SURV751: Big Data and Machine Learning

Data Analysis

The amount of data generated as a by-product in society is growing fast including data from satellites, sensors, transactions, social media and smartphones, just to name a few. Such data are often referred to as "big data", and can be used to create value in different areas such as health and crime prevention, commerce and fraud detection.  Big Data are often used for prediction and classification tasks. Both of which can be tackled with machine learning techniques. In this course we explore how Big Data concepts, processes and methods can be used within the context of Survey Research.  Throughout this course we will illustrate key concepts using specific survey research examples including tailored survey designs and nonresponse adjustments and evaluation. 

We will start with a discussion of key Big Data terminology and concepts. We place emphasis on understanding data generating processes and errors that can occur during these processes. Parallels between the errors in survey data collection and Big Data gatherings will be discussed. Special emphasis will be given to coverage error and measurement error. The key goal of any analytics task is information extraction and signal detection. Such task can look quite differently in the context of Big Data. We will compare common statistical methods to those use in the Big Data context and explain the difference in focus on prediction vs. causation. Most of the course time will be spend on general machine learning concepts, potential pitfalls, and the actual analytic processing steps when conducting applying techniques such as classification trees, random forests, conditional forests to process Big Data.

We use R and provide example code for the homework problems.

Course objectives: 

This course covers

  • an overview of key Big Data terminology and concepts
  • an introduction to common data generating processes
  • a discussion of some primary issues with linking Big Data with Survey Data
  • issues of coverage and measurement errors within the Big Data context
  • inference versus prediction
  • general concepts from machine learning including signal detection and information extraction
  • potential pitfalls for inference from Big Data
  • key analytic techniques (e.g. classification trees, random forests, conditional forests) to process Big Data using R with example code provided

Grading will be based on:

  • 4 online quizzes (worth 5% each)
  • Participation in discussion during the weekly online meetings and submission of questions via discussion form (deadline: Sunday, 1:00 PM EDT/7:00 PM CEST before class) demonstrating understanding of the required readings and video lectures (20% of grade). Obviously in the first week one question will be enough, since we just started.
  • 3 homework assignments (worth 20% each)

Dates of when assignment will be due are indicated in the syllabus. Late assignments will not be accepted without prior arrangement with the instructors.



No prerequisite.

We recommend good understanding of the material typically taught in undergraduate statistics courses and some familiarity with regression techniques. Knowledge about survey data collection at the level provided in the IPSDS course Fundamentals of Survey and Data Science.
While not a prerequisite, familiarity with the R software package (base R or R using Rstudio) is strongly encouraged. 

Course Dates


Spring Term (March – May)


Spring Term (March – May)