CourseSpecifications
Valid as from the academic year 2024-2025

Big Data Science (C003802)

Course size
Credits 5.0(nominal values; actual values may depend on programme)
Study time 150 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
Peralta Cámara, Daniel
Goetghebeur, Els

lectureindependent work
seminar

WE02
WE02

lecturer-in-charge
co-lecturer

Offered in the following programmes in 2024-2025
Master of Science in Statistical Data Analysis

crdts5offeringA

Teaching languages
EnglishKeywords
Data visualisation, Data mining, Machine learning, Processing big data, Statistical learning.
Position of the course
This course offers a broad perspective on big data science and its role within academia andindustry. It will focus in diverse aspects of data science, such as• exploration: data visualisation and data mining;• modelling: focused analysis of big data;• computing: data capture, adaptation, storage and processing.
Contents• The role of the data scientist.
• Epistemology of data science.
• Identifying data sources and biases.
• Data acquisition processes and data preparation (standardisation).
• Relations between bias, variance and model complexity.
• Predictive data mining techniques: penalisation methods, classification methods (e.g. support• vector machines), bagging, boosting, random forests• Cluster analysis, Principal Component Analysis, Multidimensional Scaling.
• Missing data
• Data translation and conversion tools.
• IT paradigms for parallelizing data analysis: MapReduce, in-memory approaches such as• Spark.
• Data visualisation techniques: visual analytics.
• Processing unstructured text and graph data.
• Neural networks and deep learning
Initial competences
Basic data analysis skills and concepts (such as offered in the courses Principles of Statistical
Data Analysis, and Statistical Modelling) and programming skills (such as offered in thecourses Statistical Computing, and, Programming and Algorithms), preferably in Python.
Final competences
1 Have knowledge of methods and concepts for the processing of big data sets.
2 Query and process internal and external data sources that contain raw information, such as
1 non-standardised data, unstructured text, ...
(Approved)

13 Visualise big datasets in an accessible manner that provides insight into the research
1 question.
4 Use state-of-the-art data mining algorithms (e.g. classification, regression, dimensionality
1 reduction...) to explore big datasets.
5 Understand how big data analyses may be subject to bias.
6 Express the uncertainty of big data analyses.
7 Collaborate with colleagues.
8 Adequately report the results from a big data analysis.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture, Independent work
Study material
Type: Slides
Name: Big data science
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Additional information: The slides and any additional material for the course will be made available via Ufora. Most ofthe recommended books are freely available online. For the practical part of the course, it is recommended to bringyour own laptop, but the heavy processing can be done on the UGent HPC infrastructure.
References
O'Neil. C., and Schutt. R. (2013) Doing Data Science: Straight Talk from the Frontline. O’Reilly.
Trochim. W. (2006) The Research Methods Knowledge Base. Cenage Learning
Hastie. T., Tibshirani. R., and Friedman J. (2009) The Elements of Statistical Learning: Data
Mining, Inference, and Prediction. Springer
Gareth. J., Witten. D., Hastie. T., and Tibshirani. R. (2013) An Introduction to Statistical
Learning with Applications in R. Springer.
Course content-related study coaching
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Oral assessment, Assignment
Examination methods in case of periodic assessment during the second examination period
Oral assessment, Assignment
Examination methods in case of permanent assessment
Oral assessment, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Calculation of the examination mark50% on group work and 50% on end-of-term exam.

(Approved)2