What Are the Benefits of Learning Python for Data Science?
Before we go into how to learn Python for data science, it’s important to first understand why you should learn Python in the first place.
In short, knowing Python is one of the most important skills for a data science profession.
Python is the programming language of choice for data science, albeit it hasn’t always been. Here’s a quick rundown of the past:
On Kaggle, the top platform for data science competitions, it surpassed R in 2016.
It surpassed R in KDNuggets’ annual survey of data scientists’ most utilized tools in 2017.
Python was the top language for analytics experts in 2018, with 66 percent of data scientists reporting that they use it on a regular basis.
Experts in data science believe that this trend will continue as the Python ecosystem develops. While your journey to learn Python programming may be just getting started, it’s comforting to know that job prospects are plenty (and rising).
The average income for a Data Scientist is $121,583, according to Indeed.
What’s the good news? This figure is only expected to rise as the demand for data scientists continues to rise. According to Quanthub, there will be three times as many data science job ads as data science job searches in 2020. As a result, the demand for data scientists far outnumbers the supply.
So, data science has a bright future ahead of it, and Python is just one component of the puzzle. Learning Python and other programming essentials is, fortunately, as simple as it has ever been. In five easy steps, we’ll show you how.
But keep in mind that just because the processes are straightforward doesn’t mean you won’t have to put in some effort. You have the opportunity to not only learn a new talent, but also to take your career to a new level if you apply yourself and devote serious time to studying Python.
Python for Data Sciences: How to Get Started
To begin, you’ll need to choose a suitable course to assist you in learning Python programming. Dataquest’s courses are tailored to help you learn Python for data science at your own speed, requiring you to write actual code and work with actual data in our interactive, in-browser interface.
Your path to becoming a data scientist should include soft skills in addition to learning Python in a classroom setting. We also suggested that you master some complementary technical skills along the road.
Step 1. Learn the fundamentals of Python.
Everyone has to begin somewhere. You’ll study the fundamentals of Python programming in this first stage. You’ll also need a crash course in data science.
Jupyter Notebook, which comes preconfigured with Python modules to assist you master these two things, is one of the crucial tools you should start utilising early in your trip.
Joining a community might help you get started with your studies.
By joining a community, you’ll be surrounded by others who share your interests and enhance your job prospects. Employee referrals account for 30% of all hires, according to the Society for Human Resource Management.
Create a Kaggle account, join a local Meetup group, and interact with current students and alumni in Dataquest’s learner community.
Complementary abilities: Take a look at the Command Line Interface. You may use the Command Line Interface (CLI) to run scripts faster, allowing you to test programs and work with more data.
Step 2: Put your Python skills to the test with mini projects.
Hands-on learning is something we really believe in. You might be astonished at how quickly you’ll be able to construct simple Python projects. We’ve already put out a fantastic beginner’s guide to Python projects, which covers ideas like:
Tracking and Analyzing Your Personal Amazon.com Spending Habits — This is a fun project that will help you learn Python and pandas fundamentals while also providing you with some actual insight into your personal finances.
Analyze the Results of a Survey — In this starter project, you’ll learn how to drill down into replies to harvest insights using public survey data or survey data from your own work.
Try one of our Guided Projects, which are interactive Python projects for people of all skill levels that use actual data and provide instruction while also pushing you to utilise your talents in new ways.
But that’s actually only the top of the iceberg. You can attempt developing calculators for an online game or a software that retrieves the weather in your city from Google. You may also use Python to create small games and apps to help you learn the language.
Mini projects like this will assist you in learning Python. These kind of programming tasks are common across all languages and are an excellent method to cement your mastery of the fundamentals.
Start by gaining knowledge with APIs and then move on to web scraping. Web scraping will be useful for data collection in the future, in addition to helping you understand Python programming.
Reading is a great way to get started with your study.
Improve your academic performance and learn how to solve Python programming problems. To learn Python and data science best practices – and gain fresh ideas – read guidebooks, blog posts, and even other people’s open-source code.
Al Sweigart’s Automate The Boring Stuff With Python is a great and interesting resource. However, we’ve compiled a comprehensive list of data science ebooks that you can download for free. Among the highlights are:
The Data Science Handbook – A fantastic collection of interviews with practicing data scientists that will give you a better understanding of what data science is all about and how you may excel in the profession.
Python Data Science Handbook – A comprehensive handbook that’s also available on Github in Jupyter Notebook format, allowing you to dive right in and try out all of the sample code.
Statistical Learning Elements — A comprehensive and recently updated statistics textbook that may be used as a reference while studying Python to ensure that your work is statistically sound.
SQL is a language that is used to communicate with databases in order to change, update, and restructure data. SQL is a common language in the data science industry, and we’ve created an entire post about why you should learn SQL if you want to work in the field.
Step 3: Become familiar with Python Data Science Libraries.
In Python, unlike some other programming languages, there is usually the best way to do something. NumPy, Pandas, and Matplotlib are the three best and most important Python libraries for data research. We’ve compiled a list of the 15 most significant Python libraries for data science, however, there are a few that are absolutely essential for any Python data work:
NumPy is a Python library that simplifies a range of mathematical and statistical procedures; it is also the foundation for many of the panda’s library’s capabilities.
pandas — A Python library designed to make dealing with data easier, pandas is the backbone of a lot of Python data science work.
Matplotlib is a visualisation package that enables creating charts from data simple and rapid.
The most popular Python machine learning library is scikit-learn.
NumPy and Pandas are excellent for data exploration and manipulation. Matplotlib is a data visualization package that allows you to create graphs similar to those found in Excel or Google Sheets.
Asking questions is a great way to get started with your study.
You have no idea what you don’t know!
Python has a large community of specialists willing to assist you in learning the language. People are eager to contribute their knowledge and assist you to learn Python programming on sites like Quora, Stack Overflow, and Dataquest’s learner community. We also provide a FAQ for each mission to aid you with any questions you may have during your Dataquest programming classes.
Step 4: As you learn Python, create a data science portfolio.
A portfolio is essential for prospective data scientists.
These projects should incorporate work with a variety of datasets and provide readers with valuable insights gained from your research. Consider the following categories of projects:
Data Cleaning Project – Because most real-world data requires cleaning, any project that involves cleaning and analyzing filthy or “unstructured” data will impress future employers.
Project on Data Visualization — Making appealing, easy-to-read visualizations is a programming and design challenge, but if you succeed, your research will be far more meaningful. A project with great-looking charts will stand out in your portfolio.
Machine Learning Project – If you want to be a data scientist, you’ll need a project to show off your machine learning capabilities. You could want to do a few distinct machine learning projects, each focusing on a different popular method.
Your findings should be presented clearly and visually, ideally in a Jupyter Notebook format so that technical people can read your code while non-technical people can follow along with the charts and written explanations.
A specific theme isn’t required for your portfolio. Find datasets that interest you and devise a method for combining them. If you want to work for a certain company or industry, however, include projects from that industry in your portfolio is a fantastic idea.
One of the appealing aspects of data science is that your portfolio serves as a résumé, displaying the abilities you’ve acquired, such as Python programming.
Start by communicating, collaborating, and concentrating on technical proficiency.
During this period, you’ll want to focus on developing the soft skills needed to collaborate with people, as well as ensuring that you fully comprehend the inner workings of the tools you’re utilizing.
Step 5: Implement Advanced Data Science Methodologies
Finally, strive to improve your abilities. Your data science journey will be filled with ongoing learning, but you may take advanced courses to ensure you’ve covered all the bases.
Regression, classification, and k-means clustering models are all things you’ll need to know. You may also use scikit-learn to get started with machine learning by bootstrapping models and building neural networks.
As part of programming initiatives, models based on live data feeds can now be created. This type of model adapts its predictions over time.
Data science is a rapidly expanding field that affects a wide range of sectors.
There are exponential opportunities to learn at the rate that demand is expanding. Continue to read, collaborate, and converse with others, and you’ll be sure to keep your interest and competitive edge.
What is the best place to learn Python for Data Science?
There are many Python learning resources available, but if you want to learn it for data science, it’s best to go to a place that specializes in data science.
Python is utilized in a wide range of programming disciplines, from game development to mobile apps, which explains why. Generic “learn Python” resources attempt to teach a little bit of everything, which means you’ll learn a lot of things that aren’t actually relevant to data science.
Furthermore, working on something that is unrelated to your objectives might be quite demotivating. It’s easy to become frustrated and quit a course that teaches you how to develop a game with Python instead of doing data analysis.
What is Python’s Role in Data Science?
Python and other programming languages are employed at every stage of the data science process. A data science project pipeline, for example, might look like this:
1 You build a query in Python and SQL to get the data you need from your company database.
2 You clean and sort the data into a dataframe (table) that is ready for analysis using Python and the pandas module.
3 You begin analysing, investigating, and visualising the data using Python and the pandas and matplotlib tools.
4 You use Python and the scikit-learn module to develop a predictive model that anticipates future outcomes for your organization based on the data you pulled after learning more about it through your inquiry.
5 You put your final analysis and model results into a communication format that your teammates can understand. Python is utilized at nearly every stage of the process!
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