Hands On Training

Big Data Fundamentals

University of Adelaide

via

edX

Course Features

Duration

10 weeks

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Effort

10 hours per week

Teaching Type

Self Paced

Course Description

Organizations now have access to massive amounts of data and it’s influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.

In this course, part of the Big Data MicroMasters program, you will learn how big data is driving organisational change and the key challenges organizations face when trying to analyse massive data sets.

You will learn fundamental techniques, such as data mining and stream processing. You will also learn how to design and implement PageRank algorithms using MapReduce, a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. You will learn how big data has improved web search and how online advertising systems work.

By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research.

Course Overview

International Faculty

Post Course Interactions

Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Candidates interested in pursuing the MicroMasters program in Big Data are advised to completeProgramming for Data Science and Computational Thinking and Big Databefore undertaking this course.

What You Will Learn

Knowledge and application of MapReduce

Understanding the rate of occurrences of events in big data

How to design algorithms for stream processing and counting of frequent elements in Big Data

Understand and design PageRank algorithms

Understand underlying random walk algorithms

Course Content

Expand all sections

Module 1: The basics of working with big data

Module 2: Web and social networks

Module 3: Clustering big data

Module 4: Google web search

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.

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...

Wanru (Kelly) Gao

Lecturer, School of Computer Science

Kelly is a lecturer in the School of Computer Science at the University of Adelaide. She has been teaching several introductory computer science courses and some advanced courses about algorithms and...

Vahid Roostapour

PhD Student, School of Computer Science

Vahid is a PhD student in the School of Computer Science at the University of Adelaide. His research focuses on bio-inspired algorithms and problems with dynamically changing constraints. He is also ...