Prerequisites

MY472 teaches computational techniques for working with data. This will involve a fair amount of computational work, mostly using the Python programming language.

At a glance

To take MY472, you will need:

• your own suitable laptop, which you must bring to every seminar
• macOS, Windows or Linux, and at least 8 GB of RAM
• the required software, which you will install before your first seminar
• basic Python knowledge by the end of Week 2 of Autumn Term

You do not need to be an experienced Python programmer to join this course.

Computer requirements

Your laptop

Any recent laptop running macOS, Windows or Linux with at least 8 GB of RAM should work. You cannot use a tablet or iPad. Some very old laptops and low-specification Chromebooks will not run the required software.

If you are unsure whether your laptop is suitable, email the course convenor. You do not need to buy a new computer for this course, if you already have one meeting the specifications above. However, if you are buying a new computer anyway and would like advice for MY472 and similar LSE courses, you are welcome to contact the convenor for advice.

Operating-system expectations

You may use macOS, Windows or Linux. Nearly all of the work in this course happens inside Positron, writing Python code, and this is nearly identical across operating systems. You will write the same code, load the same libraries and see the same output.

The course materials are developed on macOS, and most lectures and seminars are taught on Apple laptops. Both exams also take place on School-issued Apple laptops, so every student sits the exams on macOS regardless of what they have used during term. See the Assessments page for more information about the exams.

Some installation steps, file paths and command-line instructions will look different on Windows or Linux. We will provide platform-specific guidance where practical, but we are not IT specialists and cannot devote much class time to technical differences between operating systems. If you use Windows or Linux, you should be prepared to (occasionally) work through some of those differences. Keep in mind that the ability to work across computing environments is an important data science skill!

Windows users may find it useful to use a Unix-like environment for this course (and other courses in data science). The Windows Subsystem for Linux (WSL) offers a way to work within a Linux environment while running Windows. This is optional, however, and we have not confirmed that WSL runs all of the required course software.

Python preparation

The course uses Python for all code, although we will sometimes provide R code as an optional learning resource. Students arrive with very different amounts of coding experience, and many pick up what they need as they go. What matters is your willingness to put in the effort to learn.

The course will not include a dedicated Python refresher in lecture or seminar. Unless you already have equivalent knowledge, you should complete the Dataquest modules below during Weeks 0–2 of Autumn Term (see below).

Prior Python experience is not required

Many students have succeeded in the course with no prior experience with coding. However, you should be prepared to work to learn what you need.

What you need to know

By the end of Week 2, you should be comfortable with:

• running a script or notebook cell, reading its output, and reading the error when it fails
• assigning variables, and telling numbers, strings, booleans and missing values apart
• using lists and dictionaries: indexing, adding to them, and looping over them
• calling functions and methods, and using what they return
• importing a library and calling its functions, for example import pandas as pd followed by pd.read_csv(...)
• writing an if/else statement and a for loop, and defining a function with def

You do not need to arrive knowing how to handle tabular data, write classes or run statistical models. If you can already do everything listed above, you do not need to complete the preparatory materials.

Dataquest modules

LSE provides free access to Dataquest, which has modules on Python fundamentals. First, request access through the Digital Skills Lab. Once your account is set up, complete these modules:

• Variables and data types
• Lists and for loops
• Conditional statements
• Dictionaries
• Functions

The module on Object-oriented Python is useful optional material, but we will not use these concepts regularly, if at all, in MY472.

Setup after enrolment

After enrolling, you will install Python, Quarto, Positron and Git, and create or prepare a GitHub account for the course. You do not need to do this before deciding to take MY472. We provide step-by-step instructions.

Enrolled? Start here

Once you have decided to take MY472, go to Getting started with MY472 in the course GitHub organisation.