With its open-source accessibility, user-friendly interface, and multi-purpose integration – Python has become the programming language of choice for the data industry. Python is becoming one of the most demanding skills needed by data scientists worldwide.
- Why is Python Demand for Data Science?
Businesses in many industry sectors are realizing the importance of getting as much insight as possible from their data, creating a high demand for Python. This demand for Python in data science stems from the mastery of the programming language to speed up the complex processes of data and efficiently address business needs.
For data scientists, Python is popular because it is easy to teach, easy to learn, and easy to use. Python is for big data beginners and experienced programmers looking to transfer their careers to the data industry, combining their programming expertise in languages such as Java, C, C ++, PHP, or SAS.
- Businesses are choosing Python over other technologies to accomplish daily data tasks
Python can be classified as an all-purpose programming language, which enables data professionals to quickly complete the required daily data tasks
What makes Python so appealing to businesses across the industry is that they want to hire data professionals. Python programming skills have become a real data scientist’s resume initial mark for employers and owners.
Here are 3 main reasons businesses prefer Python over other technologies such as Matlab, R, Java, or C to perform day-to-day data science tasks:
Multi-Purpose Nature – Python’s multi-purpose nature enables data professionals to perform data processing, statistics, mathematics, machine learning, and visualization tasks in a fast and easy-to-use 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 and testing solutions using Python’s data structures, analytics tools, and editable source code.
User-friendly – Python is considered one of the most user-friendly and object-oriented languages because it is easy for programming communists and career changers to learn because of its use and supportive online community. For example, Python’s interface is easy to understand code with built-in data types and dynamic typing for quick prototype development, testing, and implementation.
- How is Python used in data science and machine learning?
When it comes to selecting a programming language in data science, it depends on the type of project you are assigned.
Python is now widely used in the industry when designing, testing, and operating automated machine learning projects and processes.
A Data Scientist or Machine Learning Engineer completes AI and Machine Learning projects to achieve useful trends and models from structured and unstructured data sets, including Emotion Analysis, Natural Language Processing, or Predictive Analysis.
Python is freely developed for development and machine library projects, making it easy to develop, test, and implement machine learning projects for data professionals.
The specific examples below illustrate how some of these Python libraries are used for important work in data science-related work for every business in the industry, including data processing, analysis, manipulation, automation, and machine learning. Included:
- NumPy – used for numerical, image and text-based data analysis
- SciPy – used for scientific computing
- Pandas – used for machine learning and advanced data
- Skikit-Learn – Used for machine learning, data visualization, image/text data processing
- Matplotlib – used for data visualization
- Python’s applications in top data-driven organizations and the future of Python in data science
The data industry has grown to rely on Python as a multi-purpose programming language.
Confidence and growth among Python users have grown as Python has proven its ability to adapt to the day-to-day data demands of user-centric companies. Python has enabled these companies to quickly and efficiently complete the data analysis, visualization, automation, and machine learning tasks they need.
Here are 3 of the top data energy companies that Python uses:
Netflix – Python is used for Netflix automation and development through content creation and streaming service, and real-time monitoring by its data science and engineering teams for server-side data analysis, visualization and testing, predictive data analytics, alerts, and security. Time and internal operating procedures. For example, use Netflix’s personalized “you should see it because you see it …” playlist, deep learning and predictive analytics algorithms to give you specific recommendations based on your personal behavior data analytics.
Facebook – The social media network Facebook uses Python for production engineering, infrastructure management, and operational automation. To meet its critical user demand for real-time updates, usability, and connectivity, Facebook has purchased and implemented the Python-derived framework, Torado (now Open Source), which speeds widespread web traffic, delivering an efficient real-time experience to customers every time they sign up. In.
Google – the Python tech giant – includes data analysis, testing and monitoring, automation and predictive analytics, web applications and development, and more. Google used Python to create a deep learning framework, Tensorflow, for which it was used. Machine learning project by companies around the world.
The future of Python and Data Science is continuing its powerful partnership due to Python’s commitment and focus on meeting the demands of the data industry and emerging technologies and releasing updates regularly. If the Python programming environment develops its capabilities and versatility, it will continue to be the language of choice for data-focused companies, projects, and data professionals.
- Learn to prepare Python for a successful career transition to data science
If you’re interested in turning your career into data science, you can train yourself in the Python programming environment. An industry-focused and accredited certification programs such as the Data Science and AI Professional Training Courses of the Institute of Data is the best way to do this. These programs will teach you how to use Python to solve data-related business problems and develop your practical Python skills to prepare you for a career in the data industry.
Learning to use Python for data science gives you the competitive advantage of a change to your data science career, and the best thing about Python is that it’s easy to use and easy to learn!
There are a few additional tools available to prepare you for an intensive data science program designed to accelerate your data career. You can stretch your Python muscles before beginning your data science training by using the following resources:
DataCamp – Learn through interactive video modules
Code Academy – Learn through collaborative online modules
Python Tutorials – Learn through extensive written modules
The ability to use Python for data processing, mathematical and statistical analysis, machine learning, automation, and visualization is fast becoming the industry standard and there are many skills that recruiters and recruiters can use to hire data professionals in 2020.