Knows some Python.
- – Can write basic functions and loops
- – Has used notebooks or scripts
- – Is unsure how data science projects fit together
- – Has no deployed data project
← Back
SAAI 15-Day Applied Data Science Program
Before
After 15 Days
01
02
03
04
05
06
Buy Now
Buy Now
15-Day Applied Data Science Program
Program Cost: INR 7999.00 (includes GST)
15-Day Applied Data Science Program
From raw data to a working product.
Learn the complete data science workflow by analysing real data, building machine learning models, deploying a solution and defending every technical decision.
Python basics required
Project-first learning
Portfolio-ready output
✓
Real dataset. Real decisions.
Your end-to-end workflow
01
Frame the question
Turn a real problem into a measurable task
Design
02
Prepare the data
Clean, inspect and engineer useful features
Develop
03
Train the model
Compare algorithms and select the right metric
Develop
04
Ship the solution
Build an API or app and deploy it publicly
Deploy
05
Defend the work
Explain assumptions, trade-offs and limitations
Defend
↗
One deployed capstone
15
focused learning days
5
connected learning modules
3
practical project sprints
1
deployed portfolio project
The Transformation
Not just “I know Python.”
The learner moves from writing scripts to solving a complete data problem.
Can build and defend a data product.
- ✓ Frames a real analytical problem
- ✓ Cleans and explores imperfect data
- ✓ Trains and compares machine learning models
- ✓ Deploys and presents a portfolio project
What Learners Will Learn
Five modules. One connected workflow.
Each module adds a working layer to the same final data science project.
L Learners understand
- ✓
B Learners build
- ✓
↗
Module evidence
15-Day Journey Board
Every day produces evidence.
Learn a concept, apply it immediately and leave the day with something built.
Day 01
Problem Canvas
Think Like a Data Scientist
Frame the problem, target and decision.
Day 02
Data Notebook
NumPy + Pandas
Load, filter, group and combine datasets.
Day 03
Cleaning Pipeline
Clean the Data
Fix missing, duplicate and invalid values.
Day 04
Insight Note
Statistics That Matter
Interpret spread, relationships and uncertainty.
Day 05
EDA Notebook
Explore the Dataset
Find patterns, segments and anomalies.
Day 06
Visual Report
Tell the Data Story
Choose charts that answer real questions.
Day 07
Feature Set
Engineer Features
Represent the problem in a model-friendly form.
Day 08
Baseline Model
Machine Learning Basics
Train, validate and establish a baseline.
Day 09
Model Comparison
Regression
Predict numbers and inspect residual errors.
Day 10
Metric Defence
Classification
Predict categories using the right metrics.
Day 11
Reusable Pipeline
Improve the Model
Tune, cross-validate and build pipelines.
Day 12
Prediction API
Build the API
Serve predictions through a validated endpoint.
Day 13
Live Application
Create the App
Make the model usable and deploy it publicly.
Day 14
Capstone Build
Portfolio Build Sprint
Integrate analysis, model and product.
Day 15
Final Defence
Defend the Project
Present trade-offs, limitations and next steps.
Swipe or scroll sideways to explore all 15 days.
Inside the Program
Learners do more than watch lessons.
Every concept becomes an action, a decision or a portfolio artefact.
Investigate messy data
Work with missing values, inconsistent labels, outliers and imperfect records.
Ask better questions
Translate a broad problem into variables, hypotheses and measurable outcomes.
Run model experiments
Compare algorithms, metrics and feature choices instead of chasing one score.
Build a prediction API
Turn a trained pipeline into a usable service with validated inputs and outputs.
Deploy a live application
Give users a simple interface and publish the project beyond the notebook.
Defend every decision
Explain data choices, evaluation metrics, trade-offs, risks and limitations.
The Final Capstone
One project. Complete proof of capability.
Learners finish with a public project that shows how they think, analyse, model, build and communicate—not just a certificate.
✓ Problem statement
✓ Clean analysis notebook
✓ Trained ML pipeline
✓ Prediction API or app
✓ Public deployment
✓ README + defence document
# capstone_pipeline.py
problem = "Predict customer churn"
data_quality = "validated"
features = engineer_features(df)
model = train_pipeline(features)
metric = "recall"
api = build_prediction_api(model)
deployment = "live"
✓ model validated
✓ application deployed
✓ decisions documented
✓ ready for defence
Choose a Project Track
One method. Multiple domains.
Learners can apply the workflow to a problem they genuinely care about.
₹
→
Business Intelligence
Churn, sales, conversion or customer behaviour.
%
→
Financial Intelligence
Risk, credit behaviour or transaction patterns.
⚡
→
Sports Intelligence
Performance, consistency or match outcomes.
#
→
Social Intelligence
Sentiment, reactions, audience or trend analysis.
◎
→
Public Data Intelligence
Traffic, education, health, environment or cities.
✦
→
Product Intelligence
Retention, engagement or purchase behaviour.
The Working Toolchain
Tools are learned through use.
No disconnected software tour. Each tool appears when the project needs it.
Python problem solving
NumPy numerical work
Pandas data handling
Matplotlib visualisation
Seaborn exploration
Scikit-learn modelling
FastAPI model serving
Streamlit application layer
GitHub versioned portfolio
How Learners Are Evaluated
We assess decisions, not button-clicking.
The final score reflects both technical execution and the ability to explain the work.
Project evidence
Every major stage contributes visible, reviewable proof.
Problem framing
10%
Data preparation
15%
Analysis
15%
Machine learning
30%
Deployment
10%
Documentation + defence
20%
Defence questions
Learners must explain what they did, why they did it and where it can fail.
Why is this the right problem to solve?
Why did you choose this evaluation metric?
What evidence shows the model is not overfitting?
Which data limitation could change the result?
What would you improve in the next version?
Questions Learners Ask
Clear expectations from day one.
Data to Deployment
Build something that proves you can do data science.
Fifteen focused days. One complete workflow. One project learners can show, explain and improve.
Syllabus and Module Index
Explore modules and expand each card to view lessons.
Modules will be published soon.