Data Analysis Fundamentals

Are you the "Excel expert" in your office? A "freestyle" data analyst? Do you enjoy using data to make decisions or creating visualizations such as line plots, bar charts, box plots, or swarm plots? If so, this course is designed for you!

Before any meaningful analysis can take place, data professionals must first acquire, explore, and assess the quality of their data. These initial steps are essential for understanding the structure, patterns, and potential issues within a dataset. This course focuses on building foundational skills for working with data effectively, including data acquisition, cleaning, transformation, and visualization. By mastering these techniques, participants will gain the confidence and expertise needed to conduct insightful analyses and make data-driven decisions-key skills for advancing in the field of data science.

Designed for professionals seeking to transition into or grow their careers in data science, this course emphasizes a hands-on, practical approach. Participants will engage in guided data analysis sessions, complete homework assignments, and work on a personal project, where they are encouraged to apply their learning to their own datasets.

The course is taught in English, and participants are expected to have a basic understanding of programming concepts, such as variables, data types, and control flows. Those without prior experience are encouraged to complete the "Introduction to Programming in Python" course before enrolling.

By the end of this course, participants will be able to:

• Understand and apply key Python libraries for data analysis, including Pandas, NumPy, Matplotlib, Seaborn, OpenPyxl, and more.
• Demonstrate advanced knowledge of data acquisition techniques.
• Explore datasets and apply data quality assessment techniques.
• Perform data transformation to prepare datasets for analysis.
• Conduct statistical data analysis.
• Create insightful data visualizations.
• Evaluate the characteristics and suitability of datasets for specific analytical tasks.

This course covers the following topics:

• Getting started with Python and Jupyter Notebook
• Introduction to NumPy and Pandas
• Exploratory Data Analysis (EDA)
• Data Cleansing
• Data Transformation
• Data Visualization with Matplotlib and Seaborn
• Building Applications with Streamlit

There are 5 lessons organized once a week. After 4 lessons the students work on their final project. The final project presentation will be held during the last lesson.

This module is part of the CAS in Digital Life Sciences continuing education programme, but can also be attended independently of the CAS.

More information here: CAS in Digital Life Sciences

You can find an overview of our continuing education programmes in the field of computational science and artificial intelligence here.

The module will consist of lectures and practical exercises. In addition to lectures, students will be required to self-study selected topics. Students will work in groups on a project and present their results at the end of the course.  

• Exercises during the course: 50%
• Project: 50%

Successful completion of the course and the assessment will result in a ZHAW microcredential certificate being issued. This digital, verifiable form of certification makes your acquired competencies visible.

Participants who attend the course but do not complete the assessment will receive a course certificate without ECTS credits.