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30
Mar

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Do I Need To Learn Python Development For A Successful Career As A Data Scientist?

For a successful career in data science, you are going to need to learn at least one programming language.  Since two common languages are Python and R, a widespread question is “Which one should I learn? Python or R?”  Historically, there has been a fairly even split between the two languages. While R was built with a focus on mathematics and statistics, Python is a much better choice when it comes to overall work due to its versatility. 

What makes Python the preferred choice for data science?

To begin with, once you learn Python development, you would be able to transfer those Python skills more easily to other disciplines. Additionally, Python also comes with extensive support libraries. You’ll find that several high-use programming tasks have been already scripted into these libraries, which will reduce the length of code you need to write significantly, thus saving you a lot of time and effort.

Python has a comprehensive standard library which allows for easy programming of many common tasks in every step in the domain of data science  without having to install additional libraries.Let’s examine what a typical data science project workflow may look like:

Step 1: Suppose you need to pull some specific data from your company database. You’ll write a query using Python and SQL to get what you need.

Step 2: To get the data ready for analysis, you’ll need to clean and sort it into a DataFrame (table) for which you’ll use the Pandas – an open-source Python library. 

Step 3: You’ll typically use the Pandas and Matplotlib libraries to start data analysis, exploration, and visualization. 

Step 4: Once you learn more about the data via data exploration, you can use the scikit-learn library to make a predictive model that forecasts potential outcomes for your company based on the data you’ve already pulled and analyzed.

In the final step, you’ll organize your model results and the final analysis into a suitable format, which you’ll need to share with your coworkers or team leaders. 

Which Python Courses Should I Take?

Learning Python development for data science is different from learning it for programming. For instance, if you want to learn Python for data-crunching tasks, you’ll need to focus on NumPy and Pandas. The former offers the support of extremely optimized multidimensional arrays, which are the most fundamental data structures of a majority of ML (Machine Learning) algorithms. When it comes to manipulating data, the most popular Python library you’ll use in your data scientist career is the Pandas. You should learn NumPy first and then the Pandas, because the latter is an extension of NumPy. Also, its underlying code extensively uses the NumPy library.

Magnimind Academy covers Python extensively in our Data Science program, our 15-week course that teaches you all the skills you need to start a career in data. This course allows you to learn and implement your Python skills in real-life scenarios to build domain expertise.

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To learn more about data science, click here and read our another article.

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