In this subject, we learn the fundamentals of Python. This course is designed for people with no programming experience and will ease learners into using Python for a variety of applications, from machine learning to finance.
In these courses, we focus on getting familiar with Python without stepping into machine learning. Once you've completed these courses, you'll be ready to learn how to build machine learning algorithms in Python.
1. Python Fundamentals
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.
2. Functions, Conditionality and Loops
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.
3. Storing, Transforming and Visualizing Data
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.
4. Data Preparation
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.
5. Connecting to Live Data
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.
6. Predict frost risk in vineyards using live data
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.
7. Clean nutritional data from ingredient suppliers
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.