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2018/2019  KAN-CDASO1030U  Foundations of Business Data Analytics: Architectures, Statistics and Programming

English Title
Foundations of Business Data Analytics: Architectures, Statistics and Programming

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 19-06-2018

Relevant links
Learning objectives
  • Summarize different fundamental concepts, techniques and methods of big data processing
  • Describe and analyse various architectures and platforms for big data processing
  • Design and implement interactive programs using Python programming language using its appropriate linguistic features.
  • Demonstrate understanding of imperative, declarative and object oriented language features of Python language and know when it is appropriate to use each.
  • Write programs in Python programming language that make use of external libraries, APIs, etc.
  • Demonstrate basic understanding of mathematical and statistical foundations needed for data mining and machine learning.
  • 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
Even though there are no prerequisites for this course, it is recommended to have some experience with programming and understanding basic statistics. Moreover, this course is a demanding course as the students will learn new technologies, methods, Python programming language and therefore it 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: 3
Compulsory home assignments
Each assignment is 1-3 pages in group of 1-4 students.
The students have to pass 3 out of 5 assignments.
Examination
Foundations of Business Data Analytics: Architectures, Statistics and Programming:
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.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-4
Size of written product Max. 15 pages
Assignment type Project
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-step scale
Examiner(s) Internal examiner and second internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
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.
Course content and structure

This course provides an introduction to three main areas: big data architectures/platforms, Python programming and mathematical/statistical foundations.

Furthermore, this course provides knowledge about

  • Fundamental concepts, methods and techniques of big data
  • Architectures and platforms for big data processing such Hadoop, Spark, distributed file systems
  • Introduction to Python programming language such as programming basics, Boolean algebra, choice, repetition
  • Functions, classes, modules, data structures and collections in Python language
  • Consuming external APIs and open source libraries to develop programs
  • Algorithm Design and Recursion
  • Elementary probability theory, Bayes theorem, standard distributions, Bayesian statistics,fundamentals of Linear Algebra
  • Information theory, entropy, mutual information

 

Furthermore, the course provides the students with practical hands-on experience on many of the topics listed above. After completing the course the students will be able to apply and use various programming constructs in Python language and also a good understanding of big data architectures and foundational mathematical/statistical theories that are required for data science courses.
Description of the teaching methods
The course consists of lectures, exercises, and assignments. Each lecture is followed by an exercise session, and there will be a teaching assistant providing technical support for assignments and course projects.

The presented theories, concepts and methods should be applied in practice and exercise sessions. The students work in the entire semester on a mini project displaying the understanding of the concepts presented in the lectures and exercises. CBS Learn is used for sharing documents, slides, exercises etc. as well as for interactive lessons if applicable.
Feedback during the teaching period
Feedback on the mandatory assignment will be provided in general
Student workload
Lectures 24 hours
Exercises 24 hours
Prepare to class 48 hours
Project work & report 100 hours
Exam and prepare 10 hours
Total 206 hours
Expected literature

Textbooks:

  • Zelle, John M. Python programming: an introduction to computer science. Franklin, Beedle & Associates, Inc., 2004.
  • Manning, Christopher D., and Hinrich Schütze. Foundations of statistical natural language processing. Vol. 999. Cambridge: MIT press, 1999.
  • Furht, Borko, and Flavio Villanustre. Big Data Technologies and Applications. Springer, 2016.

 

Notes, scientific articles, chapters and webpages will be handed out/made available during the course
Last updated on 19-06-2018