Predictive Analytics: Gaining Insights from Big Data (FutureLearn)

Become a data-driven organisation with the HPE Vertica Analytics platform

What’s big data? What’s analytics? How does machine learning fit into all this? And what can we learn from predictive models? Find out the answer to these questions – and more – through a real-world case study and learn how you can rely on HPE’s Vertica Analytics platform to power your big data analytics initiatives.

Derive analytical insight with the HPE Vertica Analytics platform

Over the next four weeks, experience the power of HPE’s Vertica Analytics platform as an applied tool. Using Vertica Analytics and a case study approach, you will apply built-in predictive analytics functions and algorithms – linear regression, logistics regression and k-means clustering – to derive insight from your data, helping to create opportunities for your organisation.

By the end of the course, you should be able to:

- describe big data analytics

- identify solutions to big data problems

- evaluate predictive data analysis models

- assess the suitability of predictive models

- model data using various predictive models.

Benefit from international perspectives

By joining a study group, you will have the opportunity to work collaboratively on a case study with peers across the globe.

Learn with big data researchers and industry leaders

The course has been created by the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) at Queensland University of Technology (QUT), following its successful Big Data Analytics program on FutureLearn.

ACEMS brings together a critical mass of Australia’s best researchers in mathematics, statistics and machine learning.

The course has been developed in association with HPE as part of its mission to address the significant global skills shortage in big data analytics.

REQUIREMENTS

Prior knowledge:

This course is aimed at data scientists, data analysts and those who need to deal with big data in their workplace. For learners without experience in this field, familiarity with SQL and UNIX is highly recommended in order to make the most of the learning opportunities.

Equipment:

There are many tools available for making sense of big data. In this course, we will be using Hewlett Packard Enterprise’s Vertica Analytics platform. New to Vertica Analytics? Don’t have the software? Don’t worry: we’ll send you a link with instructions on how you can download a data-limited version – yours to keep indefinitely. The downloadable virtual machine includes a case study dataset that we’ll be using for the practical sessions of this course.

You will need access to a Windows or Mac machine with the following:

- 64-bit Windows 7 or above; 64-bit macOS (previously known as OS X)

- 30 GB free disk space

- 12 GB RAM

- administrator rights to your machine so that you can install the VM Player.