Why is the Data Science Industry demanding Python?

With its open source accessibility, user friendly interface and multi purpose integration as a full stack programming language – Python has become the programming language of choice for the data industry. Python is quickly becoming one of the most in-demand skills required for data scientists globally.

1. Why is Python in demand in data science?

Businesses across many industry sectors are realising the importance of deriving as much insight as possible from their data, creating a high demand for Python. This demand for Python in data science was born out of the programming language’s versatility to speed up data wrangling processes and efficiently meet business needs.

For Data Scientists, Python has grown in popularity because it is easy to teach, easy to learn and easy to use. Python is ideal for both big data beginners and also seasoned programmers looking to shift their careers to the data industry while adding to their programming proficiencies in languages like Java, C, C++, PHP or SAS. 

2. Businesses are choosing Python over other technologies to complete daily data tasks

Python can be classified as an all-purpose programming language that enables data professionals to quickly complete essential daily data tasks, which makes Python so attractive to businesses across industry sectors that are looking to hire data professionals. Python programming skills have become the resume mark of a true data scientist for recruiters and employers.

Here are the 3 main reasons businesses prefer Python over other technologies like Matlab, R, Java or C to complete daily data science tasks:

• Multi Purpose Nature – Python’s multi-purpose nature enables data professionals to perform data processing, statistics, mathematics, machine learning and visualisation tasks with speed and ease within one powerful programming environment.

• Free to Download and Free to Use – Python is open-source and its free standard libraries allow users to save time when designing solutions and testing products using Python’s data structures, analysis tools and modifiable source code.

• User Friendly – Python is considered one of the most user-friendly and object-oriented languages to learn for programming beginners and career changers because of its ease of use and supportive online community. For example, Python’s interface utilises easy to understand code with built-in data types and dynamic typing for accelerated prototype development, testing and implementation.

 3. How is Python being used in data science and machine learning?  

When it comes to picking a programming language in data science, it is always determined by the type of project you have been tasked with.

Python is currently most commonly used in industry when designing, testing and conducting automated machine learning projects and processes.

A data scientist or machine learning engineer would utilise Python when completing AI and machine learning projects involving sentiment analysis, natural language processing or predictive analytics to acquire useful trends and patterns from structured and unstructured data sets.

This is made possible through Python’s evolving and free for all library packages designed to make machine learning projects easier to develop, test and execute for data professionals.

Specific examples below illustrate how some of these Python libraries are used in industry for key tasks in data science related operations for every business including, data processing, analysis, manipulation, automation and machine learning:

• NumPy – used for numeric, image and text based data analysis
• SciPy – used for scientific computing
• Pandas – used for machine learning and advanced data wrangling
• Scikit-learn – used for machine learning, data visualisation, image / text data processing
• Matplotlib – used for data visualisation

4. Applications of Python in top data driven companies and the future of Python in data science

The data industry has grown to trust Python as a multi-purpose programming language.

The confidence and growth in Python users has developed as Python has proven its ability to adapt to the daily data demands of user focused companies. Python has enabled these companies to complete necessary data analysis, visualisation, automation and machine learning tasks quickly and effectively.

Here’s just some of the ways 3 of the top data fuelled companies are using Python:

• Netflix – Python is used by content creation and streaming service Netflix across the board by its data science and engineering teams for server side data analysis, visualisation and testing, predictive data analytics, automation for alerts and security, along with development and monitoring of real-time and internal operational processes. For example, Netflix’s personalised “you should watch this next because you watched …” playlists, use deep learning and predictive analytics algorithms to give you specific recommendations based on your individual behavioural data analytics.

• Facebook – Python is increasingly used by social media network Facebook for production engineering, infrastructure management and operational automation. To meet their critical user demand for real-time updates, usability and connectivity, Facebook acquired and implemented Python derived framework Tornado (now open source) which handles extensive web traffic with speed, providing users with an efficient real-time experience every time they sign in.

• Google – Python is used by tech giant Google as much as possible – including data analysis, testing and monitoring, automation and predictive analytics, web applications and development etc. Google also used Python to create the deep learning framework, TensorFlow, which is used for machine learning projects by companies across the globe.

The future of Python and data science is expected to maintain its powerful partnership due to Python’s commitment and focus to develop and regularly release updates that meet the demands of the data industry and emerging technologies. If the Python programming environment continues to advance its capabilities and versatility, it will continue to become the language of choice for data focused companies, projects and data professionals.

5. Learn Python to prepare for a successful career switch to data science

If you are interested in switching careers to data science, you can prepare yourself for success by becoming trained in the Python programming environment. The best way to do this is through an industry focused and recognised certification program such as the Institute of Data’s data science & AI vocational training courses. These programs teach you how to use Python to solve data related business problems and develop your practical Python skills to get you job ready for a career in the data industry.

Learning to use Python for data science will give you a competitive advantage in your data science career change and the best thing about Python is that it’s easy to use and easy to learn!

Some additional tools to prepare you for an intensive data science program designed to accelerate your data career are available. You can bulk up your Python muscles before starting your data science  training using the following resources:

• DataCamp – learn via interactive video modules
• CodeAcademy – learn via collaborative online modules
• The Python Tutorial – learn via comprehensive written modules

The ability to use Python for data processing, mathematical and statistical analysis, machine learning, automation and visualisation is quickly becoming the industry standard and the most sought after skill by recruiters and employers looking to hire data professionals across industry sectors.

Find out more about how you can gain a competitive advantage by learning Python for data science and machine learning with the Institute of Data: Book a career consultation today. Part-Time and Full Time industry programs available now.