Data Science Crash Course 1/10: Introduction
By: Przemek Chojecki
Tags: Programming, Coding, Machine Learning, Python, Data Science

Data Science Crash Course 1/10: Introduction

Let’s learn Data Science in 2020.

2020 is here and it’s time to learn Data Science! This is a quick course to start your journey with Data Science. If you’re looking to jump into Data Science and especially learn Python, this is a place to start. Our goal is to start programming right away on whatever computer you have right now. Here we go!

Data Science Crash Course. Introduction to Python.

Use Python for Data

Python is a perfect language for beginners and experts alike, due to its popularity and clear structure. It’s easy to pick up and thanks to community you’ll be able to use it both for your first experiments as well as the most recent machine learning research.

Basic Data Science Concepts

First of all I’ll talk about setting up your environment. In our case that means installing Anaconda on your computer, which will allow you to quickly run Jupyter Notebooks and there you’ll be running short Python programs straight away from your browser. So from now until your first program you probably need like 15 minutes. Read this post to know more about Anaconda.

Then to do data science you will need a little bit of mathematics. I will review basic terms from linear algebra and statistics that you will need along the way. Those include:

vectors and operations on them

matrices and operations on them

mean, variance, correlation

random variables, dependence, conditional probability,

Naive Bayes theorem

Have a look at this post to learn more about mathematics for Data Science.

Then we can go directly to processing data:

importing files in .txt, .json, .xml, .html

storing data in NumPy arrays, pandas DataFrames and other structures (lists, dictionaries)

I discuss processing data methods in this post on Medium.

Getting and processing Data

I will also discuss how and where you can get interesting data from. Because Data Science is about Data, so you should find a dataset which is exciting to you. Some examples include:

books and documents, for example from Project Gutenberg

scraping websites (downloading sport or finance news)

Kaggle databases

Twitter API

Read this post to learn more about getting data for Data Science.

Standard techniques in Data Science

Having data is the first step to Data Science and once we have data we can start working with. Standard techniques include:

Classification: we have 2 or more categories (labels) and we want to classify objects according to these labels. We talk here about supervised learning and standard techniques include: KNN, Naive Bayes, Decision Trees, XGBoost, neural networks. Read more about classification here.

Clustering: we don’t have any labels but still we want to classify our data. We cluster our data by different measures of similarity, thus creating labels. We talk here about unsupervised learning and standard techniques include: k-means and DBSCAN. Read more about clustering here.

Neural Networks is a separate topic in itself. The basic idea behind them is simplifying a problem into smaller pieces which can be processed separately by ‘neurons’. I’ll cover basic frameworks like Keras and Tensorflow, and then also discuss MNIST and other problems which are great to start with. Read about neural networks here.

Dimensionality Reduction is a must if you want to visualize data or better understand it. Sometimes you just want to plot your 4D data in 2D and then you have to transform it to still capture the essence of information. Standard techniques include PCA and SVM. Have a look here.

Visualizing Data is important when it comes to business applications of Data Science. In the end you want to convey your findings to others, and the best way to do that is by presenting them with a nice graph, where everything is clear. We’ll talk about plotly and Dash. Read it here.

After this series you’ll be prepared to tackle basic Data Science problems and learn more by yourself.

Let’s learn Data Science in 2020!

If you want to see a video for this course, please have a look here: