Machine Learning with Big Data for Social Scientists

The course has two core topics:

• constructing and managing large datasets in R
• machine learning, with specific focus on providing analytics from large datasets. 

For the first topic, big data, we start by asking: What is big data? Why it is difficult to work with? We then learn the best solutions in R for working with big data depending on the size of data, from locally stored data objects to databases hosted on the cloud. 

For the second topic, machine learning, we will learn basic concepts such as:

1. problem definitions
2. objective function
3. bias-variance trade-offs
4. parameter tuning. 

Social scientists have traditionally emphasised the explanation as the primary purpose of statistical analysis. Machine learning has an overlapping but evidently different orientation.

By contrasting the inference-based approach and prediction-focused approach, you get to understand the fundamental ideas of machine learning.

After theoretical discussions, we will apply machine learning techniques to various analytical tasks in social sciences.

Day 1

Data Management in R
We discuss big data, what it is, and why it's difficult to work with. We cover how to set up computation environments in the cloud, introduce cloud computing concepts, and explain why the shift to cloud computing is occurring. To conclude the day, you will work with large data in R, starting from R objects (dataframe, tibble, etc), databases, and distributed-computing framework. 

Day 2

Databases & Machine Learning Basics
Learn the methods for handling big data using various types of databases, which unload the data from R working space while keeping it highly accessible. Explore the basics of relational databases, such as concepts, rules, and design followed by the syntax of SQL query. Participate in general discussions on the logic of machine learning to understand the basics.

Day 3

Regression
Revisiting linear regression, learn how to interpret the regression problem in the machine learning framework, covering cases where the outcome is a continuous quantity. Explore the issue of variable selection, which is frequently faced with big data that has numerous input features. Learn about typical shrinkage methods such as Ridge regression and LASSO. We will also introduce the resampling method.

Day 4

Classification Methods 1
Moving to cases where the outcome is categorical, we discuss supervised classification, in which we apply machine learning algorithms to predict known values of outputs. Using a classification method familiar to social scientists, you will learn how to evaluate the models in a machine learning framework. We will discuss model performance evaluation in greater detail.

Day 5

Classification Methods 2
Continuing on from the previous day, we focus on tree-based methods. Starting with a simple tree, we'll move on to more sophisticated methods such as random forest and boosting. To finish, we will revisit the issue of data size and get an overview of the methodology for distributed computing, which can get insights from big data as a whole.

How the course will work online

The course provides approximately 5 hours of pre-recorded lectures, plus an online forum on Slack where the Instructor and students can freely discuss the lecture materials and coding. 

Approximately two hours of each day will be an online seminar, during which we will learn how to apply the concepts and knowledge gained from pre-course lecture materials through Q&A and the live lab work. 

In the live lab, you will be given several coding tasks, and asked to code along with the Instructor. Some tasks are left as homework, which we will discuss on the online forum and during the following day’s live lab. 

You’ll also be able to advance-book one-to-one consultations with the Instructor during office hours. 

You will learn how to work with cloud computational workspace using R as a primary statistical software, with RStudio server and Google Colab. We will distribute example scripts and assignments through github so you can learn how to use online version control systems for collaboration and research accountability.

The course assumes you have some familiarity with R statistical language and can conduct basic data handling in R (opening data files, working with data frames). If you don’t have this, take the week-one course Introduction to R. 

You should also have basic knowledge of standard statistical analysis in social science, such as linear regression and hypothesis testing. These are covered in courses Introduction to Inferential Statistics and Big Data Collection and Management in R.

Before Day 1, please complete the following:

• Readings – mostly textbook materials. Approximately two hours' reading for each day of the course; 10 hours in total.
• Lectures – pre-recorded videos accompanied by supporting materials. A one-hour video for each day of the course; 5 hours in total.