Course: Python Essentials for Data Scientists

Data School/Python Essentials for Data Scientists

Python Essentials for Data Scientists

Build a solid foundation in Python and launch your Data Science career! 🚀

Get up-to-speed with Python quickly 🏃‍♀️

As a data scientist, you'll spend a lot of time using Python libraries such as pandas, NumPy, and scikit-learn. However, these libraries assume that you already know the fundamentals of the Python programming language. And if you don't know those fundamentals, you're going to get stuck!

This course will get you up-to-speed quickly with Python's most important features so that you can work in data science libraries with ease and be more productive as a data scientist!

Who should take this course?

This is the perfect course for you if:

You're brand new to Python, and you want to learn the most important features quickly
You have some Python experience, and you want to fill in the gaps in your knowledge
You've been using Python for a while, but you want to practice your skills and write more efficient code

I'm neither an aspiring programmer nor an aspiring data scientist; I'm an accountant. That said, I wanted to learn Python to make working with data easier, in a "work smarter not harder" way. Looking for an introduction to Python, I found Kevin's Python Essentials course.

Each lesson logically built onto the last lesson. The laddering of lessons was a great way to LEARN instead of memorize. Kevin takes the time to not only show a concept, but also explains the rationale. For someone like me looking to understand the WHY behind a method, Kevin's instructional style is GOLDEN!

While I started from 0 in Python (I had a bit of SQL and HTML knowledge), I can't wait to dive a bit deeper and learn even more from Kevin. For anyone looking to grasp the introductory concepts of Python: buy Kevin's course, set aside a day and you'll learn Python in no time.

- Paula Small (Accountant)

What topics are covered in the course?

This course teaches you the fundamentals of the Python programming language so that you can be successful with Python's Data Science libraries:

Basic types: Numbers, strings, booleans
Data structures: Lists, tuples, dictionaries
Working with data: Slicing, accessing nested data, using counters
Operators: Mathematical operators, membership operators
Control flow: For loops, while loops, comparisons, conditional statements
Comprehensions: List comprehensions, dictionary comprehensions
Functions: Importing functions, calling functions, defining functions, writing docstrings
Classes: Importing classes, creating instances
Useful built-in functions: print, len, type, sorted, sum, range, zip, help
Best practices: Naming objects, writing comments
Bonus topics: f-strings, multiple assignment, unpacking into a function call, basic plotting with Matplotlib

I saw where you had added content to the Python Essentials course that I had taken a while ago and I was curious to see the changes. I was surprised by all the added content and particularly pleased that you included sections on Matplotlib and list & dict comprehensions.

This class was already a good first step for anyone learning Python for data science, but with your upgrades it's all the more useful. I've already started using the comprehension protocol in my code at work.

Just wanted to say thanks again for another good class, well-designed and expertly delivered.

- Bruno DiGiorgi (Project Manager)

What's included in the course?

48 bite-sized video lessons
9 sets of exercises with detailed solution walkthroughs
7-part practice project to reinforce everything you've learned
44 interactive quiz questions
Jupyter notebook & Python script with well-commented course code
Links to my recommended resources
Certificate of completion at the end of the course
Lifetime access to everything
Free access to future course updates

Buy now

Join 500+ happy students...

Tim Reimer (CTO)

This course is a very good intro to Python for novice users. It covers all the major topics in a brief and concise format. The exercises are well-designed.

Srinivasu Nalla (DBA)

After taking this course, I'm confident enough to start my journey with Python for data science.

Bhim Karki (Senior Data Consultant)

I recommend Kevin's courses to anyone who is new to data science. The way he presents is amazing.

Paul Harnagel (Data Scientist)

Kevin, you have again delivered on your superpower: clear and insightful explanations of the content that matters. For those new to Python, this course will propel you further up the learning curve quickly.

Krystian Czech (Portfolio Manager)

This Python basics course is great! I am from Poland and my English is not very good, but the speed of the lessons was just right for me. Great job, Kevin.

Elliot Kleiman (Data Scientist)

This intro course is perfect for those who have little to no background with Python programming. At the same time, it's also useful as a review of the fundamental principles of the language for those who have more experience using Python.

Joe Stuart (Analytics Manager)

I thoroughly enjoyed this course. Its structure, length, and topics were easy to follow and exceeded my expectations. It reinforced my existing Python knowledge while teaching me features I was unfamiliar with.

I highly recommend this course if you are looking for foundational material to get you started in your data science journey.

Eraj (Analyst)

This is a great introductory course to the essentials of Python. I recommend it.

Kevin has a great approach to teaching Python without making things too involved and getting straight to the point without inundating one with unnecessary information.

Azad Ibrahim (Data Analyst)

Kevin is a great communicator and his style of explanation is very unique. All that he explained in this course was very clear to me.

The extra resources are fantastic as well, if you want to learn more about the topics covered in the course.

FAQs

What do I need to know before the course?

Nothing! It's okay if you're brand new to programming and Python.

What software do I need to install?

The only software that is strictly required is a Python editor, and you can use any editor you like. However, there are a few lessons that teach you the basics of plotting with the Matplotlib library, so it's a good idea (but not required) to install that as well.

My overall recommendation is to install the free Anaconda distribution of Python, which includes hundreds of popular data science libraries (including Matplotlib). I've included detailed Anaconda installation instructions in the course.

Which Python editor should I use?

I'll be writing code using the Jupyter Notebook, but you can use Spyder, PyCharm, Google Colab, or any other editor you like. (I've included a guide in the course that will help you to choose.)

Does the course teach Python 2 or 3?

The course teaches Python 3.

Does the course teach specific data science libraries?

The courses teaches you the fundamentals of the Python programming language so that you can be successful with data science libraries. There are a few lessons on plotting with Matplotlib, but that is not the focus of the course.

However, I do offer separate courses on pandas and scikit-learn, which are two of the most popular data science libraries!

How long is the course?

You can complete the video lessons in about 3.5 hours. However, you should budget additional time to complete the exercises, quizzes, and practice project.

Beginner Python users should expect to spend 8 to 10 hours on the course, whereas users with Python experience should expect to spend 6 to 8 hours on the course.

What if I need help during the course?

You can post a question below any video, and I'll do my best to respond!

How do I earn a certificate of completion?

Once you have watched all of the lessons and attempted all of the exercises and quizzes, you can request a certificate of completion.

How long will I have access to the course?

You will have lifetime access to the videos, exercises, quizzes, and code.

What courses should I take after this one?

You should take my introductory data science courses, pandas in 30 days and Introduction to Machine Learning with scikit-learn.

Do you offer any discounts?

Yes! I offer Purchasing Power Parity discounts (also known as location-based discounts) for all of my paid courses. If you're located in one of the 160+ qualifying countries, you should automatically see a discount code at the top of this page.

I also offer student discounts and hardship-based discounts, regardless of where you live. Please email me at kevin@dataschool.io and I'd be happy to send you the appropriate discount code.

What's your refund policy?

If you decide that the course isn't a good fit for you, I'd be happy to give you a full refund within 30 days of purchase.

I have another question...

Please email me at kevin@dataschool.io and I'd be happy to answer your question!

Course Outline

Introduction

Welcome to the course!

Install the Anaconda distribution

Choose a Python editor

Download the course code

Course overview

Basic Data Types

Quiz: Basic Data Types

Lists

Quiz: Lists

Exercise: Lists

Solution: Lists

Comparisons & Conditional Statements

Quiz: Comparisons & Conditional Statements

Functions

Quiz: Functions

Exercise: Functions

Solution: Functions

Exercise: Functions & Conditional Statements

Solution: Functions & Conditional Statements

Naming Objects

Quiz: Naming Objects

Writing Comments

Quiz: Writing Comments

List Slicing

Quiz: List Slicing

Strings

Quiz: Strings

Exercise: Strings & Slicing

Solution: Strings & Slicing

For Loops

Preview

Quiz: For Loops

Exercise: For Loops

Solution: For Loops

List Comprehensions

Preview

Quiz: List Comprehensions

Exercise: List Comprehensions

Solution: List Comprehensions

Dictionaries

Statements

Lists vs. Dictionaries

Quiz: Dictionaries

Exercise: Dictionaries

Solution: Dictionaries

Nested Data

Quiz: Nested Data

Exercise: Nested Data

Solution: Nested Data

Nested Data & Tuples

Quiz: Nested Data & Tuples

Exercise: Nested Data & Tuples

Solution: Nested Data & Tuples

Imports

Quiz: Imports

Intermission

Can I ask you a quick favor?

Project: Part 1

Project Overview

While Loops

f-strings

Mathematical Operators

Project Exercise 1

Solution to Exercise 1

Project: Part 2

Separating Functions

Writing Docstrings

Project Exercise 2

Solution to Exercise 2

Project: Part 3

Classes

Counter

Range

Project Exercise 3

Solution to Exercise 3

Project: Part 4

Zip

Project Exercise 4

Solution to Exercise 4

Project: Part 5

Line Plots with Matplotlib

Bar Plots

Multiple Assignment

Unpacking into a Function Call

Project Exercise 5

Solution to Exercise 5

Project: Part 6

Dictionary Comprehensions

Project Exercise 6

Solution to Exercise 6

Project: Part 7

Membership Operators

Project Exercise 7

Solution to Exercise 7

Conclusion

Can I ask you a quick favor?

Recommended Python resources

Request your certificate of completion

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👋 Welcome to Data School!

My name is Kevin, and I've taught Data Science in Python to over a million students.

My courses explain data science topics in a clear, thorough, and step-by-step manner.

I'd love to teach you, regardless of your educational background or professional experience.

Thanks for joining me! 🙌

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