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Data Scientist

"Turn raw, chaotic data into decisions that move businesses — the way real data scientists actually do it."

About This Program

Data science isn't about running models on Kaggle datasets. It's about sitting with a business problem, finding the right data, making it usable, building something predictive or explanatory, and communicating what you found to people who need to act on it. This program puts you through that full cycle — on real data, inside a real team, with a mentor who's worked in the field. You won't be graded on accuracy scores. You'll be evaluated on whether your work was useful.

Program Details

Duration
8–12 Weeks
Level
Beginner to Intermediate
Mode
Virtual, Team-Based
Domain
Data Science & Analytics

How the Team Works

Team Lead
Directs the overall analysis approach, owns the final presentation, runs weekly sync
Co-Team Lead
Manages the task board, owns the EDA module, supports teammates on statistics
Data Scientist (You)
Assigned a specific module: data wrangling, statistical analysis, model building or visualization

Weekly Sync — one team meeting to review findings, align on next steps, get mentor input.

Async Work — analysis pushes to GitHub, discussed over Slack. Every module feeds the team's final deliverable.

What You'll Work On

  1. Join the team, understand the business problem, review the raw dataset
  2. Own the data wrangling process for your module — cleaning, formatting, merging
  3. Run exploratory data analysis — find patterns, outliers, distributions, correlations
  4. Conduct statistical hypothesis testing — validate assumptions before modeling
  5. Build and compare predictive models — regression, classification or time series
  6. Evaluate and select the best model — precision, recall, F1, AUC, cross-validation
  7. Build visualizations and a findings report for the team presentation
  8. Present insights to the team — mentor reviews, signs off, report goes to portfolio

What You'll Learn

✓ Statistics and probability for data science

✓ Data collection, cleaning and preprocessing

✓ Exploratory data analysis — patterns before modeling

✓ Hypothesis testing and statistical inference

✓ Supervised learning — regression and classification

✓ Unsupervised learning — clustering and dimensionality reduction

✓ Model building, evaluation and optimization

✓ Data storytelling for technical and non-technical audiences

✓ Time series analysis and forecasting basics

✓ End-to-end data science project lifecycle

Skills You'll Gain

PythonStatistical AnalysisData WranglingEDAML ModelingData VisualizationHypothesis TestingCommunication

Tools & Tech Stack

Languages
Python, R
Data
Pandas, NumPy
ML
Scikit-learn, XGBoost
Visualization
Matplotlib, Seaborn, Plotly
Database
SQL, PostgreSQL
Reporting
Power BI, Tableau
Collaboration
Git, GitHub, Slack

Career Paths

Data Scientist
ML Engineer
BI Analyst
Research Analyst
Product Analyst
Quant Analyst

Capstone Project

Your team takes a real-world business dataset — e-commerce, healthcare, finance or logistics — and delivers a complete analysis with a predictive model and a presentation-ready insight report. You own a specific module. The final deliverable is reviewed and signed off by your mentor.

What Makes This Different

The data is real and messy. The problem is ambiguous. The team is depending on your module. The mentor isn't giving you answers — they're asking better questions. That pressure, that ambiguity, that accountability — that's exactly what data science at a company feels like.

Ready to Become a Data Scientist?

Transform raw data into impactful insights and drive business decisions.

Apply Now
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