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Welcome!

Course format and scheduling

Lectures: There is a two-hour lecture each week during the term on Wednesdays from 13:00 to 15:00 in CLM.2.02.

Seminars: There is a one-hour “lab-style” seminar each week during the term. See the LSE Timetable for the schedule and locations for the seminars.

There are no lectures or seminars during week 6, which is LSE’s reading week.

Instructors

Office hour slots with all instructors should be booked via LSE’s StudentHub.

Assessments

  Type Due date
1 Formative in-class exercises during seminars
2 Formative practice problem set Friday, 1 November 2024, 5pm
3 Summative mid-term problem set Friday, 22 November 2024, 5pm
4 Summative final take-home assessment Wednesday, 15 January 2025, 5pm

Outline of topics

Important note: There may be some small changes to and/or reorganisation of the course topics during the first weeks of the course.

Week Topic Lecturer
1 Introduction Ryan Hübert
2 Tabular data Ryan Hübert
3 Data visualisation Ryan Hübert
4 Textual data Ryan Hübert
5 HTML, CSS, and scraping static pages Ryan Hübert
6 Reading week  
7 XML, RSS, and scraping non-static pages Ryan Hübert
8 APIs Ryan Hübert
9 Other data types Ryan Hübert
10 Creating and managing databases Ryan Hübert
11 Interacting with online databases Ryan Hübert

Detailed course schedule

Important note: Links to slides and code scripts will be updated/added in advance of each week’s teaching. There may also be minor adjustments/updates to the weekly readings posted below, so please monitor regularly.

1. Introduction

In the first week, we will introduce some basic concepts of how data is recorded and stored, and we will also review R fundamentals. Because the course relies fundamentally on GitHub, a collaborative code and data sharing platform, we will also discuss the use of git and GitHub.

Lecture

Seminar

Readings

Additional readings

2. Tabular data

This week discusses processing tabular data in R with functions from the tidyverse after some further review of R fundamentals.

Lecture

Seminar

Readings

Note: there is a newer version of the Wickham and Grolemund text from 2023, which is available at https://r4ds.hadley.nz/.

3. Data visualisation

The lecture this week will offer an overview of the principles of exploratory data analysis through summarising data and (good) data visualisation. In the coding session and seminars, we will practice producing our own graphs using ggplot2.

Lecture

Seminar

Reading

Further reading

Formative problem set

This is a formative assessment, and is due 1 November 2024 by 5pm. You must submit your response as a knitted .html file via the Moodle page.

Feedback on the Practice Problem Set will be returned by 15th November (if submitted by the deadline).

More details to be made available later in the term.

4. Textual data

We will learn how to work with unstructured data in the form of text and discuss character encoding, search and replace with regular expressions, and elementary quantitative textual analysis.

Lecture

Seminar

Reading

Further reading

5. HTML, CSS, and scraping static pages

This week we cover the basics of web scraping for tables and unstructured data from static pages. We will also discuss the client-server model.

Lecture

Seminar

Reading

Further reading

6. Reading week

Mid-term problem set

This is a summative assessment worth 50% of your final mark. It is due 22 November 2024 by 5pm. You must submit your response as a knitted .html file via the Moodle page.

Feedback on the Mid-term problem set will be returned as per the ASDS/SRM handbook.

More details to be made available later in the term.

7. XML, RSS, and scraping non-static pages

Continuing from the material covered in Week 5, we will learn the advanced topics in scraping the web. The topics include the scraping documents in XML (such as RSS), and scraping websites with non-static components with Selenium.

Lecture

Seminar

Reading

Further reading

8. APIs

This week discusses how to work with Application Programming Interfaces (APIs) that offer developers and researchers access to data in a structured format.

Lecture

Seminar

Reading

Further reading

9. Other data types

We will learn how to work with other data types, such as spatial data. Time permitting, we will also briefly discuss compute constraints and parallelization.

Lecture

Seminar

Reading

Further reading

10. Creating and managing databases

This session will offer an introduction to relational databases: structure, logic, and main types. We will learn how to write SQL code, a language designed to query this type of databases that is currently employed by many companies; and how to use it from R using the DBI package.

Lecture

Seminar

Reading

Further reading

11. NoSQL and cloud databases

This week covers how to set up and use relational databases in the cloud and fundamentals of a document based NoSQL database.

Lecture

Seminar

Required

Further reading

Final take-home assessment

This is a summative assessment worth 50% of your final mark. It is due Wednesday, 15 January 2025 by 5pm.

More details to be made available later in the term.