Why is Learning Python for Data Science Career Important?

Learning data science skills can boost your career to a whole new level. The data scientist has ranked no.1 in Glassdoor’s list of the 50 best jobs in America. But as you may have noticed, good jobs don’t just fall out of the sky. You need to hone your technical skills to get a decent job in data science. Your knowledge of Python can determine where you land in the data science domain.

If the word “Python” gives you the image of programming codes instead of the mammoth reptile, you are already a few steps ahead of most people. As you may know, Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is used for analysis and computing of scientific data. Interestingly, it is one of the fastest-growing programming languages in the world, and Python for data science has become the norm.

While there are plenty of options for building data-intensive projects, most professionals in the field of data science prefer Python to the other options. Over the years, Python has evolved from a utility language to a major tool for artificial intelligence, machine learning, big data, and analytics. But before we discuss its importance in data science, it is important to learn the basics of this programming language.

Python: An Overview

Python has been around since the late 80s and has grown from strength to strength. Today, this high-level programming language is used in software development, mobile app development, web development, and the analysis and computing of numeric and scientific data. You’ll be surprised that all major online platforms, such as Google, Dropbox, Instagram, YouTube, and Spotify, were built with Python.

Python was initially meant to automate repetitive tasks, prototype applications, and implement those applications in other languages. Thanks to the clean and easy-to-understand code and extensive documentation, it is relatively simpler to learn and comprehend. What’s more interesting is that Python can also be used in non-technical fields like business and marketing, helping professionals with data analysis.

Moreover, Python is extremely user-friendly, even for beginners. To conduct data analytics, you won’t have to deal with cryptography or memory leaks. As long as you can clean, logical code in Python, you won’t have any problem fulfilling data analytics requirements. However, you will need proper knowledge of the underlying data structures available in Python to understand its significance in data science better.

An Introduction to the Data Structures in Python

Lists

Lists are a versatile data structure in Python. They are ordered sequences of objects separated by commas within square brackets. Lists can contain items of various types, but in most cases, all the items have the same type. The lists are mutable and can be extended or reduced as needed.

Strings

Strings are also ordered sequences that are defined by the use of single (’), double (’’), or triple (’’’) inverted commas. The strings outlined in triple quotes (’’’) are frequently used in docstrings, which are Python’s way of documenting functions. Also, the strings in triple quotes can span over multiple lines. “\” is used as an escape character. Since the Python strings are immutable, you cannot change part of the strings.

Tuples

Tuples are also ordered sequences represented by several values separated by commas. They are immutable, and parentheses surround the output to process the nested tuples properly. Moreover, tuples are considerably faster and demand less memory.

Sets

Sets are mutable and unordered sequences of unique elements. These sets share similarities with mathematical sets because they don’t duplicate values. Sets are enclosed in curly brackets.

Dictionaries

A Python dictionary holds key-value pairs. However, you cannot use an unhashable item as a key. The main difference between a set and a dictionary is that the dictionary holds key-value pairs instead of single values. The dictionaries are enclosed in curly brackets.

All these data structures have merits and demerits, so you must decide where to use them to get the best results. Also, when managing large data sets, you must dedicate significant time to “cleaning” unstructured data. Cleaning refers to handling the pieces of data that are missing values, have nonsensical outliers, or even have inconsistent formatting.

You can clean it by leveraging NumPy and Pandas. However, if you are interested in data science, you should not blindly install Python, as it can overwhelm you. The thousands of modules in Python may take you days to understand what tools you will need to engage in data analytics and then install them manually.

Tip: Use the Anaconda Python distribution to install most of your needs. You can install the rest through a GUI.

Importance of Python Programming for Data Science Career

Python certainly has a bright future in the data science field. And if you want a bright future in a data science career, you must know your way around various aspects of Python. Also, the data scientist community finds using Python with powerful tools like Jupyter Notebooks quite promising. It means you need to learn about that as well.

Interestingly, Jupyter Notebooks are easy to create and ideal for quickly running experiments. Moreover, Notebooks support multiple high-fidelity serialization formats that capture code, instructions, and results. The results can then be shared easily with other data scientists on the team or as open-source for everyone to use.

As you may realize, a data scientist’s primary duty is to develop AI or machine learning-based tools to produce predictive data. However, the question remains unanswered: “Why is Python essential for a data science career?”

We have already touched on various aspects of Python programming that answer the question. Let’s pinpoint those areas to provide you with a better understanding:

1. The flexibility

Python lets you develop software and work on mobile app development, analyze and compute numeric and scientific data, and web development. It has also become ubiquitous online, powering several high-profile websites with Web development frameworks like TurboGears, Django, and Tornado. Python is even making its way into cloud services.

If you want to try something innovative that nobody has ever tried before, then Python is the perfect fit for the job. It is ideal for developers with a knack for app and website development. No wonder most data scientists prefer this to the other programming options available.

2. Easy to comprehend and learn

Since Python is comparatively simple to understand and use, it is absolutely ideal for people who are just starting their data science careers. Python also lets programmers accomplish tasks with fewer lines of code than older languages. This feature allows data scientists to experiment with different things and spend less time dealing with codes.

Python is also tailor-made for carrying out repetitive tasks and data manipulation. Anyone who has worked with large amounts of data knows exactly how often repetition is involved. Since Python takes care of that repetitive work, data scientists are free to explore the rewarding parts of the job.

3. Open-source platform

Being an open-source language, Python is free and uses a community-based development model. Moreover, it is designed to run on Windows and Linux environments and can even be ported to multiple platforms, making it more convenient for data scientists.

Furthermore, plenty of open-source Python libraries exist for data manipulation, visualization, statistics, mathematics, machine learning, and natural language processing.

4. Seamless support

Usually, when you avail of a free service, there is a very small chance of getting assistance when things go wrong. However, things are different in Python. The large following of the programming language assignment and numerous available libraries facilitate the support required by the users whenever needed.

Python users can always rely on support points like Stack Overflow, mailing lists, and user-contributed code and documentation. The more popular Python becomes, the more supporting material is generated from users. Unlike most of the alternatives of Python, this programming language creates a self-perpetuating spiral of acceptance by the continuously increasing number of data scientists.

Conclusion

Python is more convenient for data scientists than a necessity, as it makes their jobs easier in various ways. However, Python is evolving rapidly, thanks to the growing support of the open-source community. As the language evolves, its importance within the field will grow simultaneously.

If you are thinking about learning Python, it can be an accessible path to programming and data science. For its versatility, Python is often called the “Swiss Army knife” of programming languages. Besides, data science professionals earn an average salary of $122,000 in the US and Canada. That’s a pretty good motivator.

Takeaways

• Python is the fastest-growing programming language in the world.
• Python is a versatile language that allows programmers to perform software development, mobile app development, web development, and data analytics.
• Python does not require cryptography, making it convenient for beginners.
• It can also help non-technical fields like business and marketing with data analytics.
• Initially meant for repetitive work, Python helps you easily handle large amounts of data. It also allows programmers to accomplish tasks in fewer lines of code.
• Python is an open-source programming language running on a community-based model.
• Understanding the data structures of Python helps comprehend its application better.
• More than a necessity, Python happens to be the most convenient choice for data scientists.