Media Sentiment Analysis and Trend Prediction
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 Generative AI Python Agentic AI Deep Learning Langchain LangGraph Power BI MySQL NLP Tableau Training PowerBI Training Course Data Analytics Training RAG Applications

Course Certification

Finish the course and earn the TryCatch Classes Data Science Certification — proof of 245+ hours of hands-on training across Python, Machine Learning, Generative AI, and BI tools.
Add it to your resume and LinkedIn, and back it up with the live projects you built during the course. Recruiters don’t just see a certificate; they see work they can click through.

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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Software engineer
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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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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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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)

How long is the Data Science course in Mumbai?

The complete Data Science training takes 6–8 months (245+ hours) depending on the track you choose. Weekday and weekend batches are available, and every module combines live teaching with hands-on project work — you write code from week one.

Can I learn Data Science without an IT or coding background?

Yes. Most of our students come from Commerce, Arts, Science, or Mechanical backgrounds. The course starts with Python from absolute zero and builds statistics and ML step by step. If you can use a laptop, you can learn Data Science here.

Is the course online or offline?

Both. Attend offline classes at our Borivali (West) centre in Mumbai, or join the same sessions live on Zoom/Google Meet. Online students get identical projects, doubt-solving, and placement support.

What is the placement support after the course?

Placement preparation is built into the course: resume building, an interview Q&A library, mock interviews, and access to our private jobs group. We provide 100% placement assistance and continue supporting you until you’re placed — our highest fresher package so far is ₹8.5 LPA.

What salary can a fresher Data Scientist expect in Mumbai?

Fresher data science roles in Mumbai typically start between ₹4–8 LPA depending on your portfolio and interview performance. Our students have secured packages up to ₹8.5 LPA as freshers.

Will I get a certificate after completing the course?

Yes — an industry-recognized Data Science certification from TryCatch Classes, plus a portfolio of live projects (customer segmentation, recommender system, chatbot) that carries even more weight in interviews.

What tools and software will I learn?

Python, NumPy, Pandas, scikit-learn, TensorFlow, Keras, LangChain, Hugging Face, MySQL, Tableau, Power BI, and AWS. All software is free/open-source or trial-based, and we help you install everything in the first week.

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.