Upon successful completion of this course, students should be able to:
- Deploy the Data Analytics Lifecycle to address big data analytics challenges
- Reframe a business issue as an analytics challenge
- Choose the appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
- Select appropriate data visualization technique to communicate analytic insights to business sponsors and analytic audience
- Use R and RStudio for data analysis purposes
- Explain how advanced analytics can be leveraged to create international competitive advantage
- Develop group project report, and communicate results through group presentation
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This course provides practical foundation level knowledge of Big Data analytics. It includes an introduction to big data and the Data Analytics Lifecycle to address international business issues that leverage big data. The course provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools using the statistical programming language R. The course is divided between lectures and labs. The labs using R offer opportunities for students to understand how these methods and tools may be applied to real world business issue. The course takes an open and technology-neutral approach, and includes a final lab which addresses a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle.
Content and timetable
Lectures:
- Week One: Introduction to Big Data Analytics
- Week Two: Data Analytics Lifecycle
- Week Three: Review of Basic Data Analytic Methods
- Week Four: Advanced Analytical Theory and Methods: Clustering
- Week Five: Advanced Analytical Theory and Methods: Association Rules
- Week Six: Advanced Analytical Theory and Methods: Classification
- Week Seven: Putting It All Together
Weekly technical labs using R-language are parallel to the lectures where students apply the theoretical knowledge they learned.
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To complete this course successfully and gain the maximum benefits from it, a student should have quantitative background and a solid understanding of basic statistics, as would be found in:
- Statistics
- Quantitative Research Methods
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Examination
Written exam (taken digitally)
Project report and presentation (based on group project)
Both parts of the examination need to be ≥ 5.5 in order to pass the course
Entry requirements exams
80% attendance of both lectures and labs
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