Python for Data Science — Beginner to Advanced
From your first line of Python to shipping real analysis on messy, real-world data.
Lifetime access · Certificate included · US timezone sessions
The 30-minute demo is free, needs no card, and carries no obligation. If we aren't the right fit for your goals, we'll tell you.
- Tutorials finished, but nothing deployed you can show
- A resume that lists coursework instead of shipped work
- Freezing when an interviewer asks you to explain a decision
- Unsure which skills US employers actually screen for
- 4 deployed projects with real source code
- A US-format resume built around what you shipped
- Mock interviews with written feedback, before the real one
- A clear path toward Data Analyst
Who this course is for
This course was designed around specific situations. If one of these sounds like you, you're in the right place.
What you'll learn
By the end of this course you will be able to do each of the following in real projects, not just in exercises.
Write clean, idiomatic Python for data manipulation
Clean and reshape genuinely messy datasets with Pandas
Run exploratory analysis that surfaces real insight, not just charts
Apply statistical reasoning: distributions, hypothesis testing, correlation
Communicate findings in a way stakeholders act on
Your learning roadmap
The course runs in phases. Each one ends with a concrete milestone, so you always know whether you're on track.
- 1
Python Foundations
Weeks 1–3Focus: The language
What you'll do
- Master data types, control flow, functions and comprehensions
- Work with files, APIs and JSON
- Write reusable modules and handle errors properly
- Use virtual environments and Git from day one
MilestoneBuild a script that pulls data from a public API and produces a clean dataset.
- 2
NumPy & Pandas
Weeks 4–6Focus: Data manipulation
What you'll do
- Use vectorised NumPy operations instead of slow loops
- Reshape, merge, pivot and group data with Pandas
- Handle missing data, duplicates and inconsistent types
- Work with time series and date-based aggregation
MilestoneTransform a deliberately messy 100k-row dataset into analysis-ready form.
- 3
Visualisation & Statistics
Weeks 7–8Focus: Finding and showing insight
What you'll do
- Build clear, honest visualisations with Matplotlib and Seaborn
- Apply descriptive and inferential statistics
- Run hypothesis tests and interpret p-values correctly
- Identify correlation, causation traps and sampling bias
MilestoneProduce a full exploratory data analysis report with defensible conclusions.
- 4
End-to-End Projects
Weeks 9–10Focus: Portfolio
What you'll do
- Frame a business question and choose the right analysis
- Build a reproducible analysis pipeline
- Present results to a non-technical audience
- Publish your work in a way recruiters can evaluate
MilestoneA published portfolio analysis with narrative, code and visualisations.
Everything you'll cover
A map of the whole curriculum at a glance — every major area and the topics inside it.
- Data types
- Control flow
- Functions
- Comprehensions
- Modules
- Error handling
- Arrays
- Broadcasting
- Vectorisation
- Linear algebra
- Series & DataFrames
- Cleaning
- Merging
- GroupBy
- Time series
- Pivots
- Matplotlib
- Seaborn
- Chart selection
- Storytelling
- Distributions
- Hypothesis testing
- Correlation
- Sampling
- Confidence intervals
- Jupyter
- Git
- Virtual envs
- Reproducibility
Week-by-week syllabus
A structured breakdown of exactly what you'll cover, week by week.
Projects you'll build
You finish with a portfolio of deployed work — the thing hiring managers actually look at.
Public API Data Pipeline
Pull, clean and store data from a live public API on a schedule.
Messy Data Rescue
Take a deliberately broken dataset and produce a documented, analysis-ready version.
Exploratory Analysis Report
Full EDA with statistical testing and a written narrative of findings.
Capstone Analysis
A self-chosen question answered end to end and published for recruiters.
Where this course can take you
Typical roles this course prepares you for, with current US market compensation ranges.
| Role | US salary range | Demand |
|---|---|---|
| Data Analyst | $70,000 – $105,000 | Very high |
| Business Intelligence Analyst | $80,000 – $115,000 | High |
| Junior Data Scientist | $95,000 – $130,000 | High |
| Analytics Engineer | $100,000 – $145,000 | Growing fast |
Salary ranges are indicative market figures for reference, not guarantees of employment or compensation.
Prerequisites
- No programming experience required — Python is taught from zero
- High-school level mathematics
- Curiosity about drawing conclusions from data
Tools you'll use
Frequently asked questions
Can I learn data science with no coding background?
Yes. The first three weeks teach Python from absolute zero, assuming no prior programming. Students from finance, biology, marketing and operations complete this course regularly. Expect to spend a few extra hours in the early weeks if you have never coded.
Python or R for data science?
Python, for most people. R remains strong in academia and specialised statistics, but Python dominates US industry job postings because it extends naturally into machine learning, engineering and production systems. Learning Python keeps more doors open.
What is the difference between a data analyst and a data scientist?
Data analysts focus on describing what happened using SQL, visualisation and statistics. Data scientists additionally build predictive models and often work closer to engineering. This course covers the analyst skill set completely and is the prerequisite for our Machine Learning course.
Will I have projects I can show employers?
Yes — four portfolio-grade analyses, including one self-directed capstone with a written narrative. We specifically avoid the over-used tutorial datasets that recruiters have seen a thousand times.
Do I need to be good at maths?
High-school mathematics is enough to start. The statistics you need is taught within the course, focused on practical interpretation rather than proofs.