Python for Data Analysis in Industry: A Crash Course

Learn to use Python for Data Exploration, Visualisation, Statistical Analysis, and Machine Learning

What you’ll learn
✓ Learn the basics of Python, the most common programming language employers expect you to know
✓ Learn how to conduct data manipulation, data visualisation, and data analysis in Python
✓ Learn the famous Python Libraries used for Data Analysis (pandas, matplotlib, scipy, sckit learn)
✓ Be confident in explaining basic use cases of statistical methods and machine learning techniques

Requirements
● Basic knowledge of Data Analysis

Description
For many people trying to pivot into a Data role, Python may not be your first programming language. You might be more familiar with R, or have little programming experience at all, having worked primarily with tools like SPSS or Jamovi. However, Python is the most commonly expected language in industry, and is a core skill employers look for when hiring for Data roles.

This course is designed to bridge that gap. It teaches you how to perform the data manipulation and analysis you may already be familiar with—just now, in Python. Beyond that, it introduces a wide range of data analysis tools and concepts that employers expect candidates to understand when applying for Data positions.

You don’t need to be an expert (yet). But you do need to be able to speak confidently about fundamental visualisation techniques, statistical methods, and basic Machine Learning algorithms—and understand how and when they are used in practice.

As an industry-focused crash course, this programme equips you with essential Python-based data skills that employers expect to see in candidates. By the end of the course, you will be able to confidently discuss your analytical approach, demonstrate practical Python workflows, and show that you are ready to contribute effectively in a Data role.

Course Image by Jan from Pixabay – many thanks to you!

Who this course is for
■ Aspiring Data Professionals who are unfamiliar with Python

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