And of data - Study guides, Class notes & Summaries

The exam is divided into three parts: - The general part; how to write a scientific report - Quantitative data; - Qualitative data; coding The summary is also divided into these parts. I also have a practice test available, which corresponds with the summary. It consists of 95 open-ended questions. Example questions: 9) Explain the Pendleton method 20) What are common mistakes of the discussion and conclusion section? 32) How to identify problematic scale items? 67) Explain the d...

This document contains all reading material and lectures that were discussed during the lectures of Business Intelligence & Business Analytics in the spring of 2023. Note that the last Python exercises are not added in this summary! Full list of material: - Database systems: design, implementation, and managementLinks to an external site. (9 edition), P. Rob and C. Coronell. (1.2, 1.7; 2.1, 2.2, 2.3; 3.1, 3.2, 3.6; 6.1, 6.2.) - Data Warehouse Design: Modern Principles and Methodologies ...

These notes were compiled using previous examination papers: DBE/November 2022, SACAI NOV 2021 & DBE/November 2022. (For the best results also purchase BS Paper 2 Prelim Notes. Section A: Short questions Section B: Business Opportunities/ Ventures 1. Management and leadership 2. Investment: Securities/Opportunities & insurance 3. Forms of ownership: Criteria for success and/or failure 4. Presentation and data responses Section C: Business Roles 1. Ethics and professionalism 2. Cre...

PART 1 of Practical 5: Advanced Data Analysis: full summary + explanations, screenshots and answers to questions

Summary of the following literature: 1. Edwards, M., & Edwards, K. (2019). Predictive HR Analytics: Mastering the HR Metrics (2nd ed). London: Kogan Page. Chapters 1, 2, 3, 4 & 12 (use other chapters to refresh your statistical knowledge and see applications of statistical tools on real HR problems) 2. Khan, N., & Millner, D. (2020). Introduction to People Analytics: A practical guide to Data-Driven HR. London: Kogan Page. Chapters 1, 2, 5, 6, 7 & 10 3. Cascio, W. F., Boudreau, J. W., ...

Are you struggling to keep up with the Data Science course? Do you find it challenging to stay on top of all the lecture material and required readings and does it take you an incredible amount of time the summaries all chapters from the book? Look no further than this complete and detailed summary! This comprehensive document includes all lecture slides and a detailed summary of every chapter from the textbook, Data Science for Business by Foster Provost and Tom Fawcett. This document is th...

Instructions First, review the full scenario and associated data in the Disaster Recover Plan [PDF] Then complete the following: resource. Assessment 03 Supplement: 1. Develop a disaster recovery plan for the community that will reduce health disparities and improve access to services after a disaster. Assess community needs. Consider resources, personnel, budget, and community makeup. Identify the people accountable for implementation of the plan and describe their roles. ...

As a matriculant of 2023 i find this document useful and i hope you will too. I DON'T BELEIVE IN GATE KEEPING useful information. CAT NOTES.THEORY NOTES CAT.STUDY OPPURTUNITIES.FUNWORKS.IEB.NSC CAT.COMPUTER APPLICATONS TECHNOLOGY.COMPUTER APPLICATONS TECHNOLOGY THEORY NOTES 2023.CAT STUDY NOTES GRADE 12 Chapter 1: General concepts 1.1 Computers and their uses 1.2 Data, information, knowledge and wisdom 1.3 Convergence 1.4 Social implications: Environmental Chapter 2: Hardware 2.1 Buying the c...

Summary based on lectures. I got an 8.5 for the exam. This course covers the general principles and methods that form the foundation of information organization and knowledge-intensive processes, as well as the contexts in which they can be applied and the interaction with users. The lecture topics include knowledge modeling, ontologies, logic, controlled natural language, Semantic Web and Linked Data, as well as knowledge maintenance and evaluation, in addition to guest lectures on speci...

An overview/summary of the course 'Modern Methods in Data Analysis' (part of Epidemiology at Utrecht University/UMC Utrecht). Linear Models, Likelihood and Logistic Regression are explained. Also Poisson models and generalized linear models, Survival Analysis, Resampling methods, Longitudinal Data Analysis are explained.