MicroMasters® Program in Big Data

via

edX

Course Features

Duration

12 months

Delivery Method

Online

Available on

Limited Access

Accessibility

Desktop, Laptop

Language

English

Subtitles

English

Level

Advanced

Effort

9 hours per week

Teaching Type

Self Paced

Course Description

Businesses are changing their approach to big data. Businesses can now analyse and collect data insights to improve their decision-making, thanks to a new level of data collection.

As companies seek to improve their business operations through data, they are looking for business analysts and data scientists.

This Big Data MicroMasters program will teach you tools and analytical techniques to use data to make decisions, collect and organize data at scale, as well as how data analysis can be used to support organizational change.

You’, will acquire both technical and computational skills that are highly sought after across a variety of industries. You’, will develop essential skills in programming for data science and computational thinking.

Course Overview

International Faculty

Post Course Interactions

Instructor-Moderated Discussions

Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Data-driven problem and algorithm design for big data

How to use analytical tools such as R and Java

Interpretation of data representation and analysis

Understand computational thinking which includes decomposition, pattern recognition and abstraction

Understand fundamental programming concepts including data abstraction, storage and structures

Understand key mathematical concepts, including dimension reduction and Bayesian models

How to design algorithms

Course Content

Expand all sections

Module 1: Programming for Data Science

Module 2: Computational Thinking and Big Data

Module 3: Big Data Fundamentals

Module 4: Big Data Analytics

Course Instructors

Aneta Neumann

Postgraduate Researcher, School of Computer Science

Aneta is currently undertaking postgraduate research in the School of Computer Science at the University of Adelaide. Her main research interest is understanding the fundamental link between bio-inspired computation and digital art.

David Suter

Professor of Computer Science

David is a Professor in the School of Computer Science at The University of Adelaide. Prior to that he was a Professor in the Dept. of Electrical and Computer Systems Engineering at Monash University...

Frank Neumann

Professor, School of Computer Science

Frank is a professor in the School of Computer Science and in his work he considers algorithmic approaches in particular for combined and multi-objective optimising problems. He focuses on theoretica...

Gary Glonek

Associate Professor, School of Mathematical Sciences

Gary is a lecturer in statistics and the Head of the School of Mathematical Sciences at the University of Adelaide. His research interests are in statistics, especially with applications in bioinform...