The times today are changing at a supersonic speed with the advent of Artificial Intelligence & machine learning. Data science professionals assist newer technologies by figuring out solutions to problems by linking similar data for future use. One of the advantages of data science is that organizations can find when and where their products sell the best. Locating their preferred markets becomes easier with Data Science. It powers your business with the advantage of identifying the best-suited configuration for your business’s success.
Data science brings together the domain expertise from programming, mathematics & statistics to create insights & make sense of data hence gathered. Think of Advanced image & speech recognition, website recommendation, internet search, and many other tasks, think of a data scientist who makes it easier for you to reach your preferred goal. Data science simply adds value to any business that wishes to use its data well. International biggies like Musigma, Fractal Analytics & others work through a unique ecosystem that collaborates People, Processes & Platforms, alongside solving difficult business problems with contextual AI-powered insights & achieving Digital Transformation results.
Now you must be thinking, does Python has a role in data science? Honestly, Python provides greater functionality to deal with big data and has a faster learning curve that makes utilizing syntax easier and comprehensible. It increases the speed of processing data which is comparatively tough to do in Java or R languages. Data mining, data processing & modeling along with data visualization are the primary ways Python is utilized for data analysis.
Let’s talk about Why you should learn Python from Data science professionals:
Python is proven to be the most compatible programming language & supports multi-platform application development. There is a wide range of frameworks available for Python that are capable of cross-platform application development i.e. web, desktop, mobile or touch devices.
- DEMAND FOR PYTHON PROJECTS WORLDWIDE
Python is one of the third most widely used languages worldwide in data science as per TIOBE’s 2020 index. Many companies are using Python to build frameworks & projects. As an example, Google created Tensorflow, which is based on Python, FB & Netflix also rely immensely on it.
- DATA SCIENCE PROJECTS DEMAND PYTHON FOR DATA ANALYSIS
Projects namely Sentiment Analysis, Fake News Detection, Credit Card Fraud Detection, Chatbots & Customer Churn Prediction extensively utilize Python to obtain insights from a dataset for effective resolution.
- IT’S ROBUST, SCALABLE & POSSESS A VAST FRAMEWORK FOR GRAPHICAL REPRESENTATIONS
Python is scalable as it provides better structure & support for large programs than shell scripting. It can be used as a glued scripting language that integrates the existing components & helps you build a scalable application.
- WIDE RANGE OF LIBRARIES & PACKAGES FOR DATA MANIPULATIONS & STATISTICAL CALCULATIONS
Python Libraries for data science are popular & constantly evolving. Like- Keras is a minimalist library used for deep learning that was released in 2015, becoming a critical component of the Python library ecosystem since then. Libraries like Pandas, Statsmodels, NumPy, SciPy & Scikit-Learn are very popular in data scientists’ communities.
Having talked about the pivotal role of Python in Data sciences, it becomes imperative to talk about prestigious International Data science certifications that would let you lead the pack with a versatile professional role.
- USDSI™ DATA SCIENCE CERTIFICATION- If you’re an aspiring or accomplished data science professional or wish to advance your skill set providing a multitudinous growth in your data science career, the Data Scientist role is the one for you, and what better way of becoming one from a world-renowned certification from USDSI™, that provides the latest and industry-relevant data science certifications.
- MIT DATA SCIENCE CERTIFICATION– If your dream is to churn large volumes of data into actionable insights, the data scientist’s role is for you. This program comes to you with a bunch of benefits, to mention a few; a self-paced program with recorded lectures, imbibe industry-valued skills & a curriculum designed by world-renowned MIT faculty.
- STANFORD DATA SCIENCE CERTIFICATION- Vouched as one of the most sought-after international universities, Stanford is offering a variety of data science educational opportunities for graduates & undergraduates. This program is inclusive of informative workshops, strong data components & training programs.
All the above certifications are definitely constructed seeing the massive industry demand the role of data scientists in the future that ensures a promising professional growth rate as well.
Among significant data science skills, Python is definitely the one that every employer looks for in an applicant. Probably you could infer that it’s a prerequisite for a data science aspirant to be powered with Python language as well. Python is considered to be a must-have skill for all data science learners. Python developers are in high demand, not only because the language is popular & widely used but mostly due to the fact that Python became a solution in many different areas from web applications to data science & machine learning. Given its widespread use, it’s not surprising that Python has surpassed Java as the top programming language.
Nearing the close of this article, it becomes essential to focus on the key facts about the popularity of learning Python in order to become a successful Data science professional. This intelligent technical language provides great libraries to deal with data science applications. The major reason why Python is widely used in the scientific & research communities is because of its ease of use & simple syntax, which makes it easier to adapt for people who do not have an engineering background. Being a general-purpose language, people choose Python so that they can communicate with other people, also rooting through academic research & statistical models. As a result, even novices can easily use its relatively simple syntax to create effective solutions for complex scenarios with a few lines of code.