### 10 things I didn’t know about Data Science a year ago

In my article My personal road map for learning data science in 2018 I wrote about how I try to tackle the data science knowledge sphere. Due to the fact that 2018 is slowly coming to an end I think it is time for a little wrap up. What are the things I learned about…

### Classification: Precision and Recall

In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean? Clarification Living together with little kids You very often run into classification issues: My daughter really likes dogs, so seeing a dog is something positive. When she sees a normal dog e.g. a…

### Lesson 2: Naive Bayes

Lesson 2 of the Udacity Course UD120 – Intro to Machine Learning deals with Naive Bayes classification.

### Linear Algebra with numpy

Numpy is a package for scientific computing in Python. It is blazing fast due to its implementation in C. It is often used together with pandas, matplotlib and Jupyter notebooks. Often these packages are referred to as the datascience stack. Installation You can install numpy via pip pip install numpy Basic Usage In the datascience…

### Introduction to Jupyter Notebook

JuPyteR Do You know the feeling of being already late to a party when encountering something new? But when you actually start telling others about it, you realize that it is not too common knowledge at all, e.g. Jupyter Notebooks. What is a Jupyter notebook? In my own words: a browser-based document-oriented command line style…

### Data Science Datasets: Iris flower data set

Motivation When you are going to learn some data science the aquisition of data is often the first step. To get you started scikit-learn comes with a bunch of so called “toy datasets”. One of them is the Iris dataset. Prerequisites & Imports Besides scikit-learn we will use pandas for data handling and matplotlib with…

### Data Science Overview

Questions Data Science tries to answer one of the following questions: Classification -> “Is it A or B?” Clustering -> “Are there groups which belong together?” Regression -> “How will it develop in the future?” Association -> “What is happening very often together?” There are two ways to tackle these problem domains with machine learning:…

### My personal road map for learning data science in 2018

I got confused by all the buzzwords: data science, machine learning, deep learning, neural nets, artificial intelligence, big data, and so on and so on. As an engineer I like to put some structure to the chaos. Inspired by Roadmap: How to Learn Machine Learning in 6 Months and Tetiana Ivanova – How to become…

### Bayes’ Theorem

Imagine that you come home from a party and you are stopped by the police. They ask you to take a drug test and you accept. The test result is positive. You are guilty. But wait a minute! Is it really that simple?