FutureLearn: Big Data: Statistical Inference and Machine Learning

 with  Tomasz Bednarz, Ian Turner and Kerrie Mengersen


Many people have big data but only some people know what to do with it. Why? Well, the big problem is that the data is big—the size, complexity and diversity of datasets increases every day. This means we need new solutions for analysing data.

This course equips you for working with these solutions by introducing you to selected statistical and machine learning techniques used for analysing large datasets and extracting information. We also expose you to three software packages so you can develop your coding skills by completing practical exercises.

You will enjoy this course most and benefit from the learning experience if you have a basic understanding of statistics and mathematics at a university undergraduate level.

You will be using the following free tools. Please review the product websites below to ensure your system meets the minimum requirements:

R and R Studio Desktop (open source edition)

You will complete practical exercises using R Studio, so you’ll need to be familiar enough with R to:

  • install a package
  • import data
  • read and run starter code
  • develop a solution or read through a solution and gain understanding from it.

NOTE: You must first have a working installation of R to use R Studio.

H2O Flow
H2O Flow can be used as a stand-alone package for big data analytics or can be used in conjunction with R. This package will allow you to tackle larger problems that you might encounter in your own work.

WEKA is a popular workbench for machine learning and statistical analysis. It comprises a very wide range of tools that are suitable for big data analysis.

Knowing R, H2O Flow and WEKA will give you a powerful, flexible and scalable set of tools to manipulate and analyse big data.

3 Student
Cost Free Online Course (Audit)
Pace Finished
Subject Big Data
Provider FutureLearn
Language English
Certificates Paid Certificate Available
Hours 2 hours a week
Calendar 2 weeks long

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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

Reviews for FutureLearn's Big Data: Statistical Inference and Machine Learning
4.0 Based on 3 reviews

  • 5 star 33%
  • 4 star 33%
  • 3 star 33%
  • 2 star 0%
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  • 1
5.0 2 years ago
by Ronny De Winter completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
Nice exploratory course. All levels of experience are welcome but I guess you get the most of it if you already did some introductory course before.

The coure covers some interesting tools like H2o and weka. It only touches the surface of these tools but the examples gives you a good idea of their power.

There is very good student community, with good interactions, additional references, ...

The examples are coded in R, however not necessary it is helpful if you have some background in R.

The mentioned 2 hours per week are probably not enough, I would recommend about 6 hours.
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4.0 a year ago
by Y. Nicodeme completed this course.
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3.0 2 years ago
by Colin Khein completed this course.
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