Data Science with Python

COM6018

Jon Barker

About me

• Jon Barker (email address is j.p.barker@sheffield.ac.uk)
  • DO NOT USE jon.barker@sheffield.ac.uk (that is not me!)
• Professor in the Speech and Hearing Research Group
• Research interests
  • Speech technology: speech recognition in real-world environments
  • Hearing science: hearing aids processing, speech perception, speech enhancement and speech intelligibility prediction
  • Music processing for hearing impairment
• You can find out more here

Data Science with Python

• Why is this module called Data Science with Python
• Data Science is a broad subject encompassing areas including statistics, machine learning, data visualisation, data mining, and data engineering (i.e., your entire degree!)
• The module focuses on the with Python part of the title. We will be showing you how to use Python to do data science.

COM6018 Course aims

The module has the following aims:

• to provide an introduction to the Python programming language
• to introduce a range of data analysis techniques using the latest Python tools and an understanding of how to apply the appropriate techniques for data analysis
• to understand how to present data science clearly, rigorously and reproducibly

Note, the module focuses on practical data analysis and will be less theoretical than the 'Machine Learning and Adaptive Intelligence' module.

Schedule

• Lectures on Monday 13:00-14:50
  • Broad Lane Lecture Theatre 5
• Labs on Wednesday 9:00-10:50
  • Diamond Computer Room 3
  • Bring Your Own Device

Teaching Weeks 1-5 and 7-11; Weeks 6 and 12 are 'Readings Weeks'

Assessment

Two pieces of coursework:

• Assessment 1 (40%)

  • Similar in style to the labs
  • Released Week 5
  • Due end of Week 6

• Assessment 2 (60%)

  • A larger piece of work with a written report
  • Released Week 9
  • Due end of Week 12

Feedback

• Feedback during lab classes
• Solutions will be provided for all lab classes
• Feedback will be provided for both assignments

Resources

Course content will include

• slides (like these) in PDF format for each lecture,
• tutorials,
• lab classes,
• lab class solutions in jupyter notebook format.

Recommended reading

• Python Data Science Handbook, Jake VanderPlas, O'Reilly, 2016 (available in the library, electronic holdings)
• Practical data science for information professionals, David Stuart, Facet Publishing, 2020 (available in the library, electronic holdings)
• Python for Data Analysis, Wes McKinney, O'Reilly, 2017
• Practical data science with Python Nathan George, Packt Publishing, 2021

Content Delivery

• Blackboard site
  • Discussion boards, links to course content
  • Links to the reading list
• GitHub repository
  • https://github.com/UOS-COM-6018/COM6018
  • Allowing you to download the course content
  • Updated throughout the course
• COM6018 Jupyter book
  • https://uos-com-6018.github.io/COM6018/
  • Allows you to view the Jupyter notebook content online

Course structure

Part I

• Week 1: Getting Started with Python
• Week 2: Reading and Writing Data
• Week 3: Numerical Computing with NumPy
• Week 4: Processing Structured Data with Pandas
• Week 5: Exploratory Data Analysis and Visualization

READING WEEK - Assignment 1

Course structure (continued)

Part II

• Week 7: Statistical Hypothesis testing with SciPy
• Week 8: Introducing Scikit-Learn
• Week 9: Classification with Scikit-Learn
• Week 10: Curve Fitting with Scikit-Learn
• Week 11: Regression with Scikit-Learn

READING WEEK - Assignment 2