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
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 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.
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.