Launch your career as a data scientist
Quality learning comes at the intersection of diligence and curriculum. With that vision in mind, Flatiron School has brought together passionate, experienced instructors and ambitious students to achieve incredible outcomes since 2012. And now we’re bringing that vision to data science with our London Data Science bootcamp.
Our 15-week data science program provides the full scope of skills you need to graduate with a deep understanding of fundamental statistics, Natural Language Processing, Python, and a complete toolkit to prepare you for a data science career.
Studying in our WeWork campuses, you’ll learn to code while becoming a part of the dynamic WeWork community of start-ups, entrepreneurs, and students. Plus, enjoy WeWork’s amenities: high-speed WiFi, 24/7 building access, craft beer, and conference rooms — plus fruit water and micro-brewed coffee.
Our instructors have both industry and teaching experience, and a passion for student success, ensuring you get the best education possible throughout your course that sets you up for a successful job search.
Our grads launch new careers
For job-seeking London graduates included in the 2019 Jobs Report including full-time salaried roles, full-time contract, internship, apprenticeship, and freelance roles, and part-time roles during the reporting period (see full Jobs Report here).
For job-seeking London students who accepted full-time salaried jobs during the reporting period and disclosed their compensation. The average starting salary for students who took full-time contract, internship, apprenticeship, or freelance roles and disclosed compensation was £12/hr (see full Jobs Report report here).
Where our grads work
What you'll learn: data science & machine learning
From Python to Machine Learning, our 15-week data science training program gives you the breadth and depth needed to become a well-rounded data scientist. You’ll also leave with an understanding of how to discover new techniques as your career progresses.
Every 3 weeks you’ll be introduced to a new module that builds off the learnings of the previous section while allowing you enough time to dive into each area for a thorough understanding of the subject matter.
The Data Science program moves quickly and our passionate students embrace that challenge. While no experience is necessary to apply, we require you to demonstrate some data science knowledge prior to getting admitted, then complete a prework course before Day 1. To help you prepare for our bootcamp, we provide a free introductory course. This prework ensures you come in prepared and are able to keep pace with the class.
Our first module introduces the fundamentals of Python for data science. You’ll learn basic Python programming, how to use Jupyter Notebooks, and will be familiarised with popular Python libraries that are used in data science, such as Pandas and NumPy. Additionally, you’ll learn how to use Git and Github as a collaborative version control tool. To organise your data, you’ll learn about data structures, relational databases, ways to retrieve data, and the fundamentals of SQL for data querying for structured databases. Furthermore, you’ll learn how to access data from various sources using APls, as well as perform Web Scraping.
Finally, we’ll conclude with a heavy focus on visualisations as a way to go from data to insights.
At the end of this module, students will use their newly learned skills to collect, organise and visualise data, with the goal to provide actionable insights.
Variables, Booleans and Conditionals, Lists, Dictionaries, Looping, Functions, Data Structures, Data Cleaning, Pandas, NumPy, Matplotlib/Seaborn for Data Visualisation, Git/Github, SQL, Accessing Data Through APIs, Web Scraping
Having learned how to gather and explore data with Python and SQL you can now go deeper into analysing that information with statistics. In this module, you’ll learn about the fundamentals of probability theory, where you will learn about probability principles such as combinations and permutations. You will go on and learn about statistical distributions and how to create samples when distributions are known. By the end of this module, you will be able to apply this knowledge by running A/B tests. Additionally, you’ll learn how to build your first (and important) data science model: a linear regression model.
Combinatorics, Probability Theory, Statistical Distributions, Bayes Theorem, Sampling Methods, Hypothesis Testing, A/B Testing, Linear Regression, Model Evaluation
Module 3 is all about machine learning, with a heavy focus on supervised learning. To start, you will go a little deeper into regression analysis, learning about extensions to linear regression, and a new form of regression: logistic regression. In building regression models, students will learn about penalisation terms, preventing overfitting through regularisation and using cross validation to validate regression model.
Next, you’ll learn how to build and implement the most important machine learning techniques. You’ll learn about classification algorithms such as Support Vector Machines and Decision Trees. Additionally, you’ll learn how to build even more robust classifiers using ensemble methods such as Bagged and Boosted Trees, and Random Forests.
Linear Algebra, Logistic Regression, Maximum Likelihood Estimation, Optimisation Cost Function, Gradient Descent, K Nearest Neighbors, Decision Trees, Ensemble methods, Pipeline Building, Hyperparameter Tuning, Grid Search, Scikit-Learn
After a full module on supervised learning, this module focuses on a variety of advanced Data Science techniques. You will start with learning about unsupervised learning techniques such as clustering techniques and dimensionality reduction techniques. Next, you will be introduced to threading and multiprocessing to be able to work with big data. In doing so, you’ll learn about PySpark and AWS, and how to use those tools to build a recommendation system. Next, you will get an in-depth overview of deep learning techniques, learning about densely connected neural networks, enabling high-performing classification performance. Next, students will learn how to use regular expressions in Python, and how to manage string values, analyse text and perform sentiment analysis.
Dimensionality Reduction, Clustering, Time Series Analysis, Neural Networks, Big Data, Natural Language Processing, Text Vectorisation, Natural Language Toolkit, Regular Expressions, Word2Vec, Text Classification, Recommendation Systems
In our final project, you’ll work individually to create a large-scale data science and machine learning project. This final project provides an in-depth opportunity for you to demonstrate your learning accomplishments and get a feel for what working on a large-scale data science project is really like.
You and your fellow students will each pitch three different ideas and then decide on your final project with your instructors. Instructors advise on projects based on difficulty and feasibility given the course’s time constraints. At the end of the project, you’ll receive a grade based on various factors.
Upon project completion, you’ll know how to construct a project that gathers and builds statistical or machine learning models to deliver insights and communicate findings through data visualisation and storytelling techniques.
Meet your teachers
At Flatiron School, we believe that great teachers inspire us to understand topics on a profound level and inspire us to become lifelong learners. With experience both in the field and in the classroom, our data science instructors are dedicated and thorough. Simply put: you learn from the best.
Daniel has worked as a data scientist in multiple industries and is part of the Data Ethics committee in a well known organisation in London. He’s taught data science to students at all levels in corporate, third sector, and mentorship settings.
After earning a Masters in Statistics from New York University, Fangfang worked as a data scientist in the public policy and start-up sectors. However, her love of teaching led her to join Flatiron School as a lead instructor.
Sean Abu joined Flatiron School after working for IBM as a Data Scientist Consultant and as a high school economics teacher. As a lead instructor, he combines his past experiences to prepare students for their future.
Launch a career in tech with support from our Career Services
We wrote the book on how to get a job after a bootcamp. After more than five years working with passionate students and helping them land fulfilling careers in tech, we’ve got a keen understanding of what goes into getting that first job.
During your job search, you’ll meet weekly with your dedicated Career Coach. Coaches help with everything from C.V. review to interview prep, and help you tell your story to get the job you want.
We’ve built relationships with hiring managers at top companies, creating a robust employer pipeline for Flatiron School grads. Our Employer Partnerships team is constantly evangelising our grads and helping you get in the door.
Through 1-on-1 guidance from our Career Coaching team and our tried-and-true job-search framework, you’ll gain the skills and support you need to launch your career.
Making education accessible
Future Finance Student Loan
Apply for a loan between £2,000 and £40,000 to cover the remaining cost of your tuition, plus living costs
Low monthly payments whilst studying
Available to UK, EU and international students with a UK domiciled address
Visit the Future Finance website to get a personalised quote
Submit an application online to see if you are eligible
Join us on campus
|Cohort Start Date||Status|
|Feb 17, 2020 – May 29, 2020||Closing Soon –|
|Mar 30, 2020 – Jul 10, 2020||Open –|
|May 11, 2020 – Aug 21, 2020||Open –|
Take the leap and start your journey
Start your journey towards a career as a data scientist by joining our inclusive, dynamic student community at Moorgate. At Flatiron School, we admit students who bring creativity, ingenuity, and curiosity to the classroom.
Submit a written application to our Admissions team. Tell us about yourself and why you want to learn to become a data scientist.
Speak with an Admissions Advisor in a non-technical interview. This is an opportunity for us to get to know each other a little better. Nothing technical — just a friendly conversation.
After writing and submitting some code on Learn.co, you’ll attend a live interview session with an instructor to assess your understanding of the material.
Receive your acceptance decision from Admissions. This usually happens within 4 business days.
If accepted, you'll begin course pre-work to prepare for the first day of class.