Course Orientation

Jul 7, 2023

DataSciencePursuit

Welcome to our Intro to Python Course

Welcome to our introductory Python programming course. This course is mostly designed for absolute beginners to programming. No prior programming knowledge is required. We will start from the very beginning and progress towards more challenging concepts. All you need is basic math to begin.

If you havent already, please read our page on what programming is. It provides some context that may be helpful when learning to program.

About Python

Python is the most popular programming language for data science. It is free and open-source and it is also used in web development, AI, software engineering, etc.

About this Course

This course will cover the basics of Python programming with a focus on what is needed to get started with data science. To be clear, this course will not cover data science specific topics yet. We will cover only the necessary fundamental topics to help you better understand more advanced applications of Python, for data science. These applications will be covered in future courses. For aspiring data scientists, we recommend that you don't spend too much time on the basics. The sooner you can start doing data science projects the better, especially for those seeking jobs.

If you later decide to do other things, for example web development, you may need to learn additional basic topics beyond what we cover in this.course.

What is Covered

The fundamentals in any case are very important as they are the building blocks for whatever you need to do. However, learning the basics can be a little boring (or sometimes it feels pointless), so let’s link what we will cover in this course to what we will need as data scientists in the future. In data science, we work with data in different formats, the example mentioned previously was table format. See the example below:

Here is an example of the data you will be working with:

Country Age Employed Indicator
Canada 40 Yes
USA 60 No
South Africa 50 Yes

Tables are made up of different components. They are a collection of data entries that are arranged in rows and columns. In this course, instead of looking at the entire table from the get-go, we will start from the ground up. We will look at the individual components and then build up to working with the entire table or other data formats. Specifically: [we are going to need to clean this data and other data manipulations]

  1. We will start by getting Python either working on it from a free online resource or installing it on our computer.
  2. Then we will work with Python as a calculator to get us to understand how to write and run/execute code in Python.
  3. We will also explore some Python functions. Doing this on basic math can help you focus on just learning the new stuff.
  4. Then we will learn about data types and how to work with them in Python. From our table, some data entries are numbers like age, and some are words or text like country. Different data types are treated differently in Python, and they have different rules.
  5. We will then look at data structures which are collections of data. One is a list which is similar to the rows and columns in our table. The other is called a dictionary which can be easily turned into a table like format. We will learn how to manipulate these structures.
  6. Lastly, we will learn what libraries and packages are and the different libraries that will help facilitate our next steps. How to work with the libraries will be covered in detail in future courses as we go through the data science process.

Throughout the course, we will work on creating a simple rock paper scissors game. It will incorporate almost all the topics to be covered. We will also do other coding exercises, half of which are related to data science. We encourage you to code along. That may help you understand things better and develop problem-solving skills that are key to programming. You can also think of something else you may want to build, and build it as you go through the course. This can be right away or after you learn a certain topic you can think of an application for it.

We hope along the way that you will also learn to be comfortable with googling and asking for help, even from AI (although you should always be cautious when it comes to code). The best code is the code written by someone else.

Once you are done with this course you should be able to move on to learning Python libraries like Pandas and NumPy, which will help you with data acquisition, exploration, and cleaning.

Let’s get started, first order of business, get Python!