Are you planning to learn Python to do data science, here’s how to master Python to use it for data science in 5 steps.
How to Learn Python for Data Science in 5 Steps
Python is the preferred programming language that data researchers prefer. While it's not the first primary programming language, it has gained popularity over time.
- In 2016 it surpassed R in Kaggle Kaggle, the most popular platform for data science contests.
- In 2017 it beat R on KDNuggets's annual survey of the most popular tools used by data scientists.
- In 2018 the year 2018, 66% of data analysts reported that they use Python daily. This makes it the most popular programming language used by professionals in analytics.
- In 2021 in 2021, it was ahead of Java as the top language on the TIOBE index and is currently the most used programming language.
Furthermore, data science course experts predict this trend to last.
What will the future job market appear to be for data scientists?
According to Glassdoor, Glassdoor reports that the average salary to be earned by the position of a data scientist in 2022 is $119,118.
This number is expected to increase as the demand for data scientists rises. In 2020, the number of data scientists was more than three times as many job openings for data scientists as in the prior year.
The future is positive in data science as well as Python. The good news is that learning Data Science with Python is now more straightforward than ever. Learn how to do it in just five easy steps.
How to learn Python to use it in Data Science
Step 1: Master Python basic concepts
Everyone begins somewhere. The first step is to understand Python programming fundamentals. (You'll also require an overview of the data sciences if you are not yet familiar with them.)
It is possible to do this through the help of an online Data Science with Python Course or data science boot camps, self-directed learning, or even university programs. There is no typical method for learning Python fundamentals. The most important thing is to choose the right path and remain in the same direction.
Locate the online community
To help you stay inspired to stay motivated, sign up for an online community. Most communities let you get answers to questions that members or other members ask members of the group.
It is also possible to connect with other community members and develop connections with industry professionals. This increases your chances of employment since employee referrals make up thirty% of all hiring.
Many students consider it beneficial to set up an account with Kaggle Account and be a part of a nearby Meetup group.
Step 2: Practice using hands-on learning
One of the most valuable methods to boost your education is to engage in practical learning.
Training with Python projects
Seeing how quickly you learn when building small Python projects could be surprising. It's a good thing that almost every online course includes a project to aid in your learning. Here are a few of them:
- Prison Break: Have a blast and then analyze a set of prison escapes by helicopter using Python and Jupyter Notebook.
- Profitable: App Profiles that can be used on Google Play Markets and the App Store, and Google Play Markets -In this project, which is a guided one, you'll be an analyst of data for an organization that develops mobile applications. You'll utilize Python to create value through the practical analysis of data.
- Investigating Hacker News Posts: Create a data set of posts submitted for Hacker News, a popular technology website.
- Searching for eBay Car Sales: Data Make use of Python to explore an aggregated database of used vehicles from eBay Kleinanzeigen, an area for classifieds of eBay. German eBay website.
- Make the rock game using paper, scissors, and paper.
- Create a game of adventure text
- Make a Guessing game.
- Create Interactive Mad Libs
Step 3: Learn Python data science libraries
The four most essential Python libraries are NumPy Pandas, Matplotlib, and Scikit-learn.
- NumPy - A library that can make many mathematical and statistical tasks more efficient; it is the basis for many aspects of pandas' library.
- Pandas - A Python library designed specifically for working with data. This is the foundation of lots of Python data science projects.
- Matplotlib - A visualization tool that allows you to be fast and simple to create charts using data.
- Scikit-learn -The most used software for machine learning in Python.
NumPy and Pandas are awesome to play around with and analyze data. Matplotlib is a data visualization tool that designs graphs similar to those that appear within Excel and Google Sheets.
This is a valuable reference to the 15 most essential Python libraries for Data Science.
Step 4: Develop a data science portfolio as you become proficient in Python
For students interested in data science, a portfolio is essential -and it's one of the most crucial things that hiring managers look at when looking for a suitable candidate.
The projects must involve working with various data sources, and each one should provide intriguing insights you've come across. Here are examples of projects that you can think about:
- Data Cleaning Project: The project that requires dirt and "unstructured" documents that you can clean and analyze will impress prospective employers since the vast majority of real-world data needs to be cleaned.
- Data Visualization Project: The art of creating appealing, easy-to-read visuals is both programming and a design problem. If you can accomplish it, your analysis will be significantly more valuable. The use of stunning charts in your project will help you stand out.
- Machine Learning Project: If you're looking to become a data scientist, you'll require an assignment showcasing your ML abilities. There are several different projects in machine learning that are focused on a unique algorithm.
Present your portfolio effectively.
Your analysis should be concise and easy to understand -best in a format similar to a Jupyter Notebook so a technical audience can comprehend your code. (Non-technical readers can read the chart as well as written explanations.)
Does your portfolio need a theme?
Your portfolio doesn't have to be tied to any particular subject. Look for datasets that appeal to you, and then create a strategy to connect the two. If you're looking to work for a specific company or in a certain industry, showing projects related to the particular industry is a great option.
These projects show prospective employers that you've invested the time to master Python and other essential programming skills.
Step 5: Implement modern data techniques from science
Also, you should improve your knowledge. The journey to data science is full of learning and development. However, there are advanced Python courses that you can attend to ensure that you've got all the basics covered.
Be confident with classification, regression models, and k-means clustering. It is also likely to enter the field of machine learning through studying bootstrapping and making neural networks using Scikit-learn.
Learn statistics for beginners and intermediate levels.
When you're learning Python to do data science, you'll need to acquire a solid understanding of statistics. Understanding statistics will provide you with the ability to be able to concentrate effectively to discover valuable insights.