@ -148,9 +148,9 @@ Visit [`notebook.ipynb`](notebook.ipynb) to read through the code. You can also
> If you do not know how to run code in Jupyter Notebook, have a look at [this article](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
@ -13,9 +13,9 @@ Data ethics are now _necessary guardrails_ for data science and engineering, hel
In this lesson, we'll explore the fascinating area of data ethics - from core concepts and challenges, to case studies and applied AI concepts like governance - that help establish an ethics culture in teams and organizations that work with data and AI.
Statistics and Probability Theory are two highly related areas of Mathematics that are highly relevant to Data Science. It is possible to operate with data without deep knowledge of mathematics, but it is still better to know at least some basic concepts. Here we will present a short introduction that will help you get started.
@ -235,9 +235,8 @@ In this section, we have learnt:
While this is definitely not exhaustive list of topics that exist within probability and statistics, it should be enough to give you a good start into this course.
@ -21,9 +21,9 @@ We will focus on a few examples of data processing, instead of giving you full o
> **Most useful advice**. When you need to perform certain operation on data that you do not know how to do, try searching for it in the internet. [Stackoverflow](https://stackoverflow.com/) usually contains a lot of useful code sample in Python for many typical tasks.
@ -253,9 +253,9 @@ Here are some examples of exploring data from Image data sources:
Whether you already have structured or unstructured data, using Python you can perform all steps related to data processing and understanding. It is probably the most flexible way of data processing, and that is the reason the majority of data scientists use Python as their primary tool. Learning Python in depth is probably a good idea if you are serious about your data science journey!
Jen Looper
3 years ago
committed by
GitHub