Tom Clark's Blog

5 Easy Ways To Learn Python For Data Science In Weeks

How would you like to have the [url=]sexiest job of the 21st century[/url]? It might come as a huge shock that we're talking about being a Data Scientist.
Harvard Business Review might call it sexy but we'd say in-demand, vital and well-paid.
Learning python for data science is the first step towards getting yourself a career in Data Science. And we're going to walk you through the rest of the steps to get you well on your way.
Let's start with what Python is and why it is perfect for Data Science.
What Is Python? Python is a clean and easy to understand programming language published in 1991. It was designed to automate repetitive tasks and is commonly known because of the ease to learn and extensive documentation.
Python is a very versatile language that can support many different platforms and systems
Why Learn Python for Data Science Python has proven time and time again that it can be relied on to solve complex problems efficiently. Python has gained an important role in the web development field and especially in artificial intelligence, machine learning, and data science.
Even if you don't work in these fields, python opens career opportunities due to its popularity in web development.
With all of the documentation surrounding Python, the data-focused libraries allow programmers to use Python to quickly deploy Python for data-science tasks.
How to Learn Python Efficiently The great thing about Python is that it is incredibly easy to learn - if you know how to approach it. We're going to talk all about the best [url=]way to learn python[/url] for data science but here are a few things you need to know first:
1. You don't need to have studied computer science
2. You don't need a full Python programming course
3. You don't need to memorize the full syntax
This is actually what makes learning python for data science so convenient. You don't need to learn about all of the applications for python just the ones you need.
Start with the core concepts of Python before moving onto learning the essential libraries you'll be using. Finally, take a project-based approach to learn the applications you need.
Let's take a look at the easy ways to learn Python.
5 Easy Ways of Learning Python for Data Science Learning [url=]python data science[/url] is not only easier than you might think, but it is also worthwhile, with the demand for data scientists higher than ever.
It's as easy as following these 5 steps:
1. Learn Python Fundamentals
You want to start off by learning the basics of Python and Data Science so you are familiar with the general concepts and syntax.
Set yourself up for success by surrounding yourself with the tools and support to learn and hone your skills. You can do this by joining Python communities such as Kaggle which will also help you find jobs and opportunities down the line.
Download the [url=]Jupyter Notebook[/url] to help guide you through the fundamentals of Python. It's great to have a reference when learning a new language.
2. Practice with Projects
Once you have the basics down, start using the command line to execute small Python projects. This is your opportunity to work on small apps such as games, weather apps or calculators.
You can get extra practice with APIs and web scraping which will help you prepare for future projects.
This is also the time to read as much as you can online - there are great blogs, guides and even open source code you can scour through to improve your knowledge.
3. Learn Data Science Libraries
Things are starting to get exciting! By now you're familiar with the basic concepts and you've already been practicing your Python programming.
Now it's time to dive into data science with the libraries. With Python, there is generally a correct way of doing things, and the libraries allow you to learn these methods.
The three libraries you want to get familiar with are NumPy, Pandas, and Matplotlib. The first two are great for discovering Python for data science and the last is a visualization library for creating graphs like the ones you would see in excel.
4. Build a Portfolio of Data Science Projects
If you plan on Data Science as a career then a portfolio of Python projects is a must.
Your portfolio will show future employers that you have committed to learning Python and applying it to real use-cases. Your portfolio should include different datasets to show your skills, even if there is no unified theme.
This will double as your resume and give you the opportunity to focus on specific skills.
5. Apply Advanced Techniques
You will never stop learning when it comes to any programming language as updates are ever expected. Python is no different so as you continue to learn and practice you can also incorporate advanced techniques into your code.
Collaborate with others to learn from them and embark on advanced courses to learn new tricks.
Are You All Set with Python? There you have it, five steps to learning python for data science. You can work your way towards a career with guaranteed demand and limitless possibilities for learning.
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