Training

I give data trainings to graduate and undergraduate students enlisted in the Information Science program of the Geneva School of Business Administration at the University of Applied Sciences and Arts Western Switzerland (HES-SO). Below you will find a list of my courses, descriptions and course evaluations.

MASTERS LEVEL COURSES

• M6bC2: Data Curation and visualisation [2022, 2020]
  Data visualization tools are indispensable in today’s information-dense world, enabling us to analyze, comprehend vast datasets, and support decision-making processes. This course harnesses the power of visual cognition, teaching participants to craft graphical representations that not only highlight data trends but also captivate and communicate effectively to audiences. Participants will master the preparation of raw data for precise visualization—filtering, aggregating, linking, and merging data using R and dplyr. They will learn to critically assess the impact of a visualization in conveying messages, select the most suitable visualization support based on data types and intended outcomes, and apply strategic visual methods to articulate their narratives. Furthermore, the course introduces the creation of compelling visualizations using ggplot2, ensuring participants can translate complex information into impactful, clear visual stories.
  Keywords: Data visualisation principles, Pre-attentive attributes, RStudio, R, Tidyr, Dplyr, GGplot2.

• M7aC1: Telling stories with data [2023, 2021]
  Effective communication through data is essential across all types and sizes of organizations. While often distilled to the simple concept of data visualization, true data storytelling is much more profound. It represents a structured approach designed to convey vital information derived from data, integrating the data itself, its visual representation, and a compelling narrative to provide context. This triad forms the basis of data storytelling, transforming raw data into actionable insights that can drive real-world decisions. Building on the data visualization skills acquired in a previous course (M6bC2: Data Curation and Visualisation), participants will learn the art and science of data storytelling, focusing on the symbiotic relationship between these three key elements and their implementation thourg the creation of interactive dashboards using Quarto in an RStudio environment. This course empowers participants to transform data into compelling narratives that not only capture attention but also drive strategic decisions and prompt tangible actions.
  Keywords: Storytelling, Quarto, RStudio, Interactive dashboards, R, Shiny, GGiraph, GGanimate, GGtext.

• M5C2: Strategic data management [2022, 2020]
  In today’s world, massive amounts of data are generated across all sectors, necessitating a new set of skills and a mindset shift to turn this data into tangible outcomes. This course offers a hands-on approach to demystifying and leveraging the data science ecosystem within organizations. Upon completion, participants will be equipped to establish a data-driven framework in their organization, comprehend terminology, develop and test hypotheses, interpret results, and make informed decisions based on data. Course objectives include understanding the necessary skills for a data team and how to structure it to meet an organization’s needs, identifying actionable data sources, recognizing business challenges solvable through data science, and formulating actionable recommendations based on statistical results. Additionally, participants will learn to design, plan, and analyze successful A/B tests, as well as grasp the opportunities and understand the challenges associated with implementing machine learning models in organizations.
  Keywords: Data science workflow, A/B A/A testing, R, RStudio, Power analysis, Clustering: K-means and hierarchical clustering, Classification: logistic regression.

BACHELORS LEVEL COURSES

• 7C2-CT-1: Applied statistics [2022, 2021]
  Statistics, often perceived as daunting due to its complex terminologies and perplexing equations, actually revolves around a few exciting key concepts. This course focuses on demystifying statistics through the General Linear Model (GLM), a framework that simplifies to a singular equation for predicting outcomes based on models and error prediction, while also exploring its expansion to better fit diverse data sets. Utilizing the R programming language within the RStudio environment, the course builds on a foundational understanding of statistical concepts like variable types, central tendency measures, distributions, and hypothesis testing. By its conclusion, participants will not only grasp the theoretical underpinnings of GLM but will also be proficient in fitting GLM to quantitative data, estimating variable effects, testing hypotheses, and navigating the RStudio environment, thereby unlocking the captivating world of statistics applied to research analysis.
  Keywords: R, RStudio, General linear model, Robust statistics.

• 7M3-SI3-3: Data visualisation [2023, 2022, 2021, 2020]
  Data visualization tools are essential for analyzing large volumes of information, enhancing understanding, and supporting decision-making. This course emphasizes the human capacity for visual interpretation, teaching how to effectively use graphical representations to identify data trends and highlight key messages. Participants will learn to prepare raw data for accurate visualization through filtering, aggregating, linking, and/or merging processes. They will evaluate the effectiveness of visualizations in conveying messages, choose suitable visualizations based on the type and objectives of the data, and apply strategic visual techniques to communicate essential information. The course also covers mastery of data preparation and visualization tools, specifically Tableau Prep and Tableau Desktop, equipping participants with the skills to transform complex data into insightful and compelling visual narratives.
  Keywords: Tableau Prep, Tableau Desktop, Pre-attentive attributes, Data visualisation principles.

EVALUATIONS