Teaching Portfolio

Undergraduate Teaching

My approach to undergraduate teaching is outlined in my Teaching Philosophy.

Introduction to Business Statistics (S186)

S186 was the very first module I lectured.  It is an introductory business statistics module presented by the department of Statistics and Actuarial Science at SU to the first year students of the Economic and Management Sciences (EMS) faculty. I was a lecturer to both English and Afrikaans groups of varying sizes, but was not responsible for setting tests, tutorials or class notes. I did, however, prepare my own lecture notes which students found helpful. 

SciMathUS

An introductory statistics module presented by the department of Statistics and Actuarial Science at SU as a bridging course for previously disadvantaged students with the potential of enrolling at universities. I was the course coordinator in 2010 which included the preparation of course material, tutorials and tests.  It was a privilege to lecture this wonderful group of students that had so much potential.  One of the students joined my S186 class the following year and performed exceptionally well while one of the other students moved to UWC.   

Introduction to Statistics (BUS132)

BUS132 is an introduction to statistics course offered to first year undergraduate students in the Economic and Management Sciences (EMS) faculty (↪ view).  I was a lecturer in the second semester of 2012.  In this course students are taught to understand and analyse data and to translate their findings into usable information that can be used to make informed decisions.  At the end of the course students are expected to understand the importance of statistics in both public and private sectors. This was my first time teaching a course at UWC, and it is always challenging to deliver a service course to an audience that are prescribed to take it, but don't necessarily have any interest in it. The students, however, communicated through the course evaluation that they thought I did a good job (↪ view).      

Distribution Theory (STA211)

In 2013 and 2014 I was the lecturer and course coordinator of STA211, a second year undergraduate module presented to students in the Science and EMS faculties (↪ view).  In this course students are taught the fundamentals of probability, random variables and probability distributions. At the end of the course students are expected to understand how these concepts relate to statistical inference and research.  A further objective of this course is to teach students how to analyse data using the Statistical Analysis System (SAS) and Excel.  This part is overseen by a practical demonstrator.

In 2013, 95% of the students passed. The students gave the course an overall 4.49 (out of 5) and they gave me 4.58. Some comments by the students:

• The format of Stats 211 could be a solution to Maths 105.  In terms of setup

  • No of Tests

  • Tut tests

  • 2 semesters (semester module)

• Lecturing is TOTALLY SATISFACTORY.

• MAY GOD BLESS HER AND HER FAMILY, ALL THE TIME.

• R Luus is the boss!  Makes every lecture fun and interesting.

• Really enjoyed the course.  Thanks Mam!

• This was good.  And she can teach for sure.  Regards students.

• In my opinion Mrs Luus is the best lecturer I have had at UWC.

• She is amazing wishes to continue with her for next semester.

• One of the best lecturers ever.  Thank you Mrs Luus.

This was my first time lecturing this course, so in the lecturer's report I acknowledge that I would increase the level somewhat in the following year. In 2014, the pass rate was 82%. Regarding the course evaluation, the majority of the students gave an excellent score to “Clear presentation of subject matter”, “Ability to clarify course material”, “Overall quality of lectures” and “Overall quality of the course”. Even though the pass rate was lower, the students were still happy:

• This module is excellent.  Mrs Luus is excellent.  The Stats department is excellent.

• Mrs Luus is one of the best lecturers I have ever encountered.

• Stats department needs lecturers like this one! Keep it up! Good job.  She knows her work!

• Good course.  I’ve grown to really enjoy this module, in fact it’s my favorite module and I’m planning to major in Stats and further pursue a career.

• The course is structured, the lecturer really prepares for the lectures making it easy for us students to grasp the work and understand properly.

• I like the lecturer.

The lecturer reports and course evaluations are available here.

Inference and Regression (STA221)

STA221 presents inference and regression to second year undergraduate students in the Science and EMS faculties.  The objective of this module is to introduce statistical inference to the students and to further their knowledge of regression modelling.  Students are expected to confidently engage with the concept of inference and to understand how to carry out the correct inference practice in different scenarios.  They are also expected to understand the fundamentals of regression analysis, why and when one would use regression modelling and how one would interpret the output from such a model.  In this module the practical application of statistics using SAS is continued (following from STA211) and students are given a project to complete by the end of the semester.  Students that achieve an overall average of at least 70% for the computer practical part are awarded with the SAS certification of completion based on SAS programming 1 and 2.  The practical demonstrator oversees this part of the module.  In 2013 and 2014 I was the 3rd term lecturer of this module (↪ view) and from 2016 - 2019 I was the lecturer and coordinator of the module (↪ view). 

Complete lecturer reports and course evaluations are available to view here.

Linear Regression (S318)

This is a first semester applied statistics module presented by the department of Statistics and Actuarial Science at SU to third year students that have successfully completed either second year mathematical statistics or second year applied statistics. I lectured half of the course and the topics covered in my part of the module are:  Multivariate regression analysis; Correlation analysis; Diagnostic measures; and Prediction and model selection.  Also part of this module was a practical component where theory was applied using software such as Excel, Statistica, SAS and R. I presented this course once when the usual lecturer was on study leave. My responsibilities included setting tests and formulating practical assignments.  The late Dr Johan van Vuuren prepared the course material that I presented.  It was a privilege to have been one of his students.  He was an exceptional talent in the area of regression modelling.  

Time Series Analysis and Sampling Theory (S348)

A second semester applied statistics module presented by the department of Statistics and Actuarial Science at SU to third year students that have successfully completed either second year mathematical statistics or second year applied statistics. I lectured half of the course and the topics covered in my part of the module are: Time series analysis; andSampling theory. I was responsible for developing the course material for these topics. Furthermore, this module has a practical component where theory is applied using software such as Excel, Statistica, SAS and R. My responsibilities also included the setting of tests and preparation of practical assignments. 

Biostatistics (S322)

An introductory statistics module presented by the department of Statistics and Actuarial Science at SU to the third year dietetics students of the Health Sciences faculty at the Tygerberg campus. This module focused on the application of statistical methodology in a medical/bio-statistical environment. Furthermore the students were required to do analyses in Excel. For this module I was required to be the course coordinator which included the tasks of preparing additional course material, class tutorials, computer assignments as well as tests. 

Postgraduate Teaching

Contemporary Issues in Business Analytics (STA842)

In 2016, the department of Statistics and Population Studies (SPS) was approached to start a SAS Data Science center at UWC.  The Centre for Business Mathematics and Informatics (BMI) at North-West University (NWU) has successfully been running a similar programme for more than 20 years.  After initial discussions between the SPS department, SAS and the Centre for BMI, the Centre for BMI decided to collaborate with the SPS department to ensure the successful launch of our very own Master's in Data Science programme (↪ view).  From 2018 - 2021 the UWC lecturing team acted as module facilitators while they were being up-skilled on how to offer courses at this level that were industry-focused. I was the facilitator for the Contemporary Issues in Business Analytics module.  This is a module presented to students enrolled in the Master's in Data Science program offered by the SPS department.  Module description:  

• Calculate marketing metrics, such as traditional customer metrics, customer acquisition metrics, customer activity metrics, value metrics, etc. 

• Conduct recency, frequency, monetary-value (RFM) segmentation. 

• Understand customer lifetime value (CLV), such as past customer value, formulate CLV, understand and apply retention and migration CLV models, etc. 

• Analytical customer relationship modelling (CRM) techniques to manage customers, such as customer acquisition and costs, customer retention – cross selling and up-selling, balancing acquisition and retention, customer churn prediction and reduction to churn. 

• Extensions of the RFM model. 

• Estimate revenue streams. 

The BMI lecturers video recorded their weekly lectures and the facilitator then watched these videos and worked through the lecture notes before watching the video again with the students.  As module facilitator I was also expected to mark certain assessments and to finalise the UWC students' marks at the end of the semester.  

In 2022 the UWC lecturing team started co-teaching the modules with the BMI lecturers. At this time I started teaching half of the topics of STA842 (BWIB822 at NWU) and also prepared the assignments for the seminars I presented. 

In 2023 the lecturing and coordination of the module became my full responsibility and I also started training/mentoring a new staff member at BMI. A hybrid teaching approach was followed so that I could, at the same time, present the module to both UWC and NWU BMI students. During this time, I took care of the preparation of all course material, assignments and assessments. In 2025, I continued to lecture the theory part of the course while the BMI lecturer started offering practical demonstrations (annual course schedules are in separate sheets ↪ view). 

Of course this is a group of advanced analytics students so a big proportion of the time is spent on enlightening them on the application of statistical methods in CRM. The students are expected to apply the taught knowledge in SAS through the completion of assignments that cover all phases of CRM, i.e. acquisition, retention, churn and win-back. Another part of this course is to shed light on the marketing side of CRM. Given the background of the group they often find themselves confused and or overwhelmed by the content. I thus make a point of connecting the dots for them. An example of the end of semester overview can be perused here.

The masters programme is evaluated as a whole, I thus do not have any course evaluation feedback to present. Since the first cohort in 2018, the annual pass rate has remained at 100%.

Statistical Modelling (STA737)

In 2017 the Statistics and Population Studies department started offering a BSc Honours degree with specialisation in Data Science (↪ view).  At this time the specialisation area consisted of three modules of which two are presented by the SPS department and the third by Computer Science.  The Statistical Modelling module, STA737, is one of these modules and the first cohort of students took it in 2017.  At this point I was one of 3 lecturers among which the course content was divided (↪ view).  In 2018, after the start of the semester, I took over as course coordinator and I also acted as the practical demonstrator of the module (↪ view).  This course covers a broad scope of statistical models, linear and otherwise, and follows the well-known book Introduction to Statistical Learning by James et al.  The course outline of the last year I was involved with this module, can be viewed here. Some of the topics covered:

• Linear regression

• Classification (Logistic Regression; K-Nearest Neighbors; Tree-based methods; etc.)

• Non-linear regression (Polynomial regression; Generalized Additive Models; etc.)

• Model selection (Subset selection; Regularization methods; Principal Component Regression; etc.)

• Model evaluation (Cross-validation; Bootstrap)

The theoretical fundamentals were discussed in depth with the students after which they proceeded to apply the theory to real data in the practical sessions.  A significant contribution of this module was the student exposure to the statistical software RStudio.  For an example of an R lecture, ↪ view.

In 2021 the practical was presented as a fully online course due to the Covid pandemic. Each week students received a voice recorded PowerPoint presentation which included links to instructional videos. They were expected to work through this on their own and then complete an assignment based on the week's content. The following week I would have a Google Meet catch-up session where I'd work through the assignment solution with them, sharing my screen so that they can follow what I'm scripting in RStudio. These demonstrations were also recorded and shared with the class. The outcome was, thankfully, a successful completion of the practical with a 95% pass rate.  

From 2022 onwards the practical demonstration continued online, but recorded lectures were replaced by live online lectures via Google Meet. Students joined these lectures from the Honours lab on campus and could interact with the lecturer easily thanks to technological advances. Practical lectures were still recorded and shared with the students, enabling flexible learning.

My contribution to this course was directed towards turning the students into confident predictive modellers in RStudio. The practical component was evaluated separately, and a summary of the responses are shown below.

Time Series Analysis (COF711)

Curriculum alignment is a continuous process in our department.  After the first cohort of MSc Statistics (with Data Science specialisation) students graduated, the Data Science lecturing team reflected on the MSc curriculum that was taught and industry projects that were completed.  It was decided to revive the Honours level Time Series Analysis (COF711) module given the wide application of this methodology to problems in the financial and retail industries.  In the second semester of 2019 I became the lecturer and course coordinator of the module (↪ view) and I was asked to redevelop the course.  In a nutshell, time series forecasting using exponential smoothing methods and ARIMA models formed the basis of this module.  Students were coached to solve time series problems using the R opensource statistical software as well as SAS.  For an example of a lecture, ↪ view.

Introductory Multivariate Analysis (STA739)

In the first semester of 2022 I became the coordinator and co-lecturer of the Matrix Methods (STA734) module (↪ view). The first term of this module was devoted to making sure the students' knowledge of matrix algebra and its application in multivariate statistics, is at the correct level. This included:

• matrix operations;

• orthogonal vectors and matrices;

• eigenvalues and eigenvectors; and

• principal component analysis.

In the second term of the module the students were introduced to multivariate statistics, starting with the characterisation and display of such datasets. Topics included in this term:

• univariate, bivariate and multivariate descriptive statistics;

• univariate, bivariate and multivariate graphical displays;

• linear combinations of variables;

• two-group discriminant analysis; and

• multi-group discriminant analysis.

Students had to know what discriminant analysis is used for, how to interpret discriminant functions, how to test for significant discriminant functions and how to perform stepwise discriminant analysis.

Throughout the module attention was paid to the students' ability to script solutions to problems using either SAS or RStudio. For an example of a lecture, ↪ view

In 2023 this module was renamed Introductory Multivariate Analysis (STA739) to emphasise the continuation of the students' multivariate analysis journey in the second semester with STA701 (Multivariate Analysis). At this time I became the coordinator and sole lecturer of the course. It was my task to determine the curriculum for this module, making sure it connects well with STA701, whose curriculum I also had to develop. As most statistical analyses form part of multivariate analysis and STA739 together with STA701 form our students' multivariate analysis journey, an assessment of the topics covered by other postgraduate courses was done to identify any important techniques not yet taught to our students. This content evaluation led to the following topics being covered in STA739 in the first semester (↪ view):

1. Multivariate Analysis of Numerical Data

2. Multivariate Analysis of Categorical Data

3. Introduction to Survival Analysis

The process of selecting these topics, and then developing the course material, is discussed here. 

Theoretical content were interleaved with application in RStudio. In 2025 students were required to use RMarkdown to compile their assignment reports. Information on the assessment of the module is available here. 

Multivariate Analysis (STA701)

This second semester Multivariate Analysis module follows on from the Introductory Multivariate Analysis (STA739). All course outlines can be viewed here. The following topics are covered in this course:

• Overview of matrix algebra required for multivariate analysis

• Principal component analysis (PCA)

• Principal component regression (PCR)

• Discriminant analysis (descriptive/exploratory)

• Classification analysis (linear and quadratic discriminant analysis)

• Hierarchical cluster analysis

• K-means cluster analysis

In addition to being the course coordinator, I also had to curate the content for this module. In terms of teaching, I co-teach the module and my responsibility is to demonstrate how these topics are applied in RStudio. More importantly, I ensure that the students understand how to interpret the results within the context of the problem. For this practical component, I authored manuals that I share with the students (↪ view).

During practical demonstrations the students are given opportunity to practice the R application (see Hands-on Practice for an example). I share how I assess this part of STA701 on the Assessment page.

Survey Methodology (POP706)

Most of the research I have conducted is in the area of survey sampling.  After joining the SPS department in 2012 I've been asked a few times (2012 - 2014 and 2016) to present "Sampling in Practice" over two sessions to postgraduate students in the Honours in Statistics and Honours in Population Studies programmes (↪ view).  After these lectures the students are expected to understand the importance of sampling in practice, the difference between good and bad sampling practice, the different sampling methods, how to determine the size of a sample and how to correctly conduct statistical analyses using survey samples.  I also showed the students how to carry out the different techniques in Excel and SAS.    

Statistics and Visualisation (BIA713)

In 2017, developed for the EMS Faculty's Diploma in Software and Media with specialisation in Data Analytics and Business Intelligence, the department started offering BIA713, Statistics and Visualization (↪ view).  I was the coordinator of this module in 2017 and 2018 and also lectured 75% of the module content. I developed all course material for the part that I lectured, which included interleaving computer application with theory content. The student cohort typically consisted of a mixed set of professionals and students with varying levels of Statistical knowledge and computer skills.  At that time the module was presented over four lectures of 6 hours each. These were presented over two weeks, with one week early in the semester and another later in the semester.  

The module content covers introductory Statistics, i.e. from understanding different data types to visualising the data to gain insights and to use the data to conduct statistical inference and basic regression.  For an example of my lecture notes, ↪ view.  Also part of the skills transferred in this module is improving the students' basic Excel skills, showing them how to utilise the Statistical tools in Excel, and then finally introducing them to the R software for statistical analysis.  For this purpose I put together a manual with clear instructions on how to carry out the various statistical techniques in Excel and/or R (↪ view). 

During my time as lecturer and coordinator, the pass rates were 97% (in 2017) and 85% (in 2018). No course evaluation was done by EMS in 2017, but the students received an online evaluation in 2018, for the whole diploma programme, and the following comments pertained to BIA713:

• BIA713 was one of the best structured modules and both lectures, Retha Luus and Ronel Jacobs (should be Rechelle Jacobs). The Stats department really made learning fun and interesting.

• The lecturers always responded in time with questions via email.

• Lecturer explained the course very well, was available for consultations 

• This was the best well presented module.  

• The level of presentation was the best. Retha's attitude is great which make it easy to  approach her. Retha is willing to help

• You're one of the best. You demonstrate a very high understanding of what you teach and you went the extra mile to help us understand the module. You instilled the basics necessary. Well done, you did well considering that I never did stats ever but to have managed to complete all my assignments and pass it demonstrates how good you are at what you do.

• Dr Luus was very professional and helpful. Thank you for sharing your knowledge with us. I'm a better analyst than i was before attending this course

• The installation of R was done well in time, and this was the perfect mix between theory and practical.  Extra days would have been even more beneficial especially with R and other practicals.  the operation of R was done step by step and all the students learned from it, and there was never a comment of "this is post grad level".

Conclusion

This page summarised all modules that I have coordinated and lectured. Over the 15 years students have rated me overall as an exceptional lecturer. The sustained excellent annual pass rates and gleaming course evaluations attest to this. I am consistently scored as a very organised, competent, and approachable lecturer and they feel that the courses I teach and skills I instill are important to their analytics career development. I focus on introducing innovative technologies that apply practically to the development of my students’ analytical careers, and many student comments over the years demonstrate that they agree with this. This should cement that I am an active undergraduate and/or postgraduate teacher that always goes above and beyond to ensure that every student that passes by my whiteboard or screen, is respected, treated professionally and takes with them a little piece of the knowledge I hold in Statistics.  

I have developed the curricula of four postgraduate courses and have extensively renewed curricula for two postgraduate courses. In addition to larger scale development and renewal, I use my annual research and supervision to inform continuous smaller enhancements. Evidence of this is presented here.