| Learning objectives |
After completing the course, students should be
able to
- Characterize the phenomena of Big Data, Big Data Analytics, and
apply different concepts, methods, and tools for analysing big data
in organisational/societal contexts.
- Understand the linkages between business intelligence/analytics
and the potential costs to and benefits for organisations.
- Demonstrate the applicability of various analytical techniques
and algorithms on the big/organisational/social/open datasets to
derive critical insights.
- Critically assess, reflect and present the findings of big data
analytics in terms of meaningful facts, actionable insights and
their impact on organisations and society.
- Analyse and apply different visual analytics concepts and tools
for big datasets.
- Exhibit deeper knowledge and understanding of the topics as
part of the project and the report should reflect on critical
awareness of the methodological choices with written skills to
accepted academic standards.
|
| Course prerequisites |
This course is a part of the minor in Data in
Business.
This is a course about DOING big data analytics NOT talking about
it. Moreover, this is a fast-paced and intensive course comprising
visual, predictive and text analytics modules. Therefore, the
students are expected to have the knowledge and a background in
quantitative methods, without which it would be difficult to follow
the course content and analytical techniques and algorithms taught
in the course. As such the course requires an interest in and
commitment to hands-on learning. |
| Prerequisites for registering for the exam
(activities during the teaching period) |
|
Number of compulsory
activities which must be approved (see s. 13 of the Programme
Regulations): 1
Compulsory home
assignments
During the course, the students will have to take 2 multiple
choice quizzes. The students have get one quiz out of the two
quizzes approved to qualify for the final exam.
Each student has to get 1 activity approved in order to go to the
ordinary exam. There will not be any extra attempts provided to the
students before the ordinary exam. If a student cannot participate
in the activities due to documented illness, or if a student does
not get the activity approved in spite of making a real attempt,
then the student cannot participate the ordinary exam.
Before the re exam the student will be given one extra attempt: one
home assignment (5 pages).
|
| Examination |
|
Big Data
Analytics:
|
| Exam
ECTS |
7,5 |
| Examination form |
Home assignment - written product |
| Individual or group exam |
Group exam
Please note the rules in the Programme Regulations about
identification of individual contributions. |
| Number of people in the group |
2-4 |
| Size of written product |
Max. 40 pages |
|
The student can also choose to have an individual
exam. The size of the written product is 15 pages for an individual
exam. |
| Assignment type |
Project |
| Duration |
Written product to be submitted on specified date
and time. |
| Grading scale |
7-point grading scale |
| Examiner(s) |
Internal examiner and second internal
examiner |
| Exam period |
Winter |
| Make-up exam/re-exam |
Same examination form as the ordinary exam
* if the student fails the ordinary
exam the course coordinator chooses whether the student will have
to hand in a revised product for the re- take or a new
project.
|
|
| Course content, structure and pedagogical
approach |
|
This course is designed to provide knowledge of key concepts,
methods, techniques, and tools of big data analytics from a
business perspective. Course contents will cover issues in and
aspects of collecting, storing, manipulating, transforming,
processing, analysing, visualizing, and reporting big data in order
to create business value. Course topics are listed below:
- Foundations: Concepts, Lifecycle, Challenges, Opportunities,
and Exemplary Cases
- Data: Types, Structures & Tokens
- Data Mining and Machine Learning: Fundementals of machine
learning, Supervised and Unsupervised algorithms
- Visual Analytics: Visuvalizations techniques, dashboards &
Tools
- Text Analytics: keyword analysis, text classification
(sentiment analysis) & Topic Modelling
- Predictive Analytics: Correlation, Regression, and
Autometrics
- Computational Social Science: Social Set Analytics
|
| Description of the teaching methods |
Lectures
Exercises
Demos
Tutorials
Cases |
| Feedback during the teaching period |
| As part of the mandatory assignments, the
students will take 2 multiple choice quizzes. The students will
receive feedback on the quizzes on whether her/his chosen answer is
wrong and a clue to where she/he can read up on the
subject. |
| Student workload |
| Lectures & Exercises |
30 hours |
| Self study |
48 hours |
| E-learning |
48 hours |
| Project Work |
50 hours |
| Project Report |
30 hours |
| Total Hours |
206 hours |
|
| Expected literature |
|
The expected literature might be changed before the semester
starts. Students are advised to find the final literature on the
Canvas before the start of the class.
Books:
- Provost, F., & Fawcett, T. (2013). Data Science for
Business: What you need to know about data mining and data-analytic
thinking. O'Reilly Media, Inc.
- Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting:
principles and practice: OTexts:
https://www.otexts.org/fpp/
- Milhoj, A. (2013). Practical Time Series Analysis Using SAS:
SAS Institute.
- Jurafsky, D., & Martin, J. H. (2018). Naive Bayes and
Sentiment Classification. Chapter 4 of Speech and language
processing (3rd Edition)
- Chapter 06 of Aggarwal, C. C., & Zhai, C. (2012). Mining
text data: Springer Science & Business Media.
Research Papers:
- Davenport, T. (2014). 10 Kinds of Stories to Tell with Data.
Blog post at Harvard Business Review.
http://blogs.hbr.org/2014/05/10-kinds-of-stories-to-tell-with-data/
- Boyd, D., & Crawford, K. (2012). Critical questions for big
data: Provocations for a cultural, technological, and scholarly
phenomenon. Information, communication & society, 15(5),
662-679.
- Singh, D., & Reddy, C. K. (2015). A survey on platforms for
big data analytics. Journal of big data, 2(1), 8.
- Tufekci, Z. (2014). Big Questions for Social Media Big Data:
Representativeness, Validity and Other Methodological Pitfalls.
Proceedings of the 8th International Aaai Conference on Weblogs and
Social Media(ICWSM) 2014, Ann Arbor, USA, June 2-4, 2014.
- Executive Summary Thomas, J. J., & Cook, K. A. (Eds.).
(2005). Illuminating the path: The research and development agenda
for visual analytics. IEEE Computer Society Press.
- Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour
through the visualization zoo. Commun. ACM, 53(6), 59-67.
- Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S.
(2012). Interactions with big data analytics. interactions, 19(3),
50-59.
- Chapter 27 of Munzner, T. (2009). Visualization. Fundamentals
of Graphics, Third Edition. AK Peters, 675-707.
|
Permalink