Python for Beginner Data Scientists

Before we get started we'll review Ryan (the instructor)'s teaching philosophy, the format of the course and about this thorough, beginner-friendly, Python for Data Science course will help you succeed.

Python proficiency is required if you want to become a professional data scientist. Learn why Python is the most important language for both beginner data enthusiasts and professionals.

Don't make the mistake of trying to install complex tools if you're a beginner. Use cloud-based notebook editors like Google Colab to immediately start learning and writing Python for Data Science –with no setup required!

Get acquainted with Google Colaboratory as a tool for writing Python for Data Science. This lesson features an overview of "notebooks" in general in addition to some Google Colab specifics.

Temzee Lessons are made to look and feel like an IPython notebook. They included video content and video transcripts about learning Python and Data Science –and also Python and Data Science exercises to practice what you're learning, and give you feedback on your code.

Whenever we create a variable we put the name of the variable first, then a single equals sign, and then the value that we want to store to that variable on the right. The technical name for this is called "declaring a variable" or is also known as "variable assignment".

Practice creating variables with legal variable names and saving integer values to them. Creating and working with variables is the most fundamental programming skill in any language.

Having descriptive variable names gives helpful context about your Python code, makes it more readable, and is easier to maintain. Readable code is more important than concise code or even code that runs fast.

Variables in Python can only be made up of letters, numbers, and underscores. Variable names cannot start with a number. Reserved Keywords cannot be used as variable names. Variables are case sensitive, and should follow the snake_case convention.

The print function (aka the print statement) displays the value that is stored to a variable to the output section of a code cell. In notebooks, it is not the only way to print out a value, but it is a foolproof method for doing so.

All data must be represented numerically before it can be passed to a machine learning algorithm, so mastering working with numeric datatypes like integers and floats is one of the most fundamental skills for prospective Data Scientists

Practice creating variables of both datatypes: float and int, and show that you can use the type() function to check a function's stored datatype.

Python excels at number crunching, the most basic form of that is simple math operations like: addition, subtraction, multiplication, division, and exponentiation. We can also use parentheses to enforce order of operations.

Practice using python's most commonly used arithmetic operators to do basic math calculations.

Strings are how we represent text in Python. They are a sequence of characters that are wrapped in quotation marks. They're important for recording categorical data and can be manipulated in a variety of ways.

Comments are how we can leave notes in our code for the benefit of ourselves and others. Lines of code that are "commented out" won't be executed by Python, but are still readable by humans viewing the code.

Functions are one of the most important parts of any programming language. They allow us to store code to variables and make our code reusable. We pass information to functions via parameters and arguments and then functions return outputs to us.

f-strings allow us to insert raw values or values held within variables into text strings. This helps us create well formatted and readable output to display below our code cells for the benefit of anyone looking through our notebook.

Booleans, Conditionals and If Statements all come together to allow us to conditionally execute certain lines code in our programs if the right conditions are met. This gives our programs some basic decision making abilities and gives a logical flow to our program's execution which (as the writer of the code) we're able to control.

Lists are so important to Data Scientists! This is our first "data structure" which will allow us to store multiple pieces of data within a single variable. This is where we actually get to start working with and analyzing data!

Practice creating new lists and using some of Python's built-in functions to manipulate lists of numbers. We'll write a function that can calculate the mean of any list of numbers!

In this lesson we'll draw from our knowledge of one kind of sequence (strings) to learn about another kind of sequence (lists), to show that many operations on these sequences have a similar (if not identical) coding syntax.

More lessons are coming to this course soon. Written versions of each lesson and associated exercises will be released first followed and then video versions will be released sometime later. DM me (Ryan) on Discord if you're dying for the next installment.