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 Data Science in 2018? Here we go:

1. The difference between Data Science, Machine Learning, Deep Learning and AI

Continue reading “10 things I didn’t know about Data Science a year ago”

Lesson 10: Feature Scaling

What is Feature Scaling?

Feature Scaling is an important pre-processing step for some machine learning algorithms.

Imagine you have three friends of whom you know the individual weight and height.

You would like to deduce Chris’ T-shirt size from Cameron’s and Sarah’s by looking at the height and weight.

Name Height in m Weight in kg T-Shirt size
Sarah 1.58 52 Small
Cameron 1.79 79 Large
Chris 1.86 64 ?

One way You could determine the shirt size is to just add up the weight and the height of each friend. You would get: Continue reading “Lesson 10: Feature Scaling”

Data Science Overview

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:

  1. Supervised Learning
  2. Unsupervised Learning

Supervised Learning

You have training and test data with labels. Labels tell You to which e.g. class a certain data item belongs. Image you have images of pets and the labels are the name of the pets.

Unsupervised Learning

Your data doesn’t have labels. Your algorithm e.g. k-means clustering need to figure out a structure given only the data

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 a Data Scientist in 6 months a hacker’s approach to career planning I build my own learning road map for this year: Continue reading “My personal road map for learning data science in 2018”