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Data Science
Statistics, code, and modeling to predict and explain.
Data science sits between analytics and engineering: you use statistics and machine learning to model problems, not just report on them. Expect more maths and Python than analytics, and a longer runway.
The route
Python & maths foundations 6–10 weeks
Solid Python plus the statistics and linear algebra that everything later depends on.
- Python
- NumPy / pandas
- Probability & statistics
- Linear algebra basics
Data wrangling & EDA 6–8 weeks
Most of the job is cleaning and exploring data. Get genuinely good at pandas and visualization.
- pandas
- Matplotlib / seaborn
- Feature engineering
- Exploratory analysis
Machine learning 8–12 weeks
Supervised and unsupervised learning, evaluation, and avoiding the classic traps.
- scikit-learn
- Regression & classification
- Cross-validation
- Overfitting & metrics
Projects, deployment & portfolio 8–12 weeks
Ship a model someone can use. Notebooks alone don't land interviews — deployed projects do.
- End-to-end ML project
- Streamlit / FastAPI
- Git & reproducibility
- Communicating results
Who it's for
- Analysts who want to model, not just report
- STEM grads comfortable with maths
- Engineers moving toward ML
Career outcomes
- Data Scientist
- ML Analyst
- Quantitative Analyst
Prefer guided learning?
If self-paced study keeps stalling, a mentored cohort can get you to job-ready faster. DTI runs structured programs in Albania across software, data, and security.
Explore DTI →