Five Steps to Learning Python for Data Science - Beginner’s Guide

Five Steps to Learning Python for Data Science - Beginner’s Guide Why should data scientists learn Python?

The preferred programming language for data scientists is Python. Although it wasn't the first primary programming language, its use has increased with time.

On Kaggle, the most popular website for data science competitions surpassed R in 2016.

Python was the most popular language among analytics professionals in 2018, with 66 percent of data scientists reporting using it daily.

What does the current data scientist job market look like?

A data scientist will make an average income of $119,118 in 2022, according to Glassdoor.

Both Python and data science look to have a promising future. As demand for data scientists rises, that number is only anticipated to climb. Fortunately, it's now simpler than ever to learn Python. We'll walk you through it in five easy stages.

How to learn Python for data science Step -1 Learn Python fundamentals.

Everyone has a beginning. Learning the fundamentals of Python programming is the first step. If you're not already familiar with data science, you'll also want to get acquainted with it.

The fundamentals of Python can be learned in any order. The secret is to pick a direction and stick with it. This can be accomplished through online Bootcamps, data science course, self-study, or academic courses.

Step-2 Practice with hands-on learning Hands-on learning is one of the finest methods to advance your knowledge.

Work on Python projects for Practice You might be surprised by how quickly you pick things up when you create simple Python programs. Thankfully, almost every Dataquest course includes a project to help you learn more. Some of them are as follows: Enjoy some fun while using Python and Jupyter Notebook to analyze a dataset of helicopter jail escapes.

Profitable App Profiles for Google Play and the App Store — In this supervised project, you'll perform data analysis work for a firm that creates mobile apps. Python will be used to add value through useful data analysis.

Examining posts on Hacker News Utilize a dataset of articles posted to the well-known technology website Hacker News.

Analyzing eBay's Car Sales Information Utilize Python to deal with a scraped dataset of used vehicles from eBay Kleinanzeigen, the German eBay website's classifieds area.

There is a tonne of additional beginner Python project ideas on this page as well: ● ● ● ●

Create a game of rock, paper, and scissors.

Make a text-based adventure game.

Construct a guessing game.

Create engaging Mad Libs

Alternative methods for learning and practicing Read manuals, blogs, Python tutorials, or other people's open-source code for fresh ideas to improve your coursework and find solutions to the Python programming issues you run across.

Step-3 Learn python data science libraries.

The four most important Python libraries are Scikit-learn, Pandas, Matplotlib, and NumPy. Several mathematical and statistical processes are made simpler by the NumPy library, which also serves as the foundation for many pandas library features.

Pandas is a Python package designed to make dealing with data easier. The mainstay of a lot of Python data science work is this.

A visualization package called Matplotlib makes it quick and simple to create charts from your data.

Step-4 Build a data science portfolio as you go.

A portfolio is a must for aspirant data scientists because it's one of the key qualities hiring managers to look for in a prospect.

These projects should include working with various datasets, and each one should present intriguing insights you found. Consider the following project categories:

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Data cleaning project- Since the majority of data in the actual world needs to be cleaned, every project you clean up and evaluate will impress future employers.

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Data visualization project-The ability to create appealing, simple-to-read visualizations is a programming and design challenge, but if you succeed, your analysis will be much more beneficial. Your portfolio will stand out if a project includes attractive charts.

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Machine Learning Project – If you want to become a data scientist, you must have a project demonstrating your ML proficiency. Several machine learning initiatives, each centered on a different algorithm, can be what you need.

Step-5 Apply advanced data science techniques.

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

To advance your data science skills, you can check out Learnbay, which offers industry-oriented data science courses in Mumbai. Gain the skills that are now in high demand and become a data scientist or analyst with IBM certification.