CourseSpecifications
Valid as from the academic year 2024-2025

Data Science (I002440)

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)
Dutch
Gent

Lecturers in academic year 2024-2025
Meys, Joris
Verwaeren, Jangroup workseminar
lecture

LA26
LA26

Offered in the following programmes in 2024-2025
Bachelor of Science in Bioscience Engineering
Master of Science in Bioinformatics(main subject Bioscience Engineering)

staff member
lecturer-in-charge
crdts
55offeringAA

Teaching languages
Dutch
Keywords
Data wrangling, data exploration, data visualization, R, explorative high-dimensional data
analysis
Position of the course
This course is an introductory course into data science. In this course, students are introduced
into the world of data analysis, both from a theoretical and an applied perspective (over two
thirds of this course consists of hands-on data analysis with R). Special attention is given to
the fact that, in a modern-day data analysis project, data can emerge in multiple forms andformats ranging from small structured flat-file datasets, that collect observations of a small
dedicated research project to online sources that are less structured and potentially containless reliable data, missing values, etc. In this course, the insight and the tools are introduced
that are needed to extract information from these data sources in a reliable, reproducible andtransparent way. In a first, more descriptive part of this course, the students are introduced to
the life-cycle of data in a scientific setting, the composition and exploration of low-dimensional
data tables with special attention to data visualization, data cleaning and data wrangling. In asecond, more technical/mathematical part of this course, the focus is oriented to the theory andapplication of resampling methods, methods for exploring high-dimensional datasets and (anintroduction to) predictive modeling. In a final (smaller) part, this use of relational databases fordata science is introduced.
Contents
This course is subdivided into five parts. Each part involves both theory and practical skills (PClabs in R).
Part 1: Introduction to data science and R• Introduction to data and the data analysis cycle• Introduction to R (important data types for data analysis, control flow, built-in and user• defined functions, vectorization, data intake)Part 2: Univariate and bivariate explorative data analysis• Data tables: scales of measurement, data collection principles and terminology, data storage• and data access.
• Univariate explorative analysis: frequency distributions, sample percentiles, measures of• central tendency and measures of scale, standardization, resampling methods (resampling• from a population and bootstrapping), missing values(Approved)1• Bivariate explorative analysis: bivariate frequency distributions, simple linear regression, R²,• local linear regression and scatterplot smoothing, Spearman correlation, Pearson correlation,• Cramér’s V, bivariate outliersPart 3: Data manipulation and visualization• Principles of reproducible and transparent data analysis and communication• Data manipulation (for data-preparation): selection, filtering, aggregation, join-operations,• wide and long formats, tidy data. In the PC-labs, the implementation of data-wrangling in• tidyverse is used.
• Data visualization: properties of good data-graphics, the layered grammar of graphics. In the• PC-labs, the implementation of the grammar of graphics in ggplot2 is used.
Part 4: Model-based and multivariate data analysis• Curve fitting: loss functions, Newton’s method and gradient descent, the algorithms of• Gauss-Newton and Levenberg-Marquardt• Dimensionality reduction methods: principal component analysis and multidimensional• scaling• Introduction to predictive modelling: k nearest neighbors and performance evaluation, linear• methods for regression and classification
Part 5: Relational databases and using personal data
A brief introduction to relational databases and SQL is given (with a focus on applications indata science) and privacy-issues with respect to the use of personal data are discussed.
Initial competences
Data science builds on certain learning outcomes of course units ‘Calculus, ‘Linear algebra',and ‘Scientific computing’; or the learning outcomes have been achieved differently.
Final competences
1 The student is aware of the different forms in which data appear, are capable of performing
1 basic integrity checks for the most important data forms/types and can select and apply a
1 proper visualization method.
2 The student can use R as a programming environment for data analysis.
3 The student performs data loading tasks for data that are available in a variety of text-based
1 data formats, merges these data and transform it into a shape that allows further processing.
4 The student performs an explorative univariate and bivariate analysis.
5 The student can compose and format summarizing figures and tables that meet the quality
1 standards required for scientific communication.
6 The student selects a useful loss function and applies an optimization alogirthm for solving a
1 curve fitting problem.
7 The student applies dimesionality reduction techniques to gain insight into datasets.
8 The student selects a predictive modeling method for solving a prediction problem.
9 The student queries a simple relational database.
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
Group work, Seminar, Lecture
Extra information on the teaching methods
During the theoretical lectures, the fundamental concepts are discussed. The practical PC
room classes exist of 10 hands-on practical sessions. In the group work, the students have tocomplete a real-life data collection and synthesis task.
Study material
Type: Syllabus
Name: Course notes data science
Indicative price: Free or paid by faculty
Optional: noLanguage : Dutch
Number of Pages : 300
Oldest Usable Edition : 2024
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No(Approved)

2Available through Student Association : NoReferences
Benjamin S. Baumer, Daniel T. Kaplan and Nicholas J. Horton. (2017) Modern Data Sciencewith R. CRC Press, 578p.
John L. Faundeen, Thomas E. Burley, et al. (2013) The United States Geological Survey
Science Data Lifecycle Model, USGS Open-File Report 2013-1265.
Course content-related study coachingStudents can make an appointment with the lecturer for asking questions related to thetheoretical classes throughout the entire semester. Teaching assistants address questions w.r.
t. the PC-labs and Ufora is used to provide on-line feedback if needed.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment with open-ended questions
Examination methods in case of periodic assessment during the second examination period
Written assessment with open-ended questions
Examination methods in case of permanent assessment
Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Extra information on the examination methods
The written exam (25% of total) evaluates the theoretical competences. Practical competences
are evaluated during an open book PC-exam (65% of total). The written report (report of groupwork) is evaluated and contributes to the final grade (10% of total).
Students are allowed to rework the group assignment form NPE for the second examination
period. The reworked version is re-evaluated.
Calculation of the examination markThe final grade is the result of: a written exam (25%), an open-book PC-exam (65%) and awritten report (10%)(Approved)3