Become a Data Scientist
Everything you need to know; What data scientists do, what tools they use, and exactly how to start building your skills.
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What Does a Data Scientist Actually Do?
Data Scientists explore data, build predictive models, and uncover insights that guide smarter business decisions.
Build and Evaluate Predictive Models (20 - 25%)
You may create and evaluate machine learning models, using techniques like regression, clustering, decision trees, or neural nets. You balance accuracy with interpretability and performance. Businesses want actionable data, so hypotheses must turn into practical outcomes.
Working Example: You build a model to predict sales for the next quarter using historical and promotional data.
Communicate Insights and Deploy (15 - 20%)
You’ll present findings to stakeholders in simple terms, build dashboards, or work with engineers to deploy models. Strong storytelling and communication are key to making sure models are used. Multiple business areas benefit from data insights that drive change.
Working Example: You explain the output of a churn model to a marketing lead and help them design a retention campaign based on it.
Frame Business needs into Data needs (25 - 30%)
You'll work closely with stakeholders to translate business questions into data driven hypotheses. Whether it’s improving a recommendation system or predicting customer churn, you start by understanding what needs answering, and formulate the models to answer them
Working Example: A product manager wants to increase user retention. You explore the data to find patterns in user activity that predict churn.
Gather, Explore, and Model Data (30 - 40%)
You'll spend a large amount of time acquiring datasets (internal or external), cleaning them, engineering features, and building models using Python, R, or other tools. This is where the “science” really happens. You will test and re-test data to ensure outcomes are consistent with expectations.
Working Example: You clean millions of log files to train a classification model that predicts fraudulent transactions.
Who Do Data Scientists Work With?
Data Scientists work across the business - their value is in bridging complex models with real-world impact. You'll collaborate with:
Data Engineers - To source clean, structured data
Machine Learning Engineers / MLOps - To help scale and deploy models into production
Product Managers - To define what problems are worth solving
Business Stakeholders (Marketing, Finance, Operations) - To apply model insights
Foundational Skills
Python
Python is the most popular language in data science for a reason. It’s powerful, beginner-friendly, and has libraries for everything from data cleaning (pandas, numpy) to visualisation (matplotlib, seaborn) and machine learning (scikit-learn, XGBoost).
Why It Matters?
These are the core skills you’ll need to become job ready, and we've provided some recommended resources to help get you prepared
Stats & Probability
Without statistics, you're just guessing. Core concepts like distributions, p-values, confidence intervals, and hypothesis testing let you validate assumptions and ensure your models are meaningful. Stakeholders require proof to make business change.
Where to Start
Why It Matters?
Python for Data Analysis
Where to Start
Naked Statistics
Writing scripts to clean and analyse datasets
Building predictive models for customer churn
Automating repetitive analysis tasks
Defining datasets for ML models
Redesigning data engineering output
Real World Use Cases
Pro Tip
Start with pandas and matplotlib before jumping into machine learning, a strong foundation give you better models later
Python for Data Science
Jupyter Notebook
Real World Use Cases
Pro Tip
Testing if a campaign actually increased signups
Articulating variance in customer spend by region
Building confidence intervals around predictions
Ratifying churn prediction by day, month, year
Predicting signup duration within forecast
You don’t need to be a mathematician. Focus on applied, not theoretical, stats for real-world projects to support business change.
Intro to Statistics
Khan Academy for stats
Data Wrangling
Real world data is messy. Learning how to handle missing values, outliers, duplicates, and strange formatting is essential before you model anything. Exploratory Data Analysis helps uncover patterns, anomalies, and distributions.
Why It Matters?
Data Visualisation
Data science isn’t just about analysis, it’s about storytelling. Knowing how to present your findings visually makes them accessible to decision-makers and gets your work used. Simple visualisations will normally beat complex ones
Why It Matters?
Identifying why revenue doesn't match reports
Discerning missing data is random or systematic
Preparing datasets for modeling without leakage
Re-evaluating seed dataset with engineers
Working with SMEs to understand source data
Real World Use Cases
Pro Tip
You’ll spend more time cleaning data than building models. Master .groupby(), .isnull(), and .describe() in pandas.
Real World Use Cases
Pro Tip
Highlighting churn trends in a stakeholder report
Visualising correlations between ads and sales
Creating clear summaries of model outputs
Identifying data composition for further analysis
Establishing baselines for predictive success
Start simple. Line charts, bar plots, histograms, and numeric matrices are more powerful than flashy visuals when used right.
Where to Start
Data Science from Scratch
Where to Start
Storytelling with Data
Pandas Mastery
Google Colab
Data Visualisation, Python
Tools for Data Science
Advanced Skills
Machine Learning
Once your data is clean and structured, machine learning helps uncover patterns and make predictions. From classification and regression to clustering and recommendation engines, ML is where data science meets real-world automation.
Why It Matters?
These are the aspirationl skills you’ll need to excel as a Data Scientist
Feature Engineering
Great models don’t come from throwing data at algorithms. Feature engineering turns raw data into meaningful signals, and deployment into accessible tables are what make your work usable by others, to be delivered to stakeholders.
Where to Start
Why It Matters?
Hands-On ML
Where to Start
Feature Engineering for ML
Predicting which customers are likely to churn
Recommending products from past purchases
Forecasting demand for inventory management
Distributing sales campaign renewals
Predicting performance to identify sales resource
Real World Use Cases
Pro Tip
Start with logistic regression and decision trees before diving into deep learning, simple foundational models win most business cases.
Machine Learning A-Z
Real World Use Cases
Pro Tip
Creating a feature that tracks user activity
Encoding time-based patterns into fraud models
Deploying a prediction service that updates via API
Profiling customer types for use as a feature
Establishing baseline GP for predictive growth
Most models fail because they never get used, the outputs are either inaccessible or not actionable, learn deployment early to bridge the last mile.
Deploying ML Models
Big Data Tools
Business Data rarely fits in spreadsheets anymore. Knowing how to use platforms like AWS, Azure, or Google Cloud, and tools like BigQuery, S3, or Databricks, lets you work on real production data at scale.
Why It Matters?
Experiments & Testing
Data scientists don’t just explore, they validate. A/B testing lets you compare changes and measure outcomes with statistical rigor. Understanding how to design and interpret experiments is key in product, marketing, and UX teams.
Why It Matters?
Running ML pipelines in cloud environments
Querying terabytes of data in BigQuery for analysis
Processing files in S3 buckets for model training
Partitioning forecasts using Databricks
Pipeline big data in the cloud, visualise results only
Real World Use Cases
Pro Tip
Pick one cloud provider and go deep, Google Cloud Platform is often the most accessible for analytics and Machine Learning work.
Real World Use Cases
Pro Tip
Testing landing pages for signup conversion
Evaluating context impacts on click-through rates
Measuring user retention based on feature rollouts
Identifying conversion based on contact strategy
Correlating pitched product conversions
Always design your tests before you collect data, not after. Predefined metrics and thresholds prevent cherry-picking.
Where to Start
Cloud Data Science
Where to Start
Controlled Experiments
Big Data: Hadoop
Test & Behaviour Dev
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