English   Danish

2019/2020  KAN-CDASO1930U  Foundations of Business Data Analytics: Programming and Linear Algebra

English Title
Foundations of Business Data Analytics: Programming and Linear Algebra

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 12-06-2019

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 get 3 out of 5 assignments approved in order to go to the exam.

There will not be any extra attempts provided to the students before the ordinary exam.
If a student cannot hand in due to documented illness, or if a student fails the activity in spite of making a real attempt to pass the activity, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (max.10 pages) which will cover 3 mandatory assignments.
Examination
Foundations of Business Data Analytics: Programming and Linear Algebra:
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-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
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, structure and pedagogical approach

This course provides an introduction to three main areas:
Python programming, mathematical/statistical foundations of Data Science such as Linear Algebra and Big data architectures/platforms.

Furthermore, this course provides knowledge about

 

  • Introduction to Python programming language such as programming basics, Boolean algebra, choice, repetition
  • Functions, classes, modules, data structures and collections in Python language
  • Introduction to Algorithmic Complexity and some simple Algorithms and Data Structures
  • Linear Algebra: Vectors, Vector Spaces, Basis
  • Matrices, Dimensions,Gaussian Elimination, and Inner Product, Eigenvector
  • Elementary probability theory, standard distributions,
  • Architectures and platforms for big data processing such Hadoop, Spark, distributed file systems
  • Git fundamentals and branching

 

 

 

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 28 hours
Exercises 28 hours
Prepare to class 44 hours
Project work & report 96 hours
Exam and prepare 10 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.Notes, scientific articles, chapters and webpages will be handed out/made available during the course

 

Textbooks:

 

 

Authors(s)

Title

Publisher/ ISBN/ DOI

[ICPP]

John V. Guttag

Introduction to Computation and Programming Using Python

The MIT Press/

ISBN-13: 978-0262519632

[PPICS]

John M. Zelle

 

Python Programming:

An Introduction to Computer Science

Franklin, Beedle & Associates; 3rd edition

ISBN-10: 1590282752

ISBN-13: 978-1590282755

[CMLA]

Philip N. Klein

Coding the Matrix: Linear Algebra through Applications to Computer Science

Newtonian Press/ ISBN-13: 978-0615880990

[PG]

Scott Chacon, Ben Straub

Pro Git

https:/​/​git-scm.com/​book/​en/​v2

[LAP]

David C. Lay, Steven R. Lay, Judi J. McDonald

Linear Algebra and Its Applications (FIFTH EDITION)

Pearson; 5 edition

ISBN-10: 1292092238

ISBN-13: 978-1292092232

 

 

 

 

 

 

 

 

 

 

Journal papers:

 

[1]

Singh, Dilpreet, and Chandan K. Reddy. "A survey on platforms for big data analytics." Journal of Big Data 2, no. 1 (2015): 8.

[2]

Chen, Min, Shiwen Mao, and Yunhao Liu. "Big data: A survey." Mobile networks and applications 19, no. 2 (2014): 171-209.

[3]

Fang, Hua, Zhaoyang Zhang, Chanpaul Jin Wang, Mahmoud Daneshmand, Chonggang Wang, and Honggang Wang. "A survey of big data research." IEEE network 29, no. 5 (2015): 6-9.

[4]

Oetiker, Tobias, Hubert Partl, Irene Hyna, and Elisabeth Schlegl. "The not so short introduction to LATEX2ε." Electronic document available at  https:/​/​tobi.oetiker.ch/​lshort/​lshort.pdf  (1995).
Last updated on 12-06-2019