Core Course
BI420 – Python Programming
Course Unit Code: BI420
Type Of Unit: Electives
Level of Course Unit: Second cycle
Year of Study: First/second year
Semester: On demand
Number of ECTS Credits: 6
Class Contact Hours: 28
Mode of Delivery
Face to Face
Prerequisites
None
Course Objectives
This course will introduce the students to the world of programming and teach them the fundamentals underpinning the theory of algorithms, programming and data structures. It includes a fast-paced introduction to the fundamentals of a scripting language (e.g., Python) which is widely used in the area of data science. During the class a lot of emphasis will be given on programming from scratch of well-known machine learning algorithms, for both clustering and classification, as used in data-science. By completion of this course students will be in a position to use the programming language they learned to develop programs in order to perform specific tasks. For example, mine, clean, analyze and visualize datasets and thus solve business-oriented problems efficiently.
Learning Outcomes
- Understand how to characterize data in terms of quality in the context of data-driven decision making.
- Learn to program efficiently in a scripting language (e.g., Python) widely used in data science for both mining and visualization purposes.
- Understand the basic concepts used in programming and algorithms.
- Demonstrate an understanding of how to select appropriate data structures and algorithmic procedures for addressing a problem of interest.
- Understand how to scrape, cleanse and de-dupe data making them suitable for analysis using techniques such as regular expressions.
- Program from scratch fundamental data-science classification algorithms such as Naive Bayes, Simple Linear Regression, Multiple Regression, Logistic Regression etc.
- Program of clustering algorithms such as k-Nearest Neighbours.
Course Content
Course Features
Planned learning activities and teaching methods
lectures, group work, homework lab work, role playing,project-based learning,
Assessment criteria and methods
10% Class participation
50% Assignments
40% In-class examination
Language of Instruction
English
Work Placement(s)
Not applicable
Readings
Required Reading:
1. Joel Grus. Data Science from Scratch. O’Reilly, 1st edition, 2015.
Recommended Reading:
2. Wes McKinney. Python for Data Analysis. O’Reilley, 1st edition, 2012.
3. Luciano Ramalho. Fluent Python. O’Reilley 1st edition, 2015.
English (UK) 
Ελληνικά