Data Scientist
Use statistics and machine learning to answer hard questions with data.
What does a Data Scientist do?
Data scientists build predictive models, run experiments, and extract deep insights from large datasets. You combine programming, statistics, and domain expertise to help organisations understand not just what happened, but why — and what comes next.
Who it fits
Analytical thinkers who enjoy maths, statistics, running experiments, and building models that make predictions.
Best for patient, methodical learners who enjoy theory-grounded work and iterative experimentation.
Skills you need
Tools you will use
Starter projects
Good projects to practise and add to a portfolio.
- Titanic survival prediction on Kaggle
- House price regression model
- Sentiment analysis on product reviews
- Customer churn prediction
// ROADMAP
Data Scientist career roadmap
Estimated time: 12–18 months
Badges show what each resource is. Only items marked certification lead to a professional credential — everything else is learning material.
Python & Mathematics
8–10 weeksData science requires genuine comfort with Python and applied maths. Learn NumPy, pandas, basic statistics, and linear algebra. 3Blue1Brown makes the maths visual and intuitive.
Statistics & Probability
6–8 weeksStatistics is the language of data science. Learn probability distributions, hypothesis testing, p-values, Bayes theorem. StatQuest explains everything clearly without dumbing it down.
Machine Learning Fundamentals
8–10 weeksLearn supervised and unsupervised learning with scikit-learn. Linear regression, decision trees, random forests, k-means clustering. The Andrew Ng course is the gold standard.
Deep Learning
6–8 weeksNeural networks, CNNs, RNNs, and transformers. fast.ai teaches deep learning from a practical, top-down perspective — you build real models before learning all the theory.
Projects & Kaggle Competitions
OngoingJoin Kaggle competitions to practice on real datasets with real scoring. Build 2–3 strong projects for your portfolio. Aim for publishable notebooks with clear analysis.
MLOps & Deployment
4–6 weeksModels in notebooks are not useful — models in production are. Learn to deploy with FastAPI or Flask, track experiments with MLflow, and monitor model performance over time.
// START LEARNING
Data Scientist learning resources
Videos first — they're the fastest way to get moving — then reading, hands-on practice and courses. Only resources badged professional certification award a formal credential.
Data Analysis with Python
freeCodeCamp · 4 hours
Hands-on analysis with Python, NumPy, pandas, Matplotlib and Seaborn.
Learning resource. This does not award a professional certification.
Machine Learning with Python and Scikit-Learn
freeCodeCamp · 18 hours
An end-to-end introduction to model training, evaluation and practical machine learning.
Learning resource. This does not award a professional certification.
Kaggle Learn
Kaggle
Practical micro-courses in Python, pandas, SQL, machine learning, data visualisation and AI.
Certificate availability depends on the course provider.
Machine Learning Crash Course
Google for Developers
A practical introduction to ML concepts with interactive visualisations, exercises and production guidance.
Certificate availability depends on the course provider.
PostgreSQL Tutorial
PostgreSQL
The official hands-on introduction to relational concepts, SQL queries, joins, aggregates and transactions.
Learning resource. This does not award a professional certification.
PyTorch Tutorials
PyTorch
Official tutorials from tensor fundamentals through neural networks, computer vision and distributed training.
Learning resource. This does not award a professional certification.
// PRACTISE
Gain Data Scientist work experience
Build practical evidence before your first role. Provider terms and eligibility can change.
Forage · Employer job simulations
virtual-job-simulation · REMOTE
Complete self-paced tasks designed by employers and compare your work with example solutions.
Extern · Remote company externships
externship · REMOTE
Work on structured real-company projects with flexible remote delivery.
GitHub · Good first issues
open-source · REMOTE
Find newcomer-labelled issues and build evidence of collaboration in public repositories.
// FIND WORK
Find Data Scientist jobs
Start with “Data Scientist”. Platforms without stable public search URLs open with this suggested phrase.
LinkedIn Jobs
Broad professional job search with keyword, location, experience-level and remote filters.
Wellfound
Startup and technology roles with company and compensation context.
Dice
Specialist technology roles across engineering, data, security and infrastructure.
Built In
Technology and startup job discovery, including remote and city-focused listings.
UK visa and international opportunities
Sponsorship depends on the employer, vacancy and current immigration rules. Use the linked official guidance and verify every role before applying.
Going independent
Data science consulting for businesses who need ML insights without a full-time hire. The ability to frame business problems in statistical terms is extremely valuable.
Data Science Consulting
Medium effortHelp businesses apply machine learning and statistical analysis to their specific problems.
Examples
- Customer segmentation and churn prediction for SaaS
- Demand forecasting for retail and e-commerce
- Fraud detection model building
- A/B test design and statistical analysis
Getting started
- 1. Position around a specific industry — "data science for retail operations"
- 2. Build a portfolio of case studies with real metrics (reduced churn by X%)
- 3. Write LinkedIn content demonstrating data science thinking in plain language
- 4. Target companies with existing data but no interpretation capability
Kaggle Competition Consulting and Coaching
Medium effortCoach other data scientists or help businesses compete in data challenges.
Examples
- Data science bootcamp instruction
- Kaggle competition coaching for corporate teams
- ML model building workshops for analytics teams
Getting started
- 1. Achieve a competitive Kaggle ranking as credibility signal
- 2. Document your approach in blog posts that attract search traffic
- 3. Offer structured 4-week coaching programmes at fixed price
Communities & tools
// EARNING POTENTIAL
Earning potential
2025Significant salary growth over the past decade with demand still strong. The role is increasingly focused on practical ML deployment rather than pure analysis.
Freelance rates
Day rate
£350–£800
Hourly rate
£45–£100
What affects salary
- Strong mathematics background increases earning potential significantly
- MLOps skills (model deployment, monitoring) are increasingly required
- PhD can open research-track roles but is not required for industry
- Kaggle competition performance is a recognised portfolio signal
- Sector matters — fintech and pharma data scientists earn more than media or retail
🌍 US data scientists at Big Tech earn $150k–$250k+. Research roles in the UK are catching up but remain below US levels.
Based on: Glassdoor UK · LinkedIn Salary · DataCamp State of Data · Burtch Works Study. All figures approximate.
// RESOURCES
Free resources to get started
Recommended starting points — no payment required.
- Course
- Course
- Video
- Book
- Video
Paid courses worth considering
These are not required — free resources above can get you far.
- Course
- Course
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