Python for Data Science

Course overview

Have you done a data science or data analytics course already? You know Power BI and Excel but want to add Python to your toolset.

This course will give you the foundations in Python and prepare you to study NPA Data Science with Python and R or PDA Data Science where you need Python skills. It's a transition course for those with little or basic programming skills, to equip you to take on Data science courses built around Python or R.

The course is 14 weeks with workshops delivered online through MS teams, weekly, one evening per week. You are required to study in your own time to complete the coursework.

This is a non-accredited college-certificated course which builds skills for future study or career upskilling for your current role.

What you will learn

Modules and Content:

1. Course Overview and Introduction

• This week, we look at the course's goals, expected results, and its function as a foundational step for the NPA or PDA program.

2. Integrated Development Environments (IDEs)

• Exploration of IDEs with a focus on the Thonny IDE.
• Learn about system paths and how to configure them. In the drop-in class, follow a walkthrough demonstrating the installation and configuration of Thonny.

3. Python Fundamentals

• An introduction to syntax, comments, and variables in Python.
• Learn the importance of whitespace.
• In the drop-in class, follow a walkthrough demonstrating the installation and configuration of Python.

4. Data Types, Operators, and Input Handling

• A study of Python's data types, conditionals, and string operations.
• Learn about operators, escape sequences, and receiving user input.

5. Practical Application: Test Your Knowledge

• Exercises to reinforce concepts from the initial modules.

6. Control Structures and Sequences

• Mastery of for and while loops, indexes, and ranges for data control.

7. Building Blocks: Functions and Lists

• Understand how to create functions and manage data with lists.
• Learn the concept of importing modules.

8. Interactive Learning: Test Your Knowledge

• Complete practical coding assignments focusing on the recent modules.

9. Data Structuring: Dictionaries and File Operations

• Advanced techniques for data storage and manipulation using dictionaries.
• Learn file handling for reading and writing data.

10. Engaged Learning: Test Your Knowledge

• Students work on assignments to validate their understanding of data structuring and file operations.

11. Developing Menu Systems and Planning Projects

• Crafting user navigation and planning for larger coding projects.

12. Introduction to Jupyter Notebooks and Pandas

• Overview of Jupyter Notebooks for Python scripting.
• An introduction to the Pandas Library and discovering its significance in data science.

13. Introduction to Data Visualization with Matplotlib

• An overview of the Matplotlib library and its role in data visualisation.

14. Final Review: Test Your Knowledge

• Integrative exercises to consolidate all course content.

15. Finishing touches

• The last two weeks allow time to complete any outstanding work and for your tutor to assess your submissions.

Learning Outcomes

By the end of this course, you will possess a robust set of Python programming skills and an understanding of the tools and libraries crucial for data science.

• Regular 'Test Your Knowledge' sessions provide a structured opportunity to apply what you have learned, ensuring preparedness for each subsequent module and the PDA course. 

Number of days per week

• Supported by weekly optional MS Teams sessions for live interaction and additional support.
• Mostly self-study about 8 hours per week

Entry requirements

This course equates to SCQF level 8

• You need to have qualifications or skills that equate to National 5 maths/arithmetic and National 5 computing ( if you have completed Data Science Level 7 with Power Bi this is sufficient computing skill)
• If you don't have formal qualifications then have a chat with us and we can assess your skill level in computing and maths.
• Due to the context of the delivery as Data Science, you should have a background, practical experience, certificate, badges or qualifications in data science to join this course.

English Proficiency Requirements

Progression and Articulation Routes

• NPA Data Science with Python and R 
• PDA Data Science level 7
• PDA Data Science level 8