Big Data Capstone Project

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.

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. Itwill 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.