| Learning objectives |
To achieve the grade 12, students should meet the
following learning objectives with no or only minor mistakes or
errors:
- 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.
The course has a highly practical and hands on approach to Data
Science. If you prefer more theoretical courses this course may not
be for you. 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 section 13 of the Programme
Regulations): 2
Compulsory home
assignments
Each student has to get 2 out of 3 activities approved in order to
qualify for the final exam.
There are three group reports of max. 5 pages written in groups of
2-4 students.
Each report forms the foundation of a part of the final report.
This ensures the students will understand the expectations of the
final before submission.
There will not be any extra attempts provided to the students
before the ordinary exam. If a student cannot participate in the
compulsory activities due to documented illness, or if a student
does not have the activities approved in spite of making a real
attempt, then the student will be given one extra attempt before
the re-exam: one home assignment (max.10 pages) which will cover 2
mandatory activities.
|
| Examination |
|
Big Data
Analytics:
|
| Exam
ECTS |
7,5 |
| Examination form |
Oral exam based on written product
In order to participate in the oral exam, the written product
must be handed in before the oral exam; by the set deadline. The
grade is based on an overall assessment of the written product and
the individual oral performance, see also the rules about
examination forms in the programme regulations. |
| Individual or group exam |
Oral group exam based on written group
product |
| Number of people in the group |
2-4 |
| Size of written product |
Max. 30 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 |
| Release of assignment |
Subject chosen by students themselves, see
guidelines if any |
| Duration |
Written product to be submitted on specified date and
time.
20 min. per student, including examiners' discussion of grade,
and informing plus explaining the grade |
| 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
The students can choose to hand in
the same project, or a new or revised project.
|
Description of the exam
procedure
The final exam is a group oral exam based on a group written
product of 2-4 students (maximum 30 pages). For individuals with
special approval from administration, the maximum is 15 pages. The
written product is based on a group project geared towards a
business or social application of big data analytics (text
analytics, machine learning, etc.) that builds on the topics and
techniques covered in class. In the written product, students are
expected to provide the context with clear motivation, articulate
the current state of research on the topic, derive potential
research questions needed to advance knowledge in the area, and
discuss the steps
needed.
|
|
| 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: text classification (sentiment analysis) &
Topic Modelling
- Analytics: Correlation, Regression, and Machine
Learning
The course utilizes R and R Studio for all activities. This
software is available for free.
|
| 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.
As part of the mandatory assignments, the students will have to
prepare reports (max. 5 pages) written in a group of 2-4 students.
Each group will be provided with written feedback on the reports.
In addition to that feedback on the hands-on exercise will also be
provided in the classroom. 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.
LECTURE NOTES
You will find my lecture notes on canvas for each topic. My
lecutre notes cover everything you need for my course.
IF you want to dig deeper into the topics covered, please find
the further reading below.
FURTHER READING
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.
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