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Learn Python for Data Science: A Guide

According to a study by KDnuggets, Python was used by 65.6% of data scientists as of May 2018. The study found that Python was the most popular programming language, followed by R.

There are many reasons why Python has become so popular in data science.

First, Python has an easy-to-use syntax, which means that acquiring the language is not as difficult as learning a technology like R. Second, Python has a vast repository of third-party libraries written by data scientists, for data scientists, that give you even more tools to use in your work.

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With this in mind, it’s clear that learning Python is valuable if you want to pursue a career in data science. But, you may be asking yourself: how do I learn Python for data science?

That’s a great question, because learning Python for data science is different than just learning to code in Python. In this guide, we’re going to break down five steps you can follow to learn how to code in Python for data science.

Step 1: Learn the Basics of Python

As you can expect, the first step in your journey will be to learn the basics of Python.

This may not sound like the most exciting part of performing data analysis with Python, but you need to have a solid grasp of the fundamentals before you can get on to creating advanced algorithms and analyzing big data sets.

You must spend some time becoming familiar with concepts like variables, data types, classes, loops, objects, functions, and everything else that makes up the basics of Python. You’ll also want to become proficient in using a number of Python’s in-built user functions and gain a firm understanding of Python’s object-oriented structure.

To help you learn the basics of Python, there are a few approaches you can use. These include:

  • Take an online course. Online courses provide you with a comprehensive look at how to code in Python and are a great way to get started.
  • Follow tutorials. Tutorials walk you through a specific technical concept and are a good way to attain mastery over a given idea.
  • Join a community. A community like Kaggle or a data science Slack group is a great platform to build your network, learn about their educational journeys, and discover the most effective learning resources that are out there.

Once you know the basics of Python, you’re ready to start analyzing data. That’s because there’s already a lot you can do with only the functions offered to you by Python. For instance, you can use basic Python features like loops to analyze a dataset, and inbuilt functions to perform basic data cleaning.

Step 2: Start a Few Projects

Project-based learning is an effective way of achieving mastery over a technical concept.

Python is so easy to get started with. You should have no trouble turning the theory you learn from courses and tutorials into projects. Use your skills to build something like a Python game or a simple program that interprets data about a national election.

These projects will help you reinforce your Python skills and expand your repertoire of skills. As you build projects, you’ll likely find gaps in your knowledge, but that will encourage you to learn new concepts.

Step 3: Learn to Use Python Data Science Libraries

As we mentioned earlier, there’s a lot you can do with Python – you don’t need to learn any libraries to use Python for basic data analysis.

But if you want to use Python for professional data science, you should make use of available libraries. These libraries provide a wide range of functions that automate repetitive tasks and help save time.

Here are a few of the libraries you’ll want to research in depth:

  • Numpy: Numpy is the “fundamental package for scientific computing with Python” and it offers mathematical functions you can use for data analysis.
  • Matplotlib: Matplotlib is a flexible visualization and plotting library commonly used for data analysis.
  • Pandas: Pandas is a library built on top of Numpy. It allows you to perform exploratory analysis on a dataset.
  • Seaborn: Seaborn is a library built on top of Matplotlib, allowing you to easily plot common data visualizations.

The best way to learn how to use these libraries is to pick one, then go deep into using it. Read over the basic documentation for the library, then try to follow a tutorial to use some of the functions you’ve read about in a project. Often, these libraries come with their own quickstart tutorials which act as a good guide.

Don’t feel pressured to learn every function from all of the above mentioned libraries. These libraries have been in development for years and include a number of advanced features that you may never need to use. Focus on learning the fundamentals, and as you continue on your learning journey, you can explore more advanced concepts.

Step 4: Build a Portfolio

Building a portfolio is an essential part of becoming a data scientist.

Your portfolio will act as a one-stop place where people can go to learn about your skills. You’ll be able to display the data science projects you’ve worked on, the insights you have derived from data, and the different problems you have solved.

This will give other people an indication of what skills you have – and what you are still learning – which will make it easier for prospective employers to evaluate your proficiency in data science concepts.

To build a portfolio, you can create your own website. Or, you could upload all your projects to GitHub and write a good README for each repository.

The idea is you should make sure your projects are presented well and easy for others to understand. That way, if someone finds your portfolio online, they’ll have no trouble getting to know you better.

Step 5: Reinforce Your Skills

Building a portfolio is just one part of reinforcing your skills.

Over time, you should attempt taking on increasingly difficult challenges. As you get started, you may work with small data sets with a limited number of arguments. But, when you feel confident, you can start experimenting with larger data sets and more complex methods of data analysis.

The best way to reinforce your skills is to build new projects and to build upon existing ones as you develop more knowledge.

However, another option is to participate in challenges on Kaggle. Kaggle is a site that hosts data science competitions, and allows you to test your skills. On Kaggle, you’ll find a number of datasets, alongside a description of the dataset, and the goals that the creator of the challenge wants you to meet.

The upside of using Kaggle is that the platform makes it easy to find high-quality datasets with which you can work. And because Kaggle is a community, you’ll be able to find other developers with whom you can speak about a dataset.

If you want to become an expert Python data scientist, you should keep looking for new learning opportunities. Read as much as possible, work on new projects, take on Kaggle challenges, and above all else, make sure that you are always practicing your skills.

Learning Python for Data Science

Python is an excellent language to learn if you want to become a data scientist. Not only does Python offer a wide range of tools for data scientists, but the language also has a good learning curve and a simple syntax.

While you may feel somewhat intimidated to get started, almost every new developer experiences this emotion. Once you start building projects with the skills you have learned, you’ll slowly get used to the syntax of Python and gain more confidence in your work.

If you devote the right amount of time – say, an hour a day – it will take you around a month or two to master the basics of Python. Then, it will take a few weeks to master the basics of a Python data science library, and a few months to really delve deep into each library.

Overall, you can expect that, if you commit the right amount of time to learning, you should be able to learn Python for data science within a few months to a year.

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