English   Danish

2020/2021  DIP-D1FMV2025U  Data analytics with Python

English Title
Data analytics with Python

Course information

Language English
Course ECTS 5 ECTS
Type Elective
Level Graduate Diploma
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 40
Study board
Study Board for Graduate Diploma in Business Administration
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation
Main academic disciplines
  • Information technology
Teaching methods
  • Face-to-face teaching
Last updated on 02-03-2020

Relevant links

Learning objectives
  • Demonstrate understanding of imperative, declarative and object-oriented language features of Python language and know when it is appropriate to use each.
  • Demonstrate basic Python coding skills for data manipulation, data analysis, and data visualization on various data formats.
  • Characterize the linkages between different data analytics operations such as business understanding, data understanding, data preparation data transformations, data modelling, data visualization and evaluation.
  • Demonstrate an understanding of the underlying data mining and machine learning algorithms and differences between supervised and unsupervised approaches in data analytics
  • Write programs in Python programming language that make use of external libraries, APIs, etc.
  • Identify the key challenges and design goals for performing data analysis using Python programming
  • Critically assess the advantages of using of Python programming for data analytics
  • Design and implement interactive programs using Python programming language using its appropriate linguistic features.
Course prerequisites
No prerequisites for this course, but it would be advantageous to have some experience with any programming language.
Examination
Data Analytics with Python:
Exam ECTS 5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Duration 7 days to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Spring
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

The primary goal of this course is to introduce Python programming skills to the

students with a purpose to collect, transform, model, analyze and visualizes

broad range of datasets. The course uses the Python programming language

to learn how to work with numerical, string, and more complex data formats,

and to perform data analysis with basic data mining and machine algorithms

using both supervised and unsupervised approaches.

 

With a keep focus on open source technologies, the course will focus providing

hands-on experience with open source libraries in Python for data mining,

machine learning and data visualizations. Finally, students will develop

practical programming skills in problem solving by working on real-world

datasets as part of their final project.

 

Course content:

 

• Introduction to Python programming language constructs such as

programming basics, control flow, Operators, expressions, choice,

repetition

 

• Functions, data structures and collections in Python language

 

• Object oriented programming features of Python language such as

classes, methods.

 

• Exception handling, standard libraries, consuming external APIs and open

source libraries to develop programs

 

• Data transformations and text processing including reading and writing

files

 

• Data analysis with basic data mining and machine learning algorithms for

clustering, classification using unsupervised and supervised approaches

 

• Data visualizations using open source libraries in Python such as

matplotlib, ggplot, pygal etc.

Description of the teaching methods
Lectures and group work
Feedback during the teaching period
During classes working on exercises
Student workload
In class 30 hours
Preparation and exam 120 hours
Expected literature

 

Authors(s)

Title

Publisher/ ISBN/ DOI

 

Books/chapters

 

[ICPP]

John V. Guttag

Introduction to Computation and Programming Using Python

The MIT Press/

ISBN-13: 978-0262519632

 

[PPIC]

John Zelle

Python programming: an introduction to computer science

Franklin, Beedle & Associates; 3rd edition (August 8, 2016),  

ISBN-13: 978-1590282755

 

[BP]

Swaroop C H

A Byte of Python

 

Online Book Link:

https:/​/​python.swaroopch.com/​

 

[PDSH]

Jake VanderPlas

Python Data Science Handbook: Essential Tools for Working with Data

Oreilly, ISBN-13: 978-1491912058

 

Online Book Link:

https:/​/​jakevdp.github.io/​PythonDataScienceHandbook/​

 

https:/​/​github.com/​jakevdp/​PythonDataScienceHandbook

 

 

 

Last updated on 02-03-2020