The origin of the discipline of data science dates back to the early 1960s. It has been around with diﬀerent names like ‘market research’, ‘data analyses’, and many others. Modern-day data science emphasizes on problems and techniques exclusive to digital data. Now, what exactly is data science? Why is it becoming ubiquitous by the passing day? Data Science is the science of analyzing data, both raw and secondary, using statistics and machine learning techniques in order to draw conclusions about that information.
Almost all contemporary industries adopt data science to help them with making informed business decisions. This can be in diverse areas such as risk assessment and monitoring, customer database analysis, tailoring personal preferences by understanding the target audience, improving product relevance by critically examining existing business processes, and testing of models/ theories in the sciences. Embracing data science implies traversing through its standard process of inspecting, cleaning, transforming, modeling, analyzing, and interpreting raw/unprocessed data. The terms ‘data science’ and ‘Python’ often go hand in hand. What is Python? When and why did it become the obvious choice for data science applications?
Python is a dynamic, interpreted and a general-purpose programming language with a user-friendly syntax, known for being dynamically typed and garbage-collected (a form of automatic memory management). Created by 'Guido van Rossum' and ﬁrst available to the public in the year 1991, Python has unique attributes and is easy to use when it comes to quantitative and analytical computing. It is an industry leader for quite some time now and is being widely used in various ﬁelds like oil and gas, signal processing, ﬁnance, healthcare, insurance, aerospace, retail banking and consulting services, to name a few. For instance, Python has been used to strengthen Google’s internal infrastructure and in building applications like YouTube, which is quite a feat for this ﬂexible and an open-source language. Python has vast number of libraries that are frequently used for data manipulation and are easy to learn, even for a novice in the ﬁeld of data science. Additionally, a wide number of data science and machine learning tutorials and resources are available online that are easily accessible to the common public.
Why is Python preferred over other data science tools?
Powerful and Easy to use – Python is considered a beginner’s language as it is an interpreted programming language with language constructs and an object-oriented approach that can be understood and replicated by a layman with basic programming knowledge. This structural methodology of the language helps debug codes faster resulting in a drastically reduced overall processing time and tackles common software engineering constraints with panache. In contrast to its contemporaries like C, C# and Java, the time for code implementation in Python is lesser, which helps programmers concentrate more on the algorithm-design.
Choice of Libraries – Python boasts of an extensive ecosystem of libraries that are robust and varied. These libraries
provide key feature sets that are crucial to data science. Some of the most popular libraries include Scikit Learn, TensorFlow, Seaborn, Pytorch, Matplotlib and many more. Python’s ‘pandas’ library oﬀers a variety of functions for data wrangling (pre-processing) and data wrangling processes. Libraries like ‘matplotlib’, and ‘seaborn’, common with the data visualization folks, aid in condensing statistical information and help in identifying trends and relationships. Machine learning libraries like ‘sci-kit learn’, oﬀer a bouquet of machine learning algorithms.
Scalability – As compared to its competitors in the ﬁeld, Python has proved itself to be a highly scalable and a faster
language (to prototype). The kind of ﬂexibility that it provides to solve unique, complex problems is unmatchable. Not only does it support both object-oriented and functional programming paradigms, but also provides supports reading data ﬁles from locally available drives, databases and the cloud. A vast majority of the companies hence employ it to develop rapid applications and tools of all kinds.
Visualization and Graphics – There are a wide variety of options for data visualization in Python. The aforementioned
‘matplotlib’ library provides a strong foundation (for plotting and visualizing the data) around which other libraries like ggplot, pytorch, and the like, have been built. These packages are designed to create charts, web-ready plots, graphical layouts, etc.
Adding to the stated advantages is Python’s tight-knit integration with big data frameworks like Hadoop, Spark, etc. and an expanding user community base.
According to a survey conducted nationally, open-source tools have been preferred over paid or custom-made tools by a vast majority of Indian data scientists as of the year 2018. Python has been touted as the most-used and a critical programming language for data analysis across the globe from an internationally conducted survey.
How Python is used in each stage of Data Science and analysis?
Stage I – The initial and the most common hurdle is to acquire the necessary data. As data is not always necessarily readily available, one needs to accordingly search for appropriate data from the web/ internet. Here, the libraries of Python – ‘Scrapy’ and ‘BeautifulSoup’ comes handy for extracting data from the internet.
Stage II –In the second stage, one needs to understand the type/ form of the data. If one comes across data in an excel
sheet with millions of rows and columns, then one ought to know the mannerism to deal with the massive data. Insights need to be derived by making use of appropriate functions and looking for a particular type of data in every single row and column. As is obvious, this entire process of computation could consume a lot of time and man-hours. This is where the libraries of Python – ‘pandas’ and ‘numpy’ prove beneﬁcial since they possess the abilities to quickly get the job done by using the concept of ‘parallel processing’.
Stage III – By now, one has the data processed and classiﬁed. Then comes the requirement to get the data visualized
or graphical presented, in order to be able to derive meaningful insights. It gets quite diﬃcult to interpret information with multiple numbers on the system monitor. The best way to handle this is by representing the data in the form of graphs, pie charts, and various other formats. This can be achieved by utilizing the ‘seaborn’ and ‘matplotlib’ libraries of Python.
Stage IV – The next and the ﬁnal step in the journey of data science is machine learning, which is a highly complex
computational technique. It involves mathematical tools like probability, calculus and matrix functions of over millions of columns and rows. The process of using machine learning techniques can be eased and made eﬃcient by using the ‘scikit-learn’ machine learning library of Python.
Python is also equipped to handle image processing operations as well. The open source library named ‘opencv’ is solely dedicated to catering to the area of image processing. In addition to the wonders Python is doing in the niche areas of data science, it is expected to have a potential impact in what will be the future of machine learning in data science – the union of artiﬁcial intelligence and advanced deep learning algorithms. Python is observed to provide a better support in the case of deep learning algorithms. Given the strong dependence on machine learning tools, even a minute headway in this ﬁeld will have a direct impact on the world’s outlook of data science, its values and capabilities. With Python’s unique selling proposition of supporting easier-to-use algorithms and its ﬂexibility in handling complex real-time problems and being vastly scalable, the future of data science certainly seems to be in safe hands.
With a deﬁnitive increase in the demand for both data scientists and data analysis, given the exponential growth in data, it will not be wrong to conclude that Python is undoubtedly the most versatile, user-friendly, open-source language with a strong community base that promises to make the lives of programmers, data managers and data scientists better with each passing day.
About the Authors
Kalaivany Kameshwaran holds a Master's degree in Biomedical Informatics from the prestigious University of Texas Health Science Center at Houston. She currently works as a Senior Data Scientist at GITAA Pvt. Ltd., an IITM incubated company. Her expertise is in the area of Health Data Science with a focus in Bioinformatics. She also possesses experience in electronic health records like Cerner and Athena.
Shayrin holds a Master's degree in statistics and currently works as a Junior data scientist at GITAA. She works on new material and case study development for training programs. Her passion for teaching has helped her mentor many students online.