Reasons to learn Python for Data Analysis

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Data Science is one of the hottest and exciting careers that offer tremendous growth opportunities! In today’s date, irrespective of the size of organizations, the insights extracted from the data are crucial since the organizations rely upon these insights for measuring progress, making decisions, planning the future of the organization, and a lot more. 

If you wish to start your career as a Data Scientist, data analysis is crucial, and along with that comes Python. Let us understand each term in detail and figure out why you need to learn Python for data analysis.

Data Science and Data Scientist

Data science is a field that extracts insights from several structural and unstructured data using scientific methods, algorithms, processes, and systems. Data science is associated with machine learning, data mining, and big data. It is a concept which merges the data analysis, statistics, machine learning to analyze and understand the actual scenario with the help of data.

Harvard called the profession of Data Scientist as the Sexiest Job of the 21st Century. A data scientist is a half mathematician, a half computer scientist, and a half trendspotter! The data scientist gathers and analyzes a large set of data that may be structured or unstructured. They work on the data, analyze it, and process the data to provide insights that will help create actionable plans for the company and its growth in the coming future.

Data Scientists stay on top of analytical techniques, including deep learning, machine learning, and text analytics. They Communicate and collaborate with both IT and business. Data scientists always keep a look for structure in the data; they also keep looking for trends that can help a company’s business prospect. The data scientists should be having a solid hold of statistics, including statistical distributions and tests. They solve business-related problems using data-driven techniques and work with various programming languages like Python, R, SAS, etc.

Python in Brief

Python is an object-oriented, high level, and interpreted programming language with dynamic semantics. Python is a programming language that is simple and easy to learn, whose syntax focuses on readability and reduces the program maintenance cost. Programmers can now write clear and logical code for large- and small-scale projects due to Python’s language constructs and object-oriented approach.

Python is garbage collected and dynamically typed. It keeps up multiple programming paradigms like object-oriented, structured, and functional programming. Due to its comprehensive standard libraries, it is described as a “batteries included” programming language. 

Python is one of the most learned programming languages in the world. It is, with no doubt – widely used by data scientists. Python is an optimal choice for beginners since it is a dynamic language – easy to read and learn. Python can interface with high-performance algorithms written in Fortran or C and also enables quick improvements. The demand for skilled Python experts is on the rise, grab your Python for data science course today!

Why is Python preferred over any other tool for data science?

One of the top tools used by the data scientists across the industries is Python. This programming language is a choice of many data scientists for the daily tasks that they tackle. Python is an ideal choice for data scientists who integrate data with web applications or include statistical code into the production database. Implementing algorithms is something that data scientists often need to work on; Python can be used for it.

Numpy, Pandas, and SciPy are the python packages that are explicitly tailored for specific functions. Scikit-learn is a valuable tool for the data scientist who works on machine learning-related tasks. When it comes to requiring graphics and other visuals, MATPLOTLIB, another package in Python, is the perfect solution for the data scientists. The Python for data science course has covered everything that you need to know about Python and provides hands-on experience with an opportunity to work on industry-level projects.

One of the reasons why learners choose Python for data science is – anyone right from a beginner in Python, or a coding enthusiast who has no prior knowledge can also learn Python quickly. Being easy to understand and learn is one of the most appealing qualities of this programming language.

Python excels when it comes to scalability, while other languages like R cannot. It is faster than languages like Stata and Matlab. The data scientists get flexibility and multiple ways to approach various problems. This is the reason why YouTube migrated to the Python programming language. Python can be found across many industries, generating the rapid development of applications for all types of uses and causes. 

The popularity of Python is the direct result of its community. The data science community using Python keeps on growing. Thus the new data science libraries are created by the users on large numbers. It leads to the creation of the modern tools and strengthened processing techniques available in today’s date. This is why people prefer Python for data science.

The cherry on the top – Python comes with many graphics and visualization options. Pandas plotting, seaborn, and GGPLOT are few libraries built across the solid foundation provided by the Matplotlib. These packages that support visualization are used to create web-ready interactive plots, charts, graphical plots, and, most importantly, to make sense of data.

As the Bureau of Labor Statistics reported, there is no doubt that the current job market is very competitive. If you are looking forward to laying your career in a stable industry, which allows growth in terms of knowledge and success, which keeps the pocket happy at the same time, then data science is the right choice for you. 

However, choosing an industry successfully and getting happy is just half the battle if looked keenly. There are multiple factors like competition, an important one to be kept in mind while applying for data science because it is not surprising that along with you, there are thousands of other applicants who are your competitors! Having the right credentials is something that will help you stand out from a crowd of applicants. Getting your certification done with Python for a data science course will surely make your resume noticeable since you will be among the skilled and trained individuals. Good luck with your data science journey!

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