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:

Table of Contents

## The difference between Data Science, Machine Learning, Deep Learning and AI

A picture says more than a thousand words.

## The difference between supervised and 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

## The areas of applied machine learning

are described here: The Essence of Machine Learning and Data Science Overview

## Bayes Theorem

In my article Bayes theorem I elaborated about the **base rate fallacy **and in naive bayes I recapped the second lesson from udacity’s UD120 Intro to Machine Learning

## Precision and Recall and ROC

In my article classification: precision and recall I wrote about different useful measures to evaluate the quality of a supervised learning algorithm.

In Receiver Operating Characteristic I wrote about another useful measures the ROC.

## Visualization with matplotlib

Matplotlib is a really good starting point for visualization. I wrote about it in Introduction to matplotlib, Matplotlib – Part 2, Scatterplot with matplotlib

## Math with numpy

I wrote some articles about the usage of numpy but only scraped the surface of this mighty library

## Image manipulation with OpenCV

## JuPyter Notebooks

Sometimes I love them sometimes I hate them. I wrote an Introduction to JuPyter Notebook

## Podcasts

In 2018 I’ve listened to a bunch of great podcasts on iTunes: