Data science is no longer a topic of the future, it is being implemented in the present. Multinational corporations and businesses are focusing on growing their business or influence worldwide. To do that they have opted for the time-saving idea of data accumulation. This technique involves gathering mountain loads of data in multiple segments and analyzing every piece of data to attain useful information, facts, and figures that can be utilized for further benefiting their businesses. This procedure includes various ways to gather and analyze data along with learning various programming languages. This article will solely focus on data science with Python programming and understanding the value of this programming language in the study and application of data science.
To be a data science professional, one has to be able to grasp the essence of various topics involving data science. For reference, data science involves statistical analysis, mathematics, artificial intelligence, and computer engineering. Using all of these topics, data scientists can derive meaningful conclusions or useful insights concerning various issues from improving productivity to enhancing efficiency. This is why, from brilliant minds to aspiring engineers everyone wants to shift their career to the domain of data science.
Opportunities derived from data science
1. Data analyst
Employees that are responsible for analyzing bulk loads of data from various databases and providing accurate data analysis are called data analysts. They perform specific tasks such as data retrieval, data system analysis, information recovery, and providing timely statements that can be used for improving business efficacy and production. Data science with Python programming is one of the few procedures that are taught by the instructors of various institutions and ed-tech startups that can help a certain individual gain exceptional professional hype and success.
2. Database administrator
As the name suggests, these types of employees play the role of database administrators. They are the people who are responsible for the safe operation and maintenance of several databases. Database admins can retrieve lost data and also segregate various types of data through the storing process. They have to have a watchful eye on all the databases as a moment’s lapse of judgment may very well result in the loss of crucial business data and data breaches.
3. Data engineers
Engineers with knowledge of making data analytics tools and systems that can safely gather, analyze, and organize bulk loads of data are called data engineers. Along with their peers, data engineers are vital for a company’s internal growth and efficiency. They are responsible for several tasks such as data acquisition, and developing algorithms that can process any number of data at any given time and provide results with high accuracy. Degree programs in data science with Python programming provide every necessary bit of knowledge available to students who wish to have a career path with numerous growth opportunities.
Statistician is the person responsible for planning various strategies according to the company’s needs. They are the ones who make various laws and regulations for their developers to follow from. They gather, scrutinize and translate data as per professional needs. They create accurate data accumulation procedures, predict trends using data analytics tools, etc. They are responsible for coordinating with other departments and groups to achieve the same goal with more efficiency. They form the direct link between their employers and their stakeholders. This makes the job role of a statistician extremely important as well as crucial.
Python – a brief illustration
Python is a computer programming language first implemented in the 1980s. Ever since Python was introduced to the world, it has been gaining tremendous positive responses from aspiring engineers to coders all around the world. The ideology revolving around Python is its coding readability and simplistic framework. This programming language reinforces structural, practical, and object-oriented programming. The versatile nature and easily understandable programming language raise its acceptability among engineering students, freshers, and even mid-career professionals. Apart from its acceptability, using data science with Python programming has its own set of advantages.
Easy to acquire and run!
Python is a computer programming language that is free to use. It is available for free with LINUX, the operating system. Python can be downloaded from open third-party sources for using it with the WINDOWS operating system. This programming language can run fluently even with the most average hardware configuration. IDEs required for operating Python are also easy to access and platforms like IntelliJ are also available for free.
The library of Python programming is huge and the variation in this programming makes it crucially versatile. The library is a collection of previously written codes for several factors of the program. Python displays features that are specifically in line with machine learning. For example, Numpy, Scikit-learn, Pandas, TensorFlow, Seaborn, Theano, Keras, PyTorch, and Matplotlib. These libraries are all tried and tested which makes the procedure more swift, flawless, and effective. The addition of crucial components in data analytics tools is not unheard of and Python is the most used and easily understood programming language of them all.
Python uses syntax that is easy to use and comprehend. The codes are constructed most simplistically because of the strong influence of human speech. Artificial intelligence and machine learning algorithms need complex coding and deep knowledge. Therefore, using a programming language that helps programmers to focus on the project at hand rather than focusing on the complexity of the language is the most logical choice. Python programming is largely intuitive and can provide a feasible solution for problems of grave impact. This makes it easy for programmers to communicate with other developers of the same field with more efficiency thus leading to a problem-free work environment.
This programming language is independent of the rest of the platform such as the codes can be written in a device and then applied on a different device. It doesn’t matter about the technological specifications, operating system, and device type. Python makes operating multiple devices using the same coding structure extremely easy. This enables the programmers to innovate without any restrictions and at their preferred schedule from any device or workspace that they choose fit.
Since its first introduction in the ’80s, python programming has caught the attention of millions of brilliant minds all over the world. The merger of data science with Python programming has witnessed noticeable growth in popularity. Industry experts around the globe are well-tested in this field and they can understand the level of hardships freshers have to endure initially. That is why, many online portals, forums, and discussion rooms are created by industry experts to help these newcomers understand the dos and don’ts of this field. Therefore, even a novice Pythonista will have no issue regarding Python programming for data science.
Above mentioned points are the few advantages that data science with Python programming has to offer. It is a quite lucrative opportunity as knowledge of programming languages comes in handy in the future when its need is elevated to a certain degree. Python is an easy-to-learn and easy-to-apply programming language that has a huge variety in terms of applications. This has elevated this language to huge proportions where every AI and ML expert strives to excel in this programming language.