Big Data Capstone Project
list 6 sequences
assignment Level : Advanced
chat_bubble_outline Language : English
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Key information

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verified_user Fee-based Certificate
timer 24 hours in total

About the content

The Big Data Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this Big Data MicroMasters program to a medium-scale data science project.

Working with organisations and stakeholders of your choice on a real-world dataset, you will further develop your data science skills and knowledge.

This project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem.

This project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.

You’ll deepen your learning of social and ethical concerns in relation to data science, including an analysis of ethical concerns and ethical frameworks in relation to data selection and data management.

By communicating the knowledge, skills and ideas you have gained to other learners through online collaborative technologies, you will learn valuable communication skills, important for any career. You’ll also deliver a written presentation of your project design, plan, methodologies, and outcomes.

The Big Data Capstone project will give you the chance to demonstrate practically what you have learned in the Big Data MicroMasters program including:

  • How to evaluate, select and apply data science techniques, principles and theory;
  • How to plan and execute a project;
  • Work autonomously using your own initiative;
  • Identify social and ethical concerns around your project;
  • Develop communication skills using online collaborative technologies.

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Prerequisite

Candidates interested in pursuing this program are advised to complete Programming for Data ScienceComputational Thinking and Big DataBig Data Fundamentals & Big Data Analytics before this course.

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Syllabus

Dataset overview, data selection and ethics
Understand ethical issues and concerns around big data projects; Describe how ethical issues apply to the sample dataset; Describe up to three ethical approaches; Apply ethical analysis to scenarios.

Exam (timed, proctored)
The exam will cover content from the first four courses in the Big Data MicroMasters program, including the Ethics section of this capstone course, DataCapX. It will include questions on topics such as code structure and testing, variable types, graphs, big data algorithms, regression and ethics. 

Project Task 1: Data cleaning and Regression
Understand the basic data cleaning and preprocessing steps required in the analysis of a real data set; Create computer code to read data and perform data cleaning and preprocessing; Judge the appropriateness of a fitted regression model to the data; Determine whether simplification of a regression model is appropriate; Apply a fitted regression model to obtain predictions for new observations.

Project Task 2: Classification
Build classifiers to predict the output of a desired factor; Analyse learned classifiers; Design a feature selection scheme; Design a scheme for evaluating the performance of classifiers.

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Instructors

Nick Falkner
Associate Professor, School of Computer Science
University of Adelaide

Gary Glonek
Associate Professor, School of Mathematical Sciences
University of Adelaide

Lingqiao Liu
Researcher, School of Computer Science
University of Adelaide

Gavin Meredith
Research Associate, School of Computer Science
University of Adelaide

Ian Knight
Lecturer, School of Computer Science
University of Adelaide

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Content designer

University of Adelaide
University of Adelaide
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Platform

Edx

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