A data science roadmap for students
A step-by-step path from beginner to job-ready in data science, with the skills and projects to focus on at each stage.
EvoAstra team14 Nov 2026 · 3 min read- Build foundations in Python, statistics and SQL first
- Learn by doing projects, not only courses
- A portfolio of real projects matters more than certificates alone
Data science can feel overwhelming: endless courses, tools and buzzwords. A clear roadmap helps you focus on what matters at each stage.
- Months 1-2Python, Excel and basic statistics
- Months 3-4SQL, data cleaning and visualisation
- Months 5-6Machine learning fundamentals
- Months 7-8Projects and an internship
- Month 9 onwardsPortfolio, interviews and specialisation
Stage one: foundations
Learn Python basics, work with data in pandas, and understand statistics: averages, distributions, correlation and probability. Excel skills remain valuable in many jobs.
Stage two: working with data
SQL lets you pull data from databases, a skill most data jobs ask for. Learn to clean messy data and tell stories with charts in tools such as Power BI or Tableau.
- 1Python
- 2Statistics
- 3SQL
- 4Visualisation
- 5Machine learning
Stage three: machine learning
Understand regression, classification, clustering and model evaluation. Use scikit-learn on real datasets and learn how to explain results to non-technical people.
Stage four: real projects
Projects prove you can apply skills. Pick problems with real data: sales forecasting, customer segmentation, review sentiment, fraud detection. An internship with mentors and deadlines accelerates this stage.
- Three solid projects
- Clean code on GitHub
- Write-ups explaining results
- One dashboard
- Verifiable internship certificate
Keep going
The field changes quickly. Follow practitioners, read case studies and keep building.
Tell us what you’re working on. Free estimate within 24 hours, NDA on request.