role-based roadmap · Data
Data Science Roadmap
A structured path covering mathematics, programming, data wrangling, machine learning, and professional skills needed to land a data science role.
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1. Stage 1: Foundations
Python Programming Basics
Python is the primary language for all data science work.
Mathematics for Data Science
Linear algebra, calculus, and probability underpin all ML models.
Command Line & Git Basics
Version control and CLI are essential for any technical role.
2. Stage 2: Data Manipulation & Analysis
NumPy
Array operations are the building blocks of data computation.
Pandas
Pandas is the standard library for tabular data wrangling.
SQL for Data Analysis
Querying relational databases is a core day-to-day data skill.
Exploratory Data Analysis (EDA)
EDA reveals patterns and informs every modelling decision.
3. Stage 3: Data Visualization
Matplotlib
Matplotlib is the foundational plotting library in Python.
Seaborn
Seaborn produces statistical charts with minimal code.
Interactive Dashboards with Plotly
Interactive visuals communicate insights to non-technical stakeholders.
4. Stage 4: Machine Learning
Scikit-learn & ML Fundamentals
Scikit-learn covers the full classical ML workflow end-to-end.
Supervised Learning Algorithms
Regression and classification are the most common real-world tasks.
Unsupervised Learning & Clustering
Clustering and dimensionality reduction uncover hidden data structure.
Model Evaluation & Validation
Proper evaluation prevents overfitting and unreliable models.
5. Stage 5: Deep Learning & Advanced Topics
Neural Networks with TensorFlow/Keras
Deep learning powers modern NLP, CV, and recommendation systems.
Natural Language Processing (NLP)
Text is the most abundant data type; NLP skills are highly valued.
Time Series Analysis
Forecasting is critical in finance, supply chain, and IoT domains.
6. Stage 6: Data Engineering & MLOps Essentials
Data Pipelines & Workflow Orchestration
Production data science requires reliable automated pipelines.
Cloud Platforms for Data Science
Most employers use cloud infrastructure for training and deployment.
Model Deployment with APIs
Deployed models create real business value via accessible endpoints.
Docker for Data Science
Containers ensure reproducible environments across dev and production.
7. Stage 7: Portfolio, Interview Prep & Job Readiness
Kaggle Competitions & Projects
Hands-on competition projects demonstrate real skills to employers.
Building a Data Science Portfolio
A strong portfolio is the most persuasive job application asset.
Statistics & Probability Interview Prep
Stat fundamentals are heavily tested in data science interviews.
SQL & Coding Interview Practice
Coding challenges are standard in every data science hiring process.