Data Analytics
Thinking about kickstarting a career in data science?
In-Person & Online
Data analysis is the process of inspecting, cleansing, transforming and modelling data, using methods from statistics and database systems to discover useful information, validate conclusions, and support decision-making across domains.
Course overview
Level-up with an industry-ready toolbox to support the common problems analytic teams face in business while building multiple portfolio pieces to showcase your skill set.
Download our Course Guide
We've put together a guide that delves into our pedagogy, course logic and structure. It's a good place to start if you'd like to learn more about us.
What you'll learn
Be prepared for 550 hours of lectures and activities in a part-time evening or full-time format Monday to Friday
Introduction to Programming
Objective: Learn the foundations of programming and how to code in Python
Project: Write a simple application in Python
Programming for Data Analytics
Objective: Understand the data analysis pipeline, review the data science foundations using probability, statistics & basic data analysis, and learn classic data analysis methods such as regression and classification
Project: Create a predictive model with a given dataset
Infrastructure and SQL
Objective: Learn how to use Linux/Bash and docker, review SQL and relational databases
Project: Create and query a relational database on Linux using Docker
Statistics
Objective: Basic statistics lays the foundation for ML and Advanced Data Analysis. In this module we review basic statistics concepts, such as probabilities, central tendency measures and charts and graphs.
Project: Use statistical methods to analyse datasets using Jupyter notebooks.
Machine Learning
Objective: Learn the differences between supervised and unsupervised machine learning methods, and the different families of algorithms within each group (e.g.: regression, classification, clustering).
Project: Create a predictive model for a given labelled dataset.
Advanced Data Analytics
Objective: Learn more advanced methods which deal with data types that are more complex than tables of numbers (e.g.: text, geospatial data, time-series, AB testing). In this module, we will cover specific methods that apply to these types of data, as well as how to pre-process them and visualize them.
Project: Use ADA methods to analyse complex datasets.
Project Phase
Objective: Apply the knowledge gathered in the previous modules to a real use case, implementing a DA project, end-to-end. Learn how to work collaboratively and build a portfolio.
Project: Propose a problem and solve it using an ADA method. Cover all stages of the DA lifecycle in both an individual and a collaborative project.
Career Prep
Objective: Prepare students for job interviews through logical puzzles and data challenges. Career coaching.
Next course start dates
Courses are taught in Central European time
Tuition and deferred payment options
Simple, flexible, and predictable pricing. Choose which deferred payment plan is best suited for you
Apply for a scholarship
Meant to support those who have experienced economic hardships and who have demonstrated a strong desire to develop their technical skills
The technologies & libraries you'll use
Project-based work with technologies that back the industry
Meet our analytics lead & CTO
If we had to put a weight on the quality of your educational experience, we'd put the majority on our educational team
Originally from Lisbon, Joana is a data scientist and software architect, with an extensive background in geospatial technologies. She has served as an independent expert at the EU Commision and FAO, and has also taught advanced analytics and GIS to both university and bootcamp students. She holds a PhD in spatial data mining and wears a super cool pacman necklace from time to time.
Data Scientist | Software Architect | Lecturer