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MODULE DESCRIPTOR
Module Title
Introduction to Data Science with Python
Reference CM3400 Version 2
Created November 2022 SCQF Level SCQF 9
Approved August 2020 SCQF Points 15
Amended January 2023 ECTS Points 7.5

Aims of Module
To introduce students to the main concepts, techniques and challenges involved in Data Science applications and to provide students with practical experience in the whole Data Science lifecycle (data cleaning, data transformation, data analysis and results interpretation and reporting) using Python.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Explain the data science lifecycle and the role of data strategies, professional, ethical and legal issues within data analysis.
2 Clean and transform data for analysis.
3 Apply statistical and visualisation techniques to a variety of datasets.
4 Apply algorithms to extract information from a dataset.
5 Effectively communicate results through appropriate conclusions and visualisation.

Indicative Module Content
The Data Science lifecycle; frameworks for data science projects, and data strategies. Professional, ethical and legal issues involved within data analysis; data bias. Data exploration, data preparation and data cleaning methods. Data summarisation, data transformation and data visualisation techniques. Introduction to and use of Python libraries to process and analyse a range of data types, and to apply data science algorithms to datasets. Statistical techniques for data analysis: summary statistics; visualising data distributions; regression; correlation; clustering; classification.

Module Delivery
The module is delivered using a combination of online self-study materials, directed reading and activities and supported using virtual workshops and tutor support.

Indicative Student Workload Full Time Part Time
Contact Hours N/A 24
Non-Contact Hours N/A 126
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL N/A 150
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4, 5
Description: A capstone project applying steps of the data science lifecycle to a dataset and presenting the analysis and conclusions in the form of a short report.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
There are formative quizzes as part of the course materials. There is a summative written coursework contributing 100% toward the final module grade.
Module Grade Minimum Requirements to achieve Module Grade:
A Grade A in C1
B Grade B in C1
C Grade C in C1
D Grade D in C1
E Grade E in C1
F Grade F in C1
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 Igual,L and Segui, S. 2017. Introduction to data science : a Python approach to concepts, techniques and applications. Cham, Switzerland : Springer
2 MCKINNEY, W., 2017. Python for Data Analysis 2nd Edition. O'Reilly.
3 VARGA, E., 2019. Practical Data Science with Python 3: Synthesizing Actionable Insights from Data. Berkeley, CA: Apress
4 PADMANBHAN, T.R., 2016. Programming with Python. Singapore: Springer.
5 LANE, D.M. et al., n.d. Online statistics education: an interactive multimedia course of study. [online]. Houston, TX: Rice University. Available from: http://onlinestatbook.com/
6 Pierson L. Data Science. 2nd edition. Hoboken, NJ: For Dummies; 2017.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781