The complete Data Science and AI program. From statistical foundations to production machine learning — everything you need to become the data scientist that drives strategy, not just reports.
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Data Science is the highest-leverage skill in modern business. Here's who succeeds in this program.
Working professionals in analytics and reporting who want to move beyond dashboards into predictive modeling and machine learning.
BAs with business intuition who want to add ML skills and move into data science roles at the intersection of strategy and engineering.
Graduates with quantitative foundations — you already know the math. This program shows you how to operationalize it in real systems.
Computer science graduates who want to specialize in data science and AI to access higher-value roles in the most in-demand field.
Domain experts in high-value sectors who want data roles — your industry knowledge plus data skills is a rare and lucrative combination.
Leaders who need to understand data science deeply enough to hire, manage, and communicate effectively with their data teams.
Salary data from 2024–2025 US placements, LinkedIn Salary Insights, and Glassdoor — not projections.
Tech · Finance · Healthcare
Startups · Enterprise · Cloud
Modern Data Stack Companies
Retail · CPG · Banking
Product-Led Companies
Scale-Ups · Enterprise
Hypothesis testing, confidence intervals, regression — the mathematical core of every data decision.
NumPy, Pandas, ggplot2, tidyverse — write clean, reproducible analysis in both dominant data languages.
Classical and modern ML — regression, trees, ensembles, XGBoost, and neural networks applied to real business problems.
Advanced SQL, dbt, Airflow — query, transform, and pipeline data from warehouse to model with confidence.
Tableau, Power BI, Plotly — tell compelling data stories that executives actually act on.
Design, run, and analyze experiments — the skill that separates analysts from scientists at product companies.
PySpark, Databricks, distributed computing — work with datasets that don't fit in memory.
Neural networks, PyTorch, fine-tuned transformers applied to tabular, text, and time-series data.
Diff-in-diff, instrumental variables, propensity scoring — go from correlation to causation in business contexts.
Choose between the 9-month Professional Certificate or the 12-month Post Graduate Program. Both are rigorous — the PGP goes deeper into causal inference, advanced ML research, and deep learning for enterprise applications.
Extended treatment of statistical learning theory with a focus on causal reasoning methods used at top tech and consulting firms.
Telecom dataset, XGBoost pipeline with feature engineering, deployed as a live prediction dashboard.
Statistical testing engine for product experiments — handles sample sizing, power analysis, and result interpretation.
Time series ensemble using Prophet + LSTM, with a live Power BI dashboard updating every 15 minutes.
Patient readmission model with HIPAA-aware data handling and SHAP explainability for clinical teams.
Collaborative filtering + content-based hybrid system modeled after streaming platform recommendations.
Solve a real business problem in your target industry with full mentor oversight — deployed and portfolio-ready.
Every mentor is actively working at a leading company. They bring current, production-grade knowledge into your sessions.
Leads ML and recommendation systems modules. Builds the models that power Netflix's personalization engine.
Leads SQL, dbt, and data engineering modules. Architected Airbnb's core analytics data models.
Leads causal inference and business analytics modules. Oversees a 40-person data science organization at JPMorgan.
Leads ML modeling modules. Builds recommendation systems at global scale.
Mentors statistics and experimentation. Drives A/B testing and causal inference.
Heads model productionization. Specialist in feature pipelines and MLOps.
The A/B testing framework project was literally used in my Netflix interview. My hiring manager asked me to describe how I'd design an experiment platform from scratch — I walked through exactly what I built in the program. They told me it was one of the most complete answers they'd heard. Five offers later, I chose Netflix.
I was an HR manager who worked with data in spreadsheets. The program rebuilt my thinking from the ground up — statistics, Python, ML — without ever making me feel behind. Shopify hired me as a Data Scientist and I'm building churn models for a product used by millions of merchants.
I spent five years doing everything in Excel. The SQL and dbt modules showed me what modern data work actually looks like. dbt Labs hired me as an Analytics Engineer — I now help other companies build the kind of data infrastructure I learned about in the program.
I was stuck as a reporting analyst. The machine-learning and experimentation modules gave me the depth to pass LinkedIn's rigorous data-science loop on the first try.
Career-switching from teaching felt impossible until Atlia. The portfolio projects — churn prediction, A/B testing — were exactly what hiring managers wanted to see.
From operations dashboards to production ML models. The statistics and Python rigor here is the real deal — I felt over-prepared for every interview.
I crunched spreadsheets as a business analyst. The ML, statistics, and portfolio projects took me all the way to a data-science role at Pinterest in under a year.
Book a free 30-minute career counselling session. We'll help you choose between PCP and PGP, plan your timeline, and answer every question you have.