Why Learn Python For Data Science?

by Sarfaraz K. Just a single word has no impact but a bunch of wo

Before we get into the details of learning Python for data science, let's briefly discuss why Python is important.

Understanding Python is a key skill for data science careers.

Although it isn't always the preferred programming language for data science, Python is still the most popular. Here's a quick history:

In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

In 2017, it overtook R on KDNuggets's annual poll of data scientists' most used tools.

In 2018, 66% of data scientists reported using Python daily, making it the number one language for analytics professionals.

Experts in data science expect this trend will continue, with the Python ecosystem growing. Even though your journey to Python programming is just beginning, it's good to know that there are many job opportunities available.

According to Indeed, the average salary for a Data Scientist is $121,583.

Good news! The good news?It is expected that it will continue growing. There will be a total ofthree timesQuanthub reports that there are more job listings in data science than job searches for data scientists. This means that the demand for data scientiststs is far greater than the supply.

Data science is a promising field, and Python is only one part of that future. Learning Python and other programming basics is easy. In five easy steps, we'll show how to do it.

Remember that the steps may seem simple, but you will still need to work hard. You can learn Python if you put in the effort and are willing to dedicate time to it.

How to learn Python for Data Science

You'll first need to choose the right course for you to learn Python programming. Edureka's data science python course  is specifically designed for you to learn Python for data science at your own pace, challenging you to write real code and use real data in our interactive, in-browser interface.

You should not only learn Python in a classroom setting. Your journey to become a data scientist must also include soft skills. We recommend that you also learn some complementary technical skills along the way.

Step 1: Learn Python fundamentals

Everybody starts somewhere. This is the first step in Python programming basics. An introduction to data science is also recommended.

One of the important tools you should start using early in your journey is Jupyter Notebook, which comes prepackaged with Python libraries to help you learn these two things.

Kickstart your learning by: Joining a Community

Joining a community will allow you to meet like-minded people, increase your chances of employment, and create a network. According to the Society for Human Resource Management (SHRM), employee referrals account for 30% in all hires.

Register for a Kaggle account and join a Meetup group in your area. You can also participate in Edureka's learner network with alumni and current students.

Step 2: Practice Mini Python Projects

We believe in hands-on learning. You may be surprised by how soon you'll be ready to build small Python projects. We've already put together a great guide to Python projects for beginners, which includes ideas like:

Tracking and Analyzing Your Personal Spending Habits -- A fun project that'll help you practice Python and pandas basics while also giving you some real insight into your personal finance.

Analyze Data from a Survey -- Find public survey data or use survey data from your own work in this beginner project that'll teach you to drill down into answers to mine insights.

Try one of our Guided Projects -- Interactive Python projects for every skill level that use real data and offer guidance while still challenging you to apply your skills in new ways.

This is just the tip of an iceberg. Programming can be used to create online games that use calculators, or programs that retrieve the weather data from Google for your area. To help you get comfortable with Python, you can create simple apps and games.

These mini projects will help you to learn Python. These programming projects are a standard feature for all languages and a great way of solidifying your knowledge.

You should start to build your experience with APIs and begin web scraping. Web scraping is useful in helping you gather data later.

Step 3: Learn Python Data Science Libraries

Python is a programming language that allows you to do things in a better way than other languages. NumPy and Pandas are the three most important Python libraries to data science.

We've put together a helpful guide to the 15 most important Python libraries for data science, but here are a few that are really critical for any data work in Python:

NumPy -- This library makes it easier to perform a wide range of mathematical and statistical operations. It is also the foundation for many of the features in the pandas library.

pandas -- This Python library was created to make it easier to work with data. It is the foundation of many Python data science projects.

Matplotlib -- An visualization library that allows you to quickly and easily create charts from your data.

scikit-learn -- The most widely used library for machine learning in Python.

Pandas and NumPy are great tools for playing and exploring data. Matplotlib, a data visualization library, creates graphs just like in Excel or Google Sheets.

Step 4: As you learn Python, create a Data Science Portfolio

For aspiring data scientists, a portfolio is a must.

These projects should involve work with multiple datasets, and should provide interesting insights for readers. Here are some examples of types of projects you might consider:

Data Cleaning Project -- You will be able to impress potential employers if you can clean up or analyze dirty data.

Data Visualization Project -- Creating attractive and easy-to-read visualizations can be both a programming challenge as well as a design challenge. However, if you do it well, your analysis will be much more effective. Your portfolio will stand out if you have great looking charts.

Machine Learning Project -- If you aspire to work as a data scientist, you definitely will need a project that shows off your ML chops (and you may want a few different machine learning projects, with each focused on your use of a different popular algorithm).

Step 5: Apply advanced data science techniques

Finally, you should strive to improve your skills. Data science is a continuous learning journey. However, there are advanced courses that you can take to make sure you have covered everything.

Regression, classification, and k means clustering models are all important skills. Machine learning is also possible - creating neural networks with scikit-learn and bootstrapping models.

Programming projects may include the creation of models with live data feeds. These machine learning models adjust their predictions as they go.

Keep these things in mind Continue learning!

Data science is a rapidly-growing field that covers many industries.

There are endless opportunities to learn at the speed that demand is rising. Keep reading, collaborate, and communicate with others to keep your interest high and give you a competitive edge.

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About Sarfaraz K. Advanced   Just a single word has no impact but a bunch of wo

26 connections, 0 recommendations, 110 honor points.
Joined APSense since, August 5th, 2019, From Mumbai, India.

Created on Aug 26th 2021 07:02. Viewed 310 times.


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