Your Course

The Data Analytics course is designed to transform your Python programming knowledge into practical data analysis skills. Over 14 weeks, you'll learn how to clean, analyze, visualize, and derive insights from diverse datasets using powerful tools like Python, SQL, and statistical methods. This intermediate-level course builds on your Python foundations and prepares you for roles in the growing field of data analytics or for more advanced courses in machine learning and AI.

Key Course Information

• Duration: 14 weeks (March 10 - June 19, 2025)

• Schedule: Twice a week (Mondays and Wednesdays, 19:00-21:00)

• Format: Hybrid learning (online sessions with optional on-site events)

• Time commitment: Approximately 15 hours per week (including sessions and independent work)

• Learning approach: Combination of taught concepts and weekly homework assignments

Course Structure

This course follows a structured learning approach where you'll develop comprehensive data analysis skills:

1. SQL Fundamentals - Learn database querying, joining tables, and visualization with SQL

2. Advanced Pandas - Master data cleaning, transformation, and analysis techniques

3. Statistical Analysis - Understand descriptive statistics, probability, and A/B testing methods

4. Final Project - Apply your data analysis skills to a real-world dataset and present your findings

The curriculum with weekly breakdowns and exercises will be shared with you each week via Google Classroom.

Weekly Structure

Each week consists of two key session types:

Monday - Session (19:00-21:00)

• Review of homework from the previous week

• Discussion of challenges and analysis approaches

• Introduction to new concepts and techniques through examples

Wednesday - Session (19:00-21:00)

• Explore new data analysis methods through theory and practice

• Participate in guided exercises with sample datasets

• Receive homework assignments to reinforce your learning

Between sessions: You'll work independently on weekly homework, spending approximately 10-12 hours per week on data analysis tasks and self-study.

On-site Activities

Depending on your location, you'll have different opportunities to participate in on-site activities:

Berlin students

We offer four on-site community events for the Berlin students throughout the semester. You can find more information in Slack.

NRW students

For students in NRW, we offer four on-site community events throughout the semester. The Demo Day will be on-site in person in Düsseldorf. You can find more information in Slack.

Hamburg Students

For the Hamburg students, we offer several on-site events. You can find more information in Slack.

What You'll Achieve

By the end of this course, you will:

• Clean and preprocess data using advanced Pandas techniques

• Query databases effectively using SQL

• Apply statistical methods to analyze and interpret data

• Create compelling data visualizations that communicate insights

• Conduct exploratory data analysis on complex datasets

• Complete a comprehensive data analysis project for your portfolio

• Be prepared to continue to the Machine Learning and AI course or the Data Circle

Graduation Requirements

To successfully graduate from the course, you'll need to:

• Attend at least 80% of the sessions (we have a camera-on policy)

• Complete and submit at least 80% of weekly homework assignments

• Submit a final project demonstrating your data analysis skills

• Attend 2 Career Events, complete 1 eLearning career course

Are you ready to dive into the world of data analytics and unlock insights from complex datasets? Let's begin this exciting journey together!