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How to Learn Python for Data Science - Explained in 5 Steps

What is Python for data science?
Python, a high-level, open-source, interpreted language, offers a good approach to object-oriented programming. Data scientists utilize it as one of the best languages for various projects and applications, whereas Python programming has excellent capabilities for working with mathematical, statistical, and scientific functions.
Why should data scientists learn Python?
The preferred programming language for data scientists is Python. It wasn't the first significant programming language, but its use has grown through time.

In 2016, Kaggle, the most popular website for data science competitions, overtook R.
On KDNuggets's yearly survey of the most-used tools by data scientists, it surpassed R in 2017.
Python was ranked as the top language for analytics experts in 2018, with 66% of data scientists reporting using it every day.
On the TIOBE index, it surpassed Java in 2021 and has since taken the top spot among programming languages.

What does the job market for data scientists look like right now?

According to Glassdoor, the average income for a data scientist in 2022 will be $119,118. That number is only expected to rise as the need for data scientists does. Data scientists had three times as many available positions in 2020 as in 2019.

Both Python and data science appear to have a very promising future. Now, the process of learning Python has never been easier. It will be explained to you in five simple steps.

Python Training for Data Science
Step 1: Learn Foundations of Python
Everyone has a beginning. The first step is to learn the foundations of Python programming. You can learn this through online courses, data science boot camps, independent study, or academic programs. You can study Python's foundations in any order. The key is to choose a course and stick with it. You can also network with other community members and develop relationships with industry experts. Additionally, since 30% of all hires come from employee referrals, this boosts your chances of finding work.

Step 2: Practice and experiential learning
When you write simple Python applications, you might be amazed at how quickly you pick things up. In order to help you learn more, practically every Dataquest course includes a project. Some of them are as follows:

Prison Break:
Have some fun while using Python and Jupyter Notebook to examine a dataset of helicopter prison breaks.
Profitable profiles for apps:
In this supervised project, you will carry out data analysis work for a business that develops mobile apps. Python will be utilized for practical data analysis to add value.
Exploring Hackers' new post:
Use a dataset of articles that have been posted to the renowned technology website Hacker News.
Explore eBay sales data:
Use Python to manage a scraped dataset of used cars from eBay Kleinanzeigen, the classifieds section of the eBay website in Germany.

Check out the comprehensive data science certification course to build impressive data science projects with experts.

Step 3: Study the libraries for data science in Python:

The four most important Python libraries are NumPy, Pandas, Scikit-learn, and Matplotlib.

NumPy:
It is a library that makes many mathematical and statistical operations easier to perform and forms the basis for many of panda's library features.
Pandas:
A Python module designed to work with data in mind. This is the cornerstone of a lot of Python data science work.
Matplotlib:
A visualization library makes making graphs from your data quicker and easier.
Scikit-learn:
Python's most widely used machine learning library.

Step 4: Create a Portfolio

Aspiring data scientists must have a portfolio because it's one of the essential characteristics hiring managers look for in a candidate.

Each of these projects should showcase intriguing insights you discovered while working with a variety of datasets.
Consider the following project categories:

Data Cleaning project:
Since the majority of data in the real world needs to be cleaned, every project you clean up and evaluate will impress future employers.
Data Visualization project:
Programming and design skills are required to produce visually appealing, easy-to-read visualizations, but your analysis will be considerably more useful if you are successful. Including appealing charts in a project will make your portfolio stand out.
Machine Learning project:
You will undoubtedly require a project demonstrating your machine learning (ML) abilities if you want to work as a data scientist. You may require a number of machine learning projects, each focused on a distinct algorithm.

Step 5: Use cutting-edge data science methods:

Finally, develop your abilities. Although learning new things will be continuous in your data science journey, there are advanced Python courses you can take to ensure you've covered everything.
Gain confidence with the k-means clustering, classification, and regression models. You may also begin with machine learning by learning about bootstrapping models and creating neural networks with Scikit-learn.

Where can I get a Python data science course?

There are several available free Python for data science courses. Numerous tutorials are offered online arranged by difficulty level and subject matter. You can take a data science course with placement to advance from a newbie to a job-ready data scientist.

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