Data Science Crash Course

You will learn how to start with Python on your computer. You'll install Anaconda and open your first Jupyter Notebook. Then you'll be able to run your first code.

Before starting proper Data Science it's important to learn the fundamentals, and that is mathematics. You'll learn about:

• vectors

• matrices and operations on them, like multiplication

• mean, variance, bias, correlation

• dependent and independent random variables

• Bayes theorem

You will learn importing texts, json files, xml and scraping the web for html files.

We store data in lists, dictionaries, NumPy arrays or pandas DataFrames.

There are many sources from which you can download data:

• books from Project Gutenberg

• scraping websites (import requests)

• Kaggle databases (www.kaggle.com)

• Twitter API

Labelling data is one of the most common problems in Data Science. You're given a dataset and a couple of labels and you try to group objects from a dataset into according labels. This is a prevalent problem in real life - from marketing to social sciences.

You'll learn basic methods for classification:

• K-Nearest Neighbours (KNN)

• Linear Regression

• Decision Trees

• XGBoost

• Neural Networks

Working with unlabeled data is a bit more complicated. Clustering is all about grouping objects from a dataset, without knowing labels a priori. You'll learn standard methods for clustering: k-means and DBSCAN.

Neural networks are among the most efficient tools in Data Science and they are the basis of deep learning. You'll learn about perceptrons, backpropagation and feed forward networks, and also learn about Keras and Tensorflow, two basic frameworks for working with Neural Networks.

Reducing dimensions of your data is often crucial to visualize it and then extract precise information. You'll learn about Principal Component Analysis, PCA, the most fundamental method for dimensionality reduction.

When you've got great results and you want to present them to others, it's crucial to use good tools. Fortunately in Python you have many ways to visualize data and results. We'll talk about:

• matplotlib

• Dash

• publishing your work on GitHub

After finishing this course, you have a basic understanding of what is Data Science.

If you're decided on pursuing a Data Science Career, the next step is to enroll for Data Science Job course: https://datasciencerush.thinkific.com/

In Data Science Job I explain in detail how to find an entry level data science job, do you need to do a PhD or how to use AngelList, and much more!