About this course
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.
What you'll learn
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.
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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Frequently asked questions
Question: This course is self-paced, but is there a course end date?
Answer: Yes. The first course release started on December 1, 2017 and ends on April 1, 2019.
The new release of the course starts on March 1, 2019 and ends on December 1, 2020.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.