> For the complete documentation index, see [llms.txt](https://redi-school-1.gitbook.io/applicant-hub/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://redi-school-1.gitbook.io/applicant-hub/data-ai-track/data-analytics.md).

# Data Analytics

## What is the course about?

Do you want to understand and work in Data Analytics? Then this course is for you! Join us to learn how to analyze, interpret, and present data using powerful tools like Python and SQL. You deepen your Python knowledge and apply data analysis techniques.

Through hands-on projects and real-world examples, you’ll gain practical skills to work with datasets, uncover insights, and tell compelling stories with data. Join us and take your first step toward a career in the growing field of data analytics!

{% hint style="info" %}

### Course Details <a href="#how-do-i-participate-in-the-courses-self-paced-mode-vs-cohort-mode" id="how-do-i-participate-in-the-courses-self-paced-mode-vs-cohort-mode"></a>

* Classes: Monday and Wednesday, 19:00 - 21:00
* Time Invest: 15 hours per week
* Timeline: Start Date is 14th of September 2026, End Date is 8th of December 2026 (14-weeks)
* Hybrid: Certain events take place in person in the following locations: NRW, Berlin, Metropolitan Region of Hamburg. [More information](#onsite-activities)
  {% endhint %}

{% embed url="<https://www.loom.com/share/3bbdc62ed40f488a8f542d155efd0baf?sid=64f7b8ac-7c82-4dcf-b5f9-23822bc06815>" %}

## Why should you take this course?

* You learn:
  * Python for Data Analysis: Use Python to clean, analyze, and visualize data.
  * Data Tools: Work with Pandas, NumPy, and Matplotlib for efficient data manipulation and visualization.
  * SQL Basics: Query databases and extract insights.
  * Statistics Fundamentals: Learn descriptive and inferential statistics for data interpretation.
  * Visualization Techniques: Create clear charts and graphs to present data.
* **Your Start -** This course is the perfect starting point for your journey toward becoming Data Analyst or Data Scientist. By the end of the course, you will have a solid foundation in Python, a GitHub portfolio showcasing your projects, and a ReDI Certificate. Afterward, you can advance your skills by enrolling in the Machine Learning & AI course.
* **Final Project**: Apply skills to real-world datasets and complete data analysis projects. You have the chance to present your project to your colleagues in the course.
* **Industry Experts -** The teachers are volunteers from the industry. They are experts in web development and will help you start your journey toward a tech career!&#x20;

## Learning Format

In the two weekly sessions, teachers introduce key concepts to the students and practice them with small exercises and live coding. Next to the two sessions, students are asked to apply the newly learned concepts in weekly homework and a final project.&#x20;

## Weekly Homework

Every Wednesday, students will receive homework to be submitted by Sunday evening. Homework review is part of the Monday session. There is a requirement for students to complete 80% of the homework throughout the course in order to graduate. Homework is not graded.

{% hint style="warning" %}

## **ReDI Style**

This course is about active participation. You will be asked to work independently on weekly homework to apply your newly learned skills. You are in charge of your learning journey. Are you ready to work hands-on and participate actively in the sessions? Then join us!&#x20;
{% endhint %}

## Course Outline

*The Course Outline may change before the start.*

<table><thead><tr><th width="165">Week</th><th width="273">Topic</th><th>Content</th></tr></thead><tbody><tr><td>0</td><td>Onboarding</td><td>Get to know ReDI</td></tr><tr><td>1</td><td>Kick-Off</td><td>Teachers &#x26; Students get to know each other</td></tr><tr><td>2</td><td>Preparation</td><td>Students learn about the tech setup</td></tr><tr><td>3</td><td>SQL</td><td>Introduction to SQL and Bigquery SQL in BigQuery - Basic functions and Joins.<br>Introducing Looker</td></tr><tr><td>4</td><td>SQL</td><td>Advanced (Date, Aggregate and Window Functions)<br>Visualization theory and practice.</td></tr><tr><td>5</td><td>Pandas</td><td>Intro to Pandas</td></tr><tr><td>6</td><td>Pandas</td><td>Transformation</td></tr><tr><td>7</td><td>Pandas &#x26; Mini Project</td><td>Students work on a small project (Mini Project) <br>Pandas data cleaning &#x26; missing values </td></tr><tr><td>8</td><td>Statistics</td><td>Data Representation &#x26; Tools<br>Statistics</td></tr><tr><td>9</td><td>Statistics</td><td>Bayesian Statistics<br>A/B Testing</td></tr><tr><td>10</td><td>Career Week</td><td>Students can participate in a variety of career workshops.</td></tr><tr><td>11</td><td>Recap</td><td>Key concepts are reviewed</td></tr><tr><td>12</td><td>Final Project Work</td><td>Students implement what they have learned in a final project.</td></tr><tr><td>13</td><td>Final Project Work</td><td>Students implement what they have learned in a final project.</td></tr><tr><td>14</td><td>Demo Day</td><td>Students present their final project.</td></tr></tbody></table>

## A typical week

<details>

<summary>Monday 19:00 - 21:00</summary>

Every Monday from 19:00 to 21:00, you have an online session in which you discuss your homework and where you will be introduced and practice new concepts.

</details>

<details>

<summary>Wednesday 19:00 - 21:00</summary>

Every Wednesday from 19:00 to 21:00, you have an online session where the volunteer teachers introduce you to new concepts. They will share the weekly homework with you in this session.

</details>

<details>

<summary>Thursday - Monday</summary>

You work on your weekly homework. That means you will be coding hands-on by yourself! If you run into problems, you can contact your class on Slack. You upload your homework before the Monday session.

</details>

## Onsite Activities

Based on your location there are different on-site activities. Find out more below.

{% tabs %}
{% tab title="Berlin" %}
If you are located in Berlin and surrounding, we invite you to some online and onsite community events throughout the semester.
{% endtab %}

{% tab title="NRW" %}
If you are located in NRW, we invite you to some online and onsite community events throughout the semester.
{% endtab %}

{% tab title="Hamburg" %}
If you are based in the Hamburg metropolitan region, you’ll attend some on-site career events and our Demo Day Celebration in December.
{% endtab %}
{% endtabs %}

## Timeline

<table><thead><tr><th width="177">Month</th><th width="230">Topics</th><th>Description</th></tr></thead><tbody><tr><td>June</td><td>Open Days</td><td>Join Info Sessions to get to know ReDI School</td></tr><tr><td>July &#x26; August</td><td>Open Days<br>Application Open<br>Student Interviews</td><td>Join Info Sessions to get to know ReDI School<br>Complete the application form and finish your prework.<br>Learners are interviewed for the course.</td></tr><tr><td>September</td><td>Kick-Off<br>Course runs</td><td>We kick-off the semester.</td></tr><tr><td>October &#x26; November</td><td>Course runs</td><td>You'll join the sessions and complete project work.</td></tr><tr><td>December</td><td>Demo Day</td><td>You'll present your final project.</td></tr></tbody></table>

## After the course

* You have built your own Data Analytics project (ML or data analysis)
* You know how to start analyzing a dataset, and about Data Science Tools (pandas, sklearn, ...)
* You can continue learning with our Data Analytics alumni group
* You can start looking for internships or continue learning in the Machine Learning & AI course or in the Data Circle.

## How to Graduate from the Course?

To graduate and receive the ReDI Certificate, we ask you to:

* Attended 80% of the sessions (We have a [camera on policy](/applicant-hub/resources/camera-on-policy.md))
* Join 2 Online or Onsite Career Events
* Complete 1 IBM SkillsBuild Course at ReDI
* Deliver and present 1 Final Project

{% hint style="success" %}

## Is this course for me?

* [x] you are interested in writing code and analyzing data
* [x] you have a solid understanding of Python&#x20;
* [x] you have an idea what data analytics is about
* [x] you can understand and speak English
* [x] you can commit at least 15 hours a week
* [x] you are eager to work on projects
* [x] you are committed to working in the [ReDI style](/applicant-hub/resources/redi-style.md)
  {% endhint %}

***

## FAQ

<details>

<summary>Not sure which track you are interested in?</summary>

If you don't have any experience with tech, apply to our introduction course: HTML & CSS, Infrastructure Basics, Python Foundations or UX/UI Design Bootcamp. To understand which tech career interests you, check out the following link:&#x20;

* [How to choose a tech career?](https://www.freecodecamp.org/news/how-to-choose-a-tech-career/)
* [Career Changer Playbook](https://ga-core.s3.amazonaws.com/cms/files/files/000/003/816/original/Career-Changers-Playbook.pdf)
* [Career Tech Guide](broken://spaces/Oa1pNW9YA7CW5ZRLDgwP/pages/5UeYVIpDeUCdYECaVbyh)

</details>

<details>

<summary>Not sure which course level to apply for? </summary>

Check out the [Prework](/applicant-hub/resources/prework.md) of the different levels. If you are a little bit challenged but able to complete a Prework, then the level is right for you.

</details>

{% hint style="info" %}

## 🤖 Unsure about which course to choose or have a question?

Try out our [AI Chatbot on Open AI](https://chatgpt.com/g/g-682b35175a5881919fac8d808d8a81ef-redi-school-dcp-course-applicants-advisor) (you need a ChatGPT account to access it). Please keep in mind that the Chatbot might make mistakes. You can find all the correct information on the Applicant Hub.&#x20;
{% endhint %}

***

### [💬](https://emojipedia.org/speech-balloon) Still unsure what to do..?&#x20;

You tried our [AI Bot](https://chatgpt.com/g/g-682b35175a5881919fac8d808d8a81ef-redi-school-dcp-course-applicants-advisor) - and didn’t find the answer you needed? Please make sure to review the **Applicant Hub** carefully, your answer is likely there. Still stuck? Check our [FAQ Page](https://redi-school-1.gitbook.io/applicant-hub/frequently-asked-questions-faq). Alternatively, you can reach out to us via email: <dcp@redi-school.org>.&#x20;


---

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