Data Preparation with Python

Data doesn't have to be just for technically minded analysts. These courses teach you all the non-technical principles of understanding and working with data in your company.

Learn the key principles of data preparation in order to format your data in a consistent manner, ready for analysis. You'll gain a solid understanding of this critical process, ensuring that your insights are built on solid foundations.

In this course, the learner will get a grasp of the basics of Python. We'll learn about the basic principles behind Python, along with learning what Python is and why it's popular.

In this course, we learn some important Python functionality that considerably enhances what we can do with Python code. We learn about functions, conditional statements, and repeating code loops.

This course focuses on using Python to organize data We'll learn about ways to load, clean, transform and visualize our data. We'll also cover 3 critical Python libraries: NumPy for advanced calculations, Pandas for reading and manipulating datasets, and MatPlotLib for visualizing data.

Learn about techniques you can use to manipulate data such as data unions, joins and aggregation. You'll also cover data cleaning methods such as handling nulls, duplicates, false data types, and more.

Use Python to connect to live data from an online source. You'll learn how to download csv files hosted online, add web page tables to Python and connect to data from APIs.

Help a winemaker that has a system in place for predicting the risk of frost in its vineyards. Currently, the company manually adds minimum temperature forecasts, but this is slow and if forgotten, can lead to frost damage. You’ll automate this process using Python to connect to live weather data.

Help a homemade meal kit delivery company with a data problem that's breaking customer trust. You’ll connect to data from multiple online sources and then apply data cleaning best practices to create a dataset free of errors and inefficiencies.