Data Science for Business notes

Full summary of the book 'Data science for business' including graphs and pictures from the book!

Lecture slides and summary of the book

This summary comprises all subjects for the course strategy analytics, containing both summary of the book and lecture notes.

Complete summary of: - Book: Data Science for Business (Provost & Fawcett) - Case studies summary and answers (P. Snoeren) All exam materials needed next to the lecture slides!

Behaalde score: (17/20); Kwalitatieve, uitgebreide, duidelijke, allesomvattende (alle behandelde hoofdstukken) (128p) samenvatting (in Engels) van het vak Data Mining gebaseerd op het handboek, eigen notities en de slides. Recentelijk geschreven en gebruikt (2022).

Summary of the course BDMA. Grade achieved: 8.8

Summary of all the lectures WITH additional information from the book added to each concept. The most complete summary you will get that helps you receiving a high grade

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...

Extensive summary for the Data Science for Business course, Based on the book Data Science for Business (Provost & Fawcett). Chapters included are Ch 1, 2, 3, 4, 5, 6, 7, 8 and 9.

A detailed summary of the lessons of Machine Learning taught by David Martens at the University of Antwerp. This is a summary of my own notes, the slides and the book Data Science for Business.

The document has all my notes from the classes and videos. I integrated all the book parts that were missing from the lectures, but I would recommend reading the chapter the professor requires before class, it makes things easier. The case studies are missing. My final grade for the exam was 8,42, and I only studied these notes

A detailed summary of the lessons of Machine Learning taught by David Martens at the University of Antwerp. This is a summary of my own notes, the slides and the book Data Science for Business.

Behaalde score: (17/20); Kwalitatieve, uitgebreide, duidelijke, allesomvattende (alle behandelde hoofdstukken) (128p) samenvatting (in Engels) van het vak Data Mining gebaseerd op het handboek, eigen notities en de slides. Recentelijk geschreven en gebruikt (2022).

Study this example: Copy and paste the provided R source code into an R markdown file and make it run. Weave an explanation of the model and it's conculsions into your markdown file.

Using Chapter 5, we will experiment with association analysis.

Using Chapter 4, let's look at other ways to do classification - nearest neighbors, decisions trees, support vector machines, even neural nets.

Using Chapter 3, let's about classification. This week we will be using the zoo dataset, another common dataset among data mining classes.

Using Chapter 2, let's learn how to explore data. We will be using the Iris dataset which is commonly one of the first datasets used in data mining classes.

Edit the provided R Markdown document so that It contains your name and then knit it as HTML.

We will be using examples from the book An R Companion for Introduction to Data Mining by Michael Hahsler. Every week you will be assigned a new example from this book. The instructions for each week are identical: copy and paste the source code into R studio and run it. understand what it does, explain how the dataset is processed, generate every graph, etc. document your work in an R Markdown document.