Flexible learning
Online postgraduate courses
Python Programming: Best Practices for Data Science
Please note to take this course you must first have completed Advanced Machine Learning & Programming in Python
This course covers best practices in Python programming. It focuses on structuring code, commenting code, using versioning tools like Github, and setting up data science projects in Python which allow for effective collaboration and maintenance of code while working with other coders in large projects.
This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
- 10 Credits
- 100 hours of study
- 15 contact hours
- 85 hours for private study
Faculty
Structure
Structure
Python Programming is an optional 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.
The total number of contact hours is 15 hours. This leaves 85 hours for private study.
Lectures
This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day.
There will be optional clinics on the last day of the course.
The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact edu@timberlake.co.uk.
Module Programme
Course Content
- The basics of writing clean code in Python
- The Python style guide
- Naming variables, functions, classes, methods, and sequences
- Documenting the code
- Block comments, in-line comments, and docstrings
- How to write clean Python loops with enumerate, zip, break, and the else clause
- Best practices for indentation, line breaks, blank lines, and whitespaces in Python
- Best practices for object-oriented programming
- What is Github?
- Tracking code changes
- Collaborative coding
- Managing projects with Repositories
- Project cloning and working on local copies
- Staging and Committing
- Branching and Merging
- Pull the latest version of the project to a local copy
- Pushing local updates to the main project
- Files that are changed, added or deleted
- You select the modified files you want to Stage
- Seeing the full history of every commit
- Reverting back to a previous commit.
Prerequisites
English Language Requirements
Both Programmes are open to applicants anywhere in the world. We may ask applicants to provide a recognised English language qualification, dependent upon their nationality and where they have studied/worked previously.
The requirement is an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We will also consider other English language qualifications. If their score is below our requirements, they may be eligible for one of Lancaster University's pre-sessional English language programmes.
Academic Requirements
Applicants to the Postgraduate Certificate of Achievement, Postgraduate Certificate, Postgraduate Diploma or full MSc in either programme require either an upper second-class degree in economics, econometrics or related subjects.
Learning Outcomes
- Cloud Computing Proficiency: Ability to leverage cloud computing services for data scienceprojects
- Collaborative Coding with GitHub: Skills in using collaborative coding environments,particularly GitHub
- Machine Learning Deployment: Proficiency in deploying, testing, and maintaining machinelearning models
- Tools Utilisation: Effective use of tools such as GitHub for collaborative coding
- Practical Application: Application of cloud computing and collaborative coding skills toreal-world projects
- Testing and Maintenance of Models: Rigorous testing and ongoing maintenance of machinelearning models
- Cloud Service Integration: Integrating cloud computing services into data science workflows
- Data Science Project Implementation: Applying skills to deploy and maintain machinelearning models in data science projects.
- Write high quality and easy-to-maintain Python scripts
- Adhere to best coding practices
- Be confident in sharing code with others
- Be familiar with using versioning systems such as Github
- Be confident in using Python in data science projects
- Understand how to best structure code for clarity, readability, and easy maintenance
- Know how to use Github for collaborative coding projects
- Know how to make use of the Python programming language in the best possible way fordata science projects.
Frequently Asked Questions
All sessions are conducted live and online at a scheduled time, but are also recorded. Students may attend live and watch the recordings back to recap the material or watch the recordings only if unable to attend live. We always advise students to attend live where possible as this will allow them the best opportunity to engage with the content and ask the lecturer's questions.
All modules are examined through online coursework submissions, you will have the support of your module lecturer/tutor in this poccess.
No, all necessary software is provided to students.
All courses are recorded and available on the LUMS internet platform throughout the current academic year. They can therefore be viewed 24 hours a day.
Timberlake Consultants and Lancaster University Management School (LUMS) Economics department have a longstanding partnership; combining 40+ years of industry expertise with over 50 years of academic excellence. We are delighted to build on this with our micro-credential postgraduate courses.
Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education
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Flexible learning
Online postgraduate courses
Specialist courses
Best-in-field experts
Tailored learning
Career-oriented education