Data Analytics & AI Bootcamp

In phase 2 of your data analyst training, you will learn what "descriptive statistics" are all about and why they are so important for data analysts: They are especially useful when dealing with extensive data material in order not to lose the overview (and your nerves).
In addition, we will teach you how to use Google Sheets, show you some tricks to create good presentations and you will learn some helpful problem-solving methods from our coaches.

Time to set up your setup: Together we will prepare your computer to work as a hacker and finally get familiar with the command line. We will install Python in a conda environment. Now you understand the concept of "virtual environments", can create them yourself, and use package and library management tools.

You will become familiar with coding in an IDE (Integrated Development Environment), and understand Python syntax, different data types and structures, variables, loops, conditional statements and functions. And because you are now able to use and write pseudocode, you will also write your first simple program.

Now that you're deep into the topic, you'll learn about the concept of an EDA - exploratory data analysis, which examines data with little knowledge of its relationships. In the process, you'll learn how to collect, expand, and cleanse data using "pandas data frames." While performing such an EDA you already combine several data sets and deal with missing values.

Using your skills in descriptive statistics (phase 2), you extract meaning from data and learn not only what KPIs (Key Performance Indicators) are, but also create them. In addition, our coaches will teach you what makes so-called "plots" useful and how to apply them. You will use Matplotlib and Seaborn and learn how to create diagrams.

In this phase, you’ll level up from describing data to drawing solid, data-driven conclusions. You’ll start by exploring probability distributions and understanding how data behaves in real-world contexts. Then, we’ll dive into confidence intervals and how they help estimate population parameters.Next, we’ll guide you through the logic and application of hypothesis testing and how A/B tests are used in product and marketing decisions – an essential skill for any data analyst working in real-world environments.But we won’t stop there. You’ll get hands-on with clustering techniques to group and segment data – ideal for finding hidden patterns. And finally, you'll be introduced to linear regression, a foundational modeling technique used to predict and explain relationships between variables.Through coach-led sessions, hands-on exercises, and real examples, you’ll learn how to go from observing patterns to making confident decisions and predictions with data.By the end of this phase, you’ll not only understand the "what" and "why" – but also the "how" of applying statistical thinking to real business problems.

Just before the final spurt, you will learn the basics about databases and their structures with the other participants. You will learn how to use DBeaver to connect to the provided PostgreSQL database, how SQL syntax is defined and how to build an SQL query.

With the help of code-along script files and other tasks provided by the coaches, you will learn SQL execution in a practical and effective way. On top, you'll learn about the elements of data infrastructures and how to design a star schema. Sounds complicated to you? Don't worry, we'll take you through it bit by bit!

In Phase 7, you'll work with API keys to connect to APIs. We'll teach you how to automate multiple calls with different parameters. In addition, you'll learn to execute SQL queries from Python and practice reading the documentation to understand limits, request and response formats.

Heard about JSON files? We'll show you how to access them and extract data from them. With all the skills you've learned so far, you'll then run a project to solidify your knowledge. You'll do this with agile tools to organize the project as perfectly as you will later on in your job!

Learn how to master Tableau for data analysis and build in-demand skills in one of the world’s leading business intelligence tools. In this final phase of the bootcamp, you’ll gain all the knowledge and hands-on experience needed to pass both the Tableau Desktop Specialist and Tableau Certified Data Analyst exams—no prior experience required.

You’ll start with the basics and progress through key Tableau features: connecting and preparing data, building calculated fields, applying filters, groups, and sets, and designing impactful, presentation-ready visualizations and dashboards. As your skills grow, you’ll explore advanced concepts like parameters, table calculations, Level of Detail (LOD) expressions, and statistical techniques.

Through hands-on exercises you’ll not only prepare for certification but also gain the confidence to use Tableau independently in data-driven roles. By the end of this phase, you’ll be equipped with a certification-ready portfolio and practical skills that set you apart to employers.

Because we are 100% convinced that the best way to remember what you have learned is through practical application, you will spend the last four weeks of your Data Analytics Bootcamp incorporating all of your knowledge into your digital capstone project.
Once you've made it this far, you can officially call yourself a Data Analyst. Congratulations! 🙌🏼