Data Science Elite Program
in Data ScienceAbout this course
From Data to Deployed Intelligence
ProDAC's Data Science Elite Program takes you from Python fundamentals to machine learning, deep learning, NLP and time series forecasting in 105 hours of live, mentor-led learning, built for people who want to build models, not just dashboards.
Built for Career Outcomes, Not Just Course Completion
Every module, project and mentoring session is designed around one goal: making you genuinely employable as a data scientist.
Industry-Aligned ML Curriculum
From classical machine learning to deep learning and NLP, in the order real data science teams actually use them.
Real Modeling Problems
Practice on messy, real business data, not clean textbook datasets, so your models hold up outside a notebook.
From Classical ML to Deep Learning
A deliberate progression: regression and classification first, then neural networks, then language and time series.
A Portfolio You Can Show
Leave with a portfolio of real-world projects and a capstone model you can walk an interviewer through, step by step.
Structured Interview Prep
Practice explaining model choices, statistics fundamentals and case studies before you sit a real interview.
Learn-by-Doing Format
Every concept, from a regression line to a neural network layer, is paired with a hands-on notebook exercise.
Where This Program Can Take You
Data science skills are in demand across BFSI, healthcare, retail, tech and consulting: anywhere predictions and pattern-finding create an edge.
Indicative Salary Progression (India)
Indicative industry ranges, not a guarantee. Actuals vary by city, company and experience.
Organizations across banking, healthcare, retail and technology are expanding data science teams to build recommendation engines, forecasting models and NLP-driven products, creating consistent demand for people who can build, not just describe, models.
The Data Science Stack You'll Master
A focused, in-demand stack: real tools, real colors, hover to see the name.
Your 105-Hour Journey, Mapped Out
A deliberate progression from fundamentals to deployable models, about 13 to 14 weeks at a steady, structured pace.
Foundation
Career prep, Python & OOP
Data Wrangling
NumPy, Pandas & EDA
Machine Learning
Regression, classification & more
Deep Learning
ANN, RNN & CNN
NLP & Time Series
Language & sequential data
105 Hours, 9 Modules, Zero Filler
Tap any module to see exactly what you'll learn, build and submit.
Topics
- Resume building & LinkedIn setup
- GitHub basics & version control
- SDLC & Agile methodology
- Understanding the data science ecosystem
Practical Exercises
- Create and push your first GitHub repository
- Set up a baseline data science resume
Assignments
- Publish a GitHub profile README
- Complete a self-assessment of current skill gaps
Topics
- Variables, data types & control flow
- Functions & modules
- Lists, dictionaries, tuples & sets
- File handling & error handling
Practical Exercises
- Build a data-cleaning script for a messy CSV file
- Write reusable functions for common data tasks
Assignments
- Solve 20+ Python programming problems
- Build a simple command-line data utility
Topics
- Classes & objects
- Inheritance & encapsulation
- Polymorphism
- OOP design for data pipelines
Practical Exercises
- Model a simple data-processing pipeline using classes
Assignments
- Build a small OOP-based data validation tool
Topics
- NumPy arrays & vectorized operations
- Pandas Series & DataFrames
- Data cleaning & transformation
- Merging, joining & groupby aggregation
Practical Exercises
- Clean and transform a multi-source dataset
Assignments
- Build an automated data-preparation pipeline
Topics
- Descriptive statistics
- Univariate & bivariate analysis
- Outlier & correlation analysis
- Visualization for modeling decisions
Practical Exercises
- Perform EDA ahead of a modeling task
Assignments
- Submit an EDA report identifying features worth modeling
Topics
- Linear & logistic regression
- Decision trees, random forests & boosting
- Clustering & dimensionality reduction
- Model evaluation, tuning & cross-validation
Practical Exercises
- Build and compare 5+ classification and regression models
Assignments
- Train, tune and evaluate a model on a real business dataset
Topics
- Artificial Neural Networks (ANN) fundamentals
- Convolutional Neural Networks (CNN) for images
- Recurrent Neural Networks (RNN) for sequences
- Activation functions, backpropagation & optimizers
Practical Exercises
- Build and train a neural network from scratch
- Train an image classifier with a CNN
Assignments
- Design and tune a deep learning model for a chosen problem
Topics
- Text preprocessing & tokenization
- Bag-of-words, TF-IDF & word embeddings
- Sentiment analysis & text classification
- Named entity recognition basics
Practical Exercises
- Build a text classification pipeline end to end
Assignments
- Train a sentiment analysis model on real review data
Topics
- Trend, seasonality & stationarity
- ARIMA & exponential smoothing
- Forecast evaluation metrics
- Practical forecasting workflows
Practical Exercises
- Build a demand or sales forecasting model
Assignments
- Forecast and evaluate a real time series dataset
The Predictive Intelligence Capstone
One end-to-end engagement that mirrors a real data scientist's workflow: source and clean a dataset, engineer features, train and tune a model (classical ML or deep learning, your choice), evaluate it honestly, then present your findings and recommendations to a mentor panel, just like a real stakeholder review.
This is the project you'll lead with in interviews.
Pull and prepare a real-world dataset with Python and Pandas.
Engineer features and train a classical ML or deep learning model.
Measure performance honestly and interpret what the model is really doing.
Walk a mentor panel through your findings and recommendations.
From Last Module to First Offer
Skill-building is half the journey. The other half is making sure you can sell those skills.
Resume Building
Craft a results-driven, data-science-ready resume that survives the first filter.
LinkedIn Optimization
Build a profile that recruiters actually find and message.
Mock Interviews
Practice live with technical, statistics and case-based interview rounds.
Portfolio Reviews
Get mentor feedback on every model and notebook before you ship it.
Career Mentoring
1:1 guidance on roles, companies and negotiation.
Aptitude & Stats Training
Sharpen the statistics and reasoning fundamentals data science interviews test first.
Built for Every Starting Point
Hover over each path to see who it's built for. Whichever stage you're starting from, the curriculum meets you there.
Students & Freshers
Build job-ready data science skills, especially useful if you come from a math, stats or engineering background.
Working Professionals
Move from analytics or BI into data science with weekend and evening batches that fit your job.
Career Switchers
Move into data science from a non-tech background with a structured, mentor-guided path.
Software Developers
Already write code? Add machine learning and deep learning to a strong existing engineering base.
Data Science Elite Program
Verify at prodac.iokode.com
A Certificate That Verifies Real Skill
Your ProDAC Certified Data Science Elite certificate is a digitally verifiable record of what you actually built.
Each certificate carries a unique ID that can be validated on ProDAC's verification page.
Add it directly to your profile to signal verified, project-backed skills to recruiters.
Comes with real-world projects and a capstone model as proof, alongside the certificate.
Ready to Accelerate Your Career?
Talk to our admissions team and find out if this program is the right next step for you.
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