Rishav Kumar
Principal Data Analyst
Beginner to Job-Ready
Data Science with AI
From Data to Decisions — Zero to Data Scientist in 3 Months
A 3-month, 96-hour weekend program taking you from Python and statistics to machine learning, time series, deep learning and agentic AI. 6 real-world capstone projects, mock interviews and placement prep across 5 phases.
- 3 Months · 12 Weeks · 96 Hours
- Live weekend cohort (Sat & Sun) + recordings
- Live instructor-led on Zoom
- 1-year placement support
Enrol online
₹19,999₹34,999
or ₹950/month with EMI
Secure checkout via Razorpay — UPI, cards, netbanking and wallets. Instant access in your dashboard.
Next cohort starts in
--Days
--Hours
--Minutes
--Seconds
Batch begins 26 September 2026
Talk to a mentor
Get the detailed syllabus, batch dates and fee options.
Overview
What this program covers
From Data to Decisions — Zero to Data Scientist in 3 Months. 72 hours of core training plus a 24-hour case study and interview track, delivered live on Saturdays and Sundays. Built for students, non-tech backgrounds, analysts/developers moving into AI/ML and career-break returners — no prior coding needed.
This program is for you if:
- You're a student or recent graduate building job-ready data science skills — no prior coding needed
- You're from a non-tech background (commerce, arts or any other stream) moving into data
- You're an analyst, software developer or engineer moving into a dedicated AI/ML role
- You're returning to work after a career break and want a structured, supportive format
Outcomes
What you will be able to do
Write confident Python for data work with NumPy, Pandas, Matplotlib and Seaborn
Apply statistical inference, hypothesis testing and A/B experiment design
Build, tune and evaluate machine learning models with Scikit-Learn and XGBoost
Forecast business demand with ARIMA and Prophet time series models
Train neural networks with TensorFlow and understand transformer architectures
Ship an agentic AI support agent with LangChain, RAG and the Claude/OpenAI APIs
Build a GitHub portfolio of 6 real-world projects and clear 3 mock interviews
Meet your instructors
Learn directly from working data professionals
Muskaan Bhagat
Data Scientist
Student success stories
Real career transformations from Skillancy alumni
“One of the most impactful learning experiences of my analytics journey — especially SQL and Python strengthened my analytical thinking.”
Sanghamitra Phukan
Oracle
“Skillancy's mentorship went beyond interview prep — teaching me how to think critically and solve real-world problems, with end-to-end support throughout.”
Jayant Khanna
PayPal
“Structured, industry-aligned training helped me secure a role at ZoomInfo. Hands-on, real-world problem-solving strengthened my technical skills and confidence.”
Mohammad Ammar
ZoomInfo
“Key to transitioning into analytics without prior experience. Structured mentorship and rigorous training built strong technical confidence.”
Umashankar Pati
RSystems
“Crucial in helping me transition into a technical role. Patient mentorship and guidance in resume building and interview prep were instrumental.”
Ankita Singh
EXL Services
Hiring partners
Where our alumni work
Target
Weekly schedule
Week-by-week curriculum
Five phases, twelve weeks, one continuous build from spreadsheets to a placement-ready portfolio.
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
- Saturday 10:00 AM – 12:00 PM — Core Session 1
- Saturday 1:30 PM – 3:30 PM — Core Session 2
- Sunday 10:00 AM – 12:00 PM — Core Session 3
- Sunday 1:30 PM – 3:30 PM — Case Study & Interview Practice
1
Phase 1 — Python & Statistics Foundations
Weeks 1–2Core content (6 hrs) · Week 1
- Python fundamentals in Google Colab: variables, data types, lists & dictionaries
- Conditional statements, loops & functions
- NumPy arrays, indexing & broadcasting
- Descriptive statistics: mean, median, mode, spread
Sunday case / interview track (2 hrs)
- Python & NumPy interview Q&A
Core content (6 hrs) · Week 2
- Pandas DataFrames, filtering & grouping
- Data cleaning & handling missing data
- Exploratory Data Analysis & outlier handling
- Visualization with Matplotlib & Seaborn
Sunday case / interview track (2 hrs)
- Case study — full EDA on a messy real-world dataset
Capstone project
Retail Sales & Inventory Trend Analysis — Tata Cliq
2
Phase 2 — Statistical Inference & Experimentation
Weeks 3–4Core content (6 hrs) · Week 3
- Probability distributions & the Central Limit Theorem
- Confidence intervals & p-values
- Hypothesis testing: t-tests & chi-square tests
- Common pitfalls: multiple testing, p-hacking
Sunday case / interview track (2 hrs)
- Statistical inference interview Q&A
Core content (6 hrs) · Week 4
- A/B test design: control vs. treatment, randomization
- Sample size & statistical power
- Interpreting results & avoiding false positives
- Communicating experiment results to stakeholders
Sunday case / interview track (2 hrs)
- A/B testing & experimentation case study
Capstone project
Checkout Flow A/B Test Analysis — Swiggy
3
Phase 3 — Machine Learning
Weeks 5–8Core content (6 hrs) · Week 5
- The ML workflow: train/test split, cross-validation
- Linear & logistic regression
- Model evaluation metrics
- Bias-variance tradeoff
Sunday case / interview track (2 hrs)
- ML fundamentals interview Q&A
Core content (6 hrs) · Week 6
- Decision trees & random forests
- Gradient boosting with XGBoost
- Hyperparameter tuning
- Handling imbalanced datasets
Sunday case / interview track (2 hrs)
- ML / XGBoost interview Q&A
Core content (6 hrs) · Week 7
- Unsupervised learning: K-means & hierarchical clustering
- Dimensionality reduction with PCA
- Choosing the right number of clusters
- Evaluating unsupervised models
Sunday case / interview track (2 hrs)
- Unsupervised learning interview Q&A
Capstone project
Customer Segmentation via Clustering — Nykaa
Core content (6 hrs) · Week 8
- Feature engineering & selection
- Model interpretability (SHAP basics)
- Deployment fundamentals
- Case study — end-to-end classification problem
Sunday case / interview track (2 hrs)
- Case study — solving a real classification problem
Capstone project
Customer Churn Prediction Using Machine Learning — Airtel
4
Phase 4 — Time Series & Deep Learning
Weeks 9–10Core content (6 hrs) · Week 9
- Time series components: trend, seasonality, noise
- Stationarity & differencing
- Forecasting with ARIMA & Prophet
- Evaluating forecast accuracy
Sunday case / interview track (2 hrs)
- Time series interview Q&A
Capstone project
Dynamic Surge Pricing Demand Forecasting — Ola
Core content (6 hrs) · Week 10
- Neural network fundamentals with TensorFlow
- Training, activation functions & optimization
- Overfitting & regularization
- Attention mechanisms & the transformer architecture
Sunday case / interview track (2 hrs)
- Deep learning interview Q&A
5
Phase 5 — Agentic AI
Weeks 11–12Core content (6 hrs) · Week 11
- NLP with HuggingFace transformers: tokenization & embeddings
- Text classification & sentiment analysis pipelines
- Function/tool calling with the Claude & OpenAI APIs
- Structured outputs & JSON schemas
Sunday case / interview track (2 hrs)
- Hands-on build — Part 1: classify & triage tickets using sentiment analysis & function calling
Capstone project
AI Customer Support Agent — Part 1: Ticket Classification — Flipkart
Core content (6 hrs) · Week 12
- Building multi-step agents with LangChain
- Tool orchestration & agent memory
- Retrieval-augmented generation (RAG) basics
- Deploying & presenting your AI agent
Sunday case / interview track (2 hrs)
- Hands-on build — Part 2: complete & present the LangChain + RAG agent; live mock interviews with feedback; resume & LinkedIn review
Capstone project
AI Customer Support Agent — Part 2: Full LangChain + RAG Agent — Flipkart
Capstone projects
Real-world projects delivered across the program
Python · Tata Cliq
Retail Sales & Inventory Trend Analysis
Pandas-based EDA & descriptive stats applied to a multi-category retail dataset
Stats · Swiggy
Checkout Flow A/B Test Analysis
Hypothesis testing & power analysis evaluating a checkout redesign experiment
ML · Nykaa
Customer Segmentation via Clustering
K-means clustering & PCA segmenting customers by purchase behavior
ML · Airtel
Customer Churn Prediction Using Machine Learning
Scikit-Learn & XGBoost models predicting subscriber churn risk
Time Series · Ola
Dynamic Surge Pricing Demand Forecasting
ARIMA/Prophet forecasting models predicting ride demand for surge pricing
Agentic AI · Flipkart
AI Customer Support Agent
A two-week build: ticket classification with function calling, then a full LangChain + RAG agent
Toolkit
Tools and platforms you will use
Python
NumPy
Pandas
Matplotlib
Seaborn
- SSciPy
Scikit-Learn
- XXGBoost
- PProphet
TensorFlow
HuggingFace
LangChain
OpenAI
Google Colab
Get certified
Certificate of Completion
Every learner who completes the program receives a Certificate of Completion from Skillancy.
Practice
Python interview questions for data analysts — practise free
Every Data Science with AI learner gets unlimited access to our in-browser practice sets. Start with these Python and SQL interview questions — no signup, code runs in your browser.
Python interview questions for data analyst
30 problems with hints, tests and explanations
- Clean messy city names from a CRM exportEasy
- Total campaign revenue from prices and quantitiesEasy
- Count orders by payment channelEasy
- Bucket engagement scores into segmentsEasy
- Median salary without using a libraryEasy
- Z-score normalise a spend array with NumPyMedium
FAQ
Frequently asked questions
Ready to start learning?
Create your account, request enrolment and start with the free preview sessions.