Why Should Every Data Scientist Know Python?

Are you planning to move into data science and wondering whether you should learn Python? Do you want to know why Python is so popular in data science? This article explains why learning Python is important for data scientists and provides tips and resources for learning.

Python is the most common programming language among data scientists. If you are planning to work as a data scientist, there is a great chance you need to work with it. Python, in data science, is essential.

You may be wondering why if you are new to data science. Data science seems to have more to do with statistics and business from the outset and little with programming. What are the uses of programming in this role?

You may also be wondering: why Python in data science, specifically? There are other great scientific programming languages like R, MATLAB, and Julia. What has made Python so successful compared to these?

Then, if you plan on learning Python for data science, it is hard to decide what to learn and where to start. The ecosystem of Python in data science is already huge.

So, we give you in this article an overview of data science and the reasons behind the popularity of Python for data scientists. It also provides resources to help you decide where to start and what you can use immediately.

If you are eager to start learning, our Python for Data Science track teaches the fundamentals of Python you need in a data science role. After picking up the core of Python, you learn to handle string data and work with the most common data formats in data science (Excel, JSON, and CSV). The learning track consists of 5 distinct Python courses and more than 300 interactive coding challenges.

Read on if you want to understand Python's relevance in data science.

What Is Data Science?

Data science is a professional field aiming to extract value from data by analytical means. It is not an entirely new discipline, but it has become popular in the last ten years.

Organizations started to seek data scientists because of the increased volume of data available and the rise of effective tools to manage and analyze it. Data scientists generate value using data to produce insights and build data-driven products and services.

Specific tasks and methods for data scientists are not always clear-cut due to the novelty of the profession and its evolving business and technology landscape. The problems data scientists address change depending on the industry and use case, and we have seen different roles promoted under the same "data science" umbrella.

One way to understand the role of the data scientist is to differentiate it from other roles such as data analysts, database analysts, data engineers, machine learning engineers, and analytics engineers. Data scientists apply analytical methods to data and are less concerned with data storage and management or the model lifecycle than their engineer counterparts. In contrast to analysts, data scientists often use programming to produce computational solutions (e.g., machine learning models) for their analytic problems.

Here is a list of common tasks data scientists do in their day-to-day work:

• Researching and understanding datasets.
• Collecting data from external sources.
• Cleaning and preparing datasets for analysis.
• Getting insights from data by producing metrics, descriptive statistics, and visualizations.
• Producing reports, reporting pipelines, and dashboards.
• Extracting complex insights with statistical means.
• Building statistical models for predictive or data mining purposes.

Data science has been a hot topic since the pandemic, and it seems likely to remain so in the future. The amount of data we produce is growing exponentially, giving rise to more novel use cases. The salaries data scientists get also reflect this demand.

The history of the interest in the "data science" keyword in Google (source)

If you want to advance your career, learning skills for data science like Python may be a good thing.

Why Python Is Popular in Data Science

Python is the main coding language data scientists typically use daily.

The creator of Python, Guido van Rossum, started developing Python at the beginning of the 1990s. The main principles he followed in its design were accessibility, multi-paradigm support, and modularity. He made the project open source to achieve these goals and created a clean, English-like syntax useful for everyday tasks.

These design principles made Python highly popular in the industry as well as academia and one of the world's most widely used programming languages with many advantages.

Python is one of the easiest languages to learn for a beginner. Its syntax is simple and easy to understand. But despite its simplicity, its rich ecosystem of libraries allows users to build useful applications in a relatively short time.

Python's flexibility also keeps it from being constrained to a particular use case like web development, statistical analysis, or scripting. You can use Python in any of these areas and many more! This has earned Python the nickname "the second-best language for everything."

Source

Python also suits well in working with data, data visualization, and other areas of data science because of its strong selection of data science libraries.

As Python has been commonly taught in universities, many researchers have produced their modeling libraries in Python and have made it publicly accessible. Because the language follows software development principles, these libraries are easy to integrate into industry-ready applications.

Python is a great choice as a first programming language to learn especially if you are planning to move into data science. Once you pick your favorite code editor, you can start coding immediately.

Learning Python for Data Science

There is a wide range of resources for learning Python. You can start by reading blog posts or digging into books.

However, it is easy to get lost in the weeds because of Python's flexibility and wide range of use cases. You soon arrive at "analysis paralysis," facing too many options and failing to commit for the fear of choosing the wrong path. Also, the Python ecosystem is constantly growing, and while learning all of its details is fun, they do not necessarily align with what employers are looking for in a job interview.

Avoid these problems by following a structured and focused approach, picking up useful skills that are quickly applicable now and in the future. A great way to do this is with practical projects in which you solve data science problems. It gives you an all-around experience and allows you to build your portfolio. Find project ideas here or in this article.

We have developed our courses on LearnPython.com with these principles in mind. There are resources in our Python courses for any stage of your data science learning process:

• If you are a beginner, start learning Python with our Python Basics learning track. It teaches you how computers work, the fundamentals of programming, and the basic data structures of Python.
• The Learn Programming with Python course is available for complete beginners but covers more topics. It teaches you data structures and algorithms; you learn about the fundamental problems of computer science like how to make programs fast and memory efficient.
• If you already know Python but want to boost your confidence level, make your skills more fluent by doing lots of practice exercises. In this short course, you challenge yourself by solving programming puzzles and sharpening your skills with edge cases.
• Or, if you want to go straight to using Python in a data science setting, check out our Introduction to Python for Data Science You can take this course without any programming experience. The course teaches you the Python fundamentals you need to start a data science project. It goes through the main tasks you face as a data scientist, like loading and cleaning data, transforming tables, doing calculations, and visualizing your results.

Our courses are interactive and organized around projects. They make you write real Python code and solve business problems from Day One. The curriculum helps you acquire the foundations of Python for use at work and in training.

Start Learning Python to Solve Data Science Problems!

We have given you an overview of Python in data science and the reasons behind Python's popularity in the profession. We've provided tips and resources for your learning journey. The next step is yours!

Check out our articles if you want to learn more before jumping on a course. We cover career prospects with Python, how to learn Python, our Python courses, and the use of Python in data science, among other topics.

Want to start your data science journey and solve problems with data and Python? Enroll in our learning track "Python for Data Science." See you there!