Content Of The eCourse Big Data

Index

The Content Of The Big Data eCourse On The Laudius Platform

The Big Data eCourse is offered in Dutch on the Laudius platform. It consists of seven lessons plus an optional final exam. Each of the seven lessons follows the same structure:

• Introduction: A brief overview of the topic covered in the lesson.

• Learning goals: The key takeaways that students should remember from the lesson.

• Tips: Additional insights and background information related to the lesson’s subject.

• Homework: Questions based on the theory, and in some lessons, practical cases where Big Data analysis can be applied.

Homework

Homework can be reviewed either by me or by the other instructor if the correct homework option is selected. In that case, the assignments are graded on a scale from 1 to 10. Afterward, Laudius can issue a certificate, provided the average grade is 5.5 or higher.

Exam Big Data

After completing the Big Data eCourse, students can choose to take an optional exam. This is an open‑book exam that includes multiple‑choice questions as well as case-based assignments. If the final grade is 5.5 or higher, the student receives a Laudius diploma.

Word List

The word list contains all Big Data–related terms that appear across the seven lessons of the course.

Real Stat Excel Add‑On

Real Statistics is a free Excel add‑on that students can download to perform all the Big Data analyses required in this eCourse. Once installed, it integrates directly into Excel. The analyses can also be carried out using SPSS, R, or Python. If a student chooses to use a program other than Excel, it is recommended to mention this in the homework assignment.

Forum

The forum provides a space where students can find additional information about Big Data and discuss topics with each other. It’s a helpful place to exchange ideas, ask questions, and learn from fellow students. It is recommended to visit this Forum regularly.

Book 'Succes Met Big Data'

All lessons are based on the book 'Succes met Big Data', which is included in the course price. Each lesson uses several chapters from the book, supplemented with additional information about Big Data to deepen the student’s understanding.

Lesson 1 Finding A Way In The Big Data eCourse

Lesson 1 helps the student get familiar with the Big Data eCourse and understand how everything works throughout the program. It includes several PDFs that explain:

• Using the platform: Guidance on navigating the Laudius environment.

• Submitting homework and cases: Instructions for sending in assignments.

• Emailing the teachers: How to contact the instructors.

• Using the forum: Tips for participating in discussions.

• Finding lesson materials: Directions on locating all course content.

There are also two introduction videos featuring the teachers and I am one of them.

If the student chooses the option to submit homework, Lesson 1 includes a very simple assignment: the student only needs to send a message stating “I have read everything and have no questions” or ask (a) question(s) if something is unclear.

Lesson 2 Big Data

In Lesson 2, students are advised to read Chapter 1 (Inleiding) and Chapter 2 (Big Data) from the book 'Succes met Big Data'. In this lesson, the student learns what Big Data is, how it works, and who uses it. The homework for this lesson consists of theory‑based questions.

Lesson 3 Storage & Processes

In Lesson 3, students are advised to read Chapter 3 (Opslag) and Chapter 2 (Proces) from the book Succes met Big Data. In this lesson, the student learns about data, information, storage, and predictive analyses.

Lesson 4 Decision Tree

In Lesson 4, students are advised to read Chapter 5 (Beslisboom) and Chapter 6 (Neuraal netwerk) from the book 'Succes met Big Data'. In this lesson, the student learns how to create a decision tree and gains an introduction to neural networks. A bonus video is included that demonstrates how to build a decision tree in Excel. Students can also use CANVAS to create decision trees. The homework for this lesson consists of theory‑based questions and a practical case in which the student creates four different decision trees.

Lesson 5 Cluster Analyses & Regression Analyses

In Lesson 5, students are advised to read Chapter 7 (Clusteren) and Chapter 8 (Lineaire regressie) from the book 'Succes met Big Data'. In this lesson, the student learns how to perform cluster analysis and linear regression.

This lesson includes several bonus videos and PDFs that provide additional explanations:

• Cluster analysis video: A walkthrough of how clustering works.

• Installing Real Stat: A video showing how to install the Real Statistics add‑on.

• Measuring and research: Video explanation of basic research concepts.

• Dispersion and central measures: Video covering statistical spread and averages.

• Calculations in Excel: Video demonstrating useful Excel functions.

• Multiple regression: Video explaining how multiple regression works.

• VBA Run Time Error 424: PDF describing how to resolve this error.

• Real Stat issues in Excel 2019/365: PDF with troubleshooting tips.

• Regression analysis explanation: PDF with additional theory.

• Dispersion and central measures PDF: Written explanation of spread and averages.

• Measuring and research PDF, Additional background information.

The homework for this lesson includes theory‑based questions and two practical cases in which the student performs:

• K‑means cluster analysis

• Multiple linear regression

For both assignments, datasets are provided for download and must be used to complete the analyses.

Lesson 6 Nearest Neighbor Analysis & Rules Derivation

In Lesson 6, students are advised to read Chapter 9 (Naaste buur) and Chapter 10 (Regels afleiden) from the book 'Succes met Big Data'. In this lesson, the student learns how Nearest Neighbor analysis works and how rules derivation is applied in practice. A bonus video is included that demonstrates how to perform a Nearest Neighbor analysis in Excel. The homework for this lesson consists of theory‑based questions and a practical case in which the student carries out a complete Nearest Neighbor analysis using the provided dataset.

Lesson 7 Sense & Nonsense Of Big Data, Ethics In Big Data

In Lesson 7, students are advised to read Chapter 11 (Zin en Onzin Big Data), Chapter 12 (Ethiek Big Data), and Chapter 13 (Uitleiding) from the book Succes met Big Data. In this final lesson, the student learns about the sense and nonsense of Big Data and explores ethical considerations related to data use, privacy, and responsible analysis. The homework for this lesson consists of theory‑based questions that encourage students to reflect on the ethical implications and practical limitations of Big Data.

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