Data Programming in Python

Course information

The course introduces learners to object-oriented programming, the programming language Python and its use for data programming and analytics.

Prerequisite Knowledge

Learners should have a basic understanding of matrix algebra and statistics. The course is suitable for learners with no prior experience in programming, however, the course advances at a brisk pace. Learners with no prior experience in programming should expect a larger time commitment in order to fully benefit from the course.

This course is typically taken in year 2 of the MSc in Data Analytics for Government programme and learners typically have the knowledge and skills covered in our year 1 course.

This course assumes that you have comparative knowledge and skills covered in the following courses, alternatively, you may wish to consider taking some of the courses listed before attempting this course.

Intended Learning Outcomes

By the end of this course learners will be able to:

  • design and implement functions and classes in Python;
  • make efficient use of the data structures built into Python, such as lists;
  • describe and exploit features of object-oriented design such as polymorphism and inheritance;
  • implement data management and visualisation tasks in Python;
  • implement data-analytic tasks in Python using external libraries such as scikit-learn, NumPy/SciPy and pandas.

Syllabus

Week 1

  • Installing Anaconda Python
  • Overview over front ends
  • Overview of distinctive features of Python
  • Data types in Python
  • Strings
  • Control structures: if, for and while

Week 2

  • Data frames
  • Transforming, subsetting and merging data frames
  • Reading and writing data from/to files

Week 3

  • List, tuples and sets
  • Dictionaries
  • Comprehensions

Week 4

  • Introduction to object-oriented programming
  • Creating classes

Week 5

  • Further object-oriented programming
  • Inheritance
  • Duck typing

Mid-term week break

Week 6

  • Working with vectors and matrices in NumPy
  • Linear algebra in NumPy and SciPy

Week 7

  • Pandas Series
  • Pandas DataFrames
  • Data manipulation in pandas

Week 8

  • Efficient methods for data management in pandas
  • Merging, grouping and summarising data in pandas

Week 9 (sample material)

  • Plotting using matplotlib
  • Data visualisation using seaborn and the plotting functions in pandas

Week 10

  • Simple statistical inference using SciPy
  • Fitting regression models using statsmodels

Week 11

  • Fitting machine learning models using scikit-learn
  • Pre-processing data for machine learning models
  • Creating pipelines

“Interesting tasks and the video solutions are great.”

Online Learning

  • Weekly live sessions with tutor(s)
  • Weekly learning material (reading material, videos, exercises with model answers)
  • Bookable one-to-one sessions with tutor(s)

Textbooks

M. Lutz. Learning Python. O'Reilly. A. B. Downey. Think Python. O'Reilly.

J. Vanderplas. Python Data Science Handbook. O'Reilly.

W. McKinney. Python for Data Analysis. O'Reilly.

Assessment (for credit only)

This will typically be made up of 4 pieces of assessment, including programming assignments and an individual project. Please note that the deadline for the larger project will be outside of the course weeks to allow flexible working.

Please note that the deadline for some assessments may fall outside the teaching weeks of the course.

Software

To take our courses please use an up-to-date version of a standard browser (such as Google Chrome, Firefox, Safari, Internet Explorer or Microsoft Edge) and a PDF reader (such as Acrobat Reader). Learning material will be distributed through Moodle. We encourage all learners to install Anaconda Python and provide detailled installation instructions, but learners can also use free cloud based services (Google Colab). Learners need to install Zoom for participating in video conferencing sessions. We recommend the use of a head set for video conferencing sessions.