Data Science

Phase 1 Coding basic knowledge

Learn the basics of coding

Welcome to our Data Science & AI bootcamp! After a short introduction phase in which you get to know the coaches and other course participants, we start directly with learning the basic skills required for working in a Unix shell. In addition, you will quickly learn the general basics of the Python programming language, in which you will write and execute your first small programs. 

By using Git and Github, we will introduce you to modern and collaborative ways of working in the industry. This includes versioning code, as well as working together on the codebase, where we apply concepts like "branching" and "pull requests".

You will be acquainted with the historical development of AI and  what the relationship between Machine Learning, Deep Learning and Neural Networks is. Finally a overview of Data professions will be presented including the differences between data scientist and data analyst and the role of a data engineer. 

Phase 2 Exploratory data analysis

Advanced course 'exploratory data analysis'

Building on the first phase of your Data Science training, you will now become familiar with tools such as SQL and Pandas for extracting and manipulating data from various sources - these can be individual files, but also databases. After data manipulation, you will move on to Exploratory Data Analysis (EDA), for which you will learn about different visualization libraries. 

This phase will conclude with a two-day project focused on EDA - based on real data sets, of course. With a focus on the business case, you will deliver tailored recommendations and meaningful visualizations to fictional stakeholders.

Phase 3 Machine learning basics

First things first: the machine learning basics

Now, basic concepts of supervised learning, such as regression and classification, are covered. You will become familiar with how different models work, including linear and logistic regression, and learn about decision trees and support vector machines. You will also learn when which models are good to use and on which assumptions or simplifications the algorithms are based. 

Afterwards, we will teach you about model evaluation and which trade-offs you have to consider when building predictive models. You'll solidify everything you've learned in a 4-day machine learning group project that covers the entire data science lifecycle. Together, you'll set milestones and the goals of your data product while working collaboratively with Git and Github - then present to stakeholders*

Phase 4 Advanced machine learning

We dive deeper: advanced machine learning

Phase 4 of your Data Scientist training: With a basic understanding of statistical learning techniques and the underlying software implementation, you are now diving into more specialized methods, such as time series prediction.

In a world where much of the data generated is "unlabeled," it's important to also address the challenges of unsupervised learning. Therefore, in the Data Science course you will also get an introduction to the techniques used for this purpose, such as dimension reduction and clustering.

Phase 5 Introduction to Neural Networks

Neural networks and their applications

In order to also gain a good understanding of the concepts that are often referred to as "AI" today, you will receive an introduction to artificial neural networks in Phase 5 of the Data Science training. Building your own deep neural network will improve your understanding of the underlying processes and concepts. 

At this stage you will also get introduced to the concept of transfer learning and get acquainted with pre-trained model repositories such as ‘Hugging Face’.

In the course of this, typical application areas, such as natural language processing and related techniques, will also be covered. Then it's time for the final phase of your training to become a Data Scientist.

Phase 6 Final project & presentation

Your finale at the graduation event

In order to apply what you've learned in practice and to deepen group work and collaboration even more, the final project provides context and time. Here you will work in small groups on a larger data science problem - from start to finish. Here, each team can choose to find their own problem, use publicly available datasets, or work with data from our corporate partners to solve a real-world business problem.

As a team, you'll develop a solution and regularly present interim results. In the process, you'll apply all your technical and agile skills and learn additional techniques tailored to the project. You made it! Congrats!