Preface
Data science, as a discipline, did not have a sudden birth. It emerged out of the womb of computer science engineering. It was the development of new technologies, software, and advancements in DevOps that formed the baseline for the rise of this subject. However, the gradual catalysis of data science happened as the mantle of data started to expand beyond the existing capabilities. This is when we felt the need of an independent discipline that would improve our ability to crunch data and cater to the emerging juggernaut inflated by data.
Introduction
Computer science engineering can be deemed as a conventional branch of engineering. On the other hand, data science is a much more advanced and application oriented branch. This is the primary reason that most of the software engineers are migrating to the data science industry and taking up the role of data scientist. As per an estimate, a data scientist earns more than 35% extra salary as compared to a software engineering professional. In the age of the 4th industrial revolution, the need for reskilling has been in the limelight. This reskilling also involves the transition to a data scientist from other related fields. It would not be an exaggeration to conclude that data science has grown in prosperity and prominence and has overshadowed the popularity of computer science engineering.
Taking into consideration the job prospects and lucrative profile of data scientists, data science online courses in India have witnessed an upsurge. Credit goes to various online platforms that have played the role of active partners in this pursuit.
Data science vis-a-vis computer science: The broad spectrum
Data science incorporates all the features, aspects, subjects as well as the curriculum of computer science engineering. The difference is that it involves additional specialization in data science and also covers the basic areas of machine learning and artificial intelligence. Familiarity with software technology is central to both the disciplines. However, data science departs from the core of computer science engineering when it comes to interdisciplinarity. This is because data science is an application oriented field and is closely aligned to business analytics, business management and business operations. It also forms a strong overlap with data based calculations. It includes deep learning, statistics, operations research and other related fields. Moreover, data science is also a programming heavy field and involves the knowledge of python as a primary programming language and Java and R as secondary programming languages.
The stage of independence
From an appendage of computer science engineering, data science slowly progressed into new fields. Over a period of time, its scope increased enormously. This provided the necessary impetus for consideration of data science as an independent branch.
Data science went on to form its own mantle of knowledge that we discuss in the following subsections.
Linear regression and logistic regression
Regression is used to predict the natural outcome of an event in the future by extrapolating the results in real time. This means that regression involves establishment of a scalar relationship between a dependent variable and an independent variable. When the relationship is to be established in the form of continuous quantity, it is called linear regression. Similarly, when it is to be established between two variables to yield output in discrete terms, it is called logistic regression. When only one variable is involved in the computational process, it is called simple linear regression. On the other hand, when multiple variables are involved, it is called multiple linear regression. This means that multiple linear regression is used to predict correlation between numerous dependent variables.
Decision trees
As the name indicates, decision trees are used to execute a specific set of rules for arriving at a particular decision. These sets of rules can be formulated in the form of flowcharts and algorithms. The relevance of decision trees is extremely important from the application point of view. Decision trees find application in all those fields where complicated decision making is involved using a specific set of rules. For instance, it is used to decide whether any play is possible on a specific day by taking into consideration factors like weather conditions. Similarly, it is used in the financial system to decide whether a particular applicant should be given a loan or not. This may be decided on the basis of certain parameters like default history, income levels and mortgage.
Support vector machines
Support vector machines are such types of classifiers that help in building a model that assigns new entities a particular category. In this way, the support vector machine acts as a line of demarcation between various data sets on the basis of assigned parameters. Support vector machines were initially designed to perform classification in a linear manner. However, a support vector machine can also be used to perform non linear classification. This is done with the help of a technique called kernel trick.
Classification and clustering
Classification is a technique that allows us to group data into certain categories depending upon the selected parameters. Clustering is a technique that allows us to arrange data sets into specific classes or groups on the basis of matching attributes. While the former falls under supervised learning technique, the later falls under unsupervised learning technique.
Bayes' theorem
It is a mathematical treatment in machine learning that involves the usage of naive bayes algorithm for the purpose of classification. It is most suitable for cases that involve large data sets.
Contribution from data science
Although data science has evolved to develop into an independent branch, it has contributed to the growth of computer science in a holistic manner. In simple terms, data science has contributed by enlarging in the scope of computer science engineering. Computer Science Engineering now involves specialization in the form of data science and the combination has applications in numerous domains. For instance, the domain of digital marketing is dependent upon data science for customer targeting, product recommendation and brand positioning. Similarly, data science finds application in e-commerce, retail, logistics and the like. It is used to keep a track of financial transactions and thus helps in banking services. It is also used to collect and analyze large data sets in surveys like census and form groupings on the basis of age, sex ratio and other demographic features. It also finds application in the analysis of meteorological data.
The way ahead
There is no doubt in the fact that data science emerged from the mantle of computer science engineering. Over a period of time, it developed into an independent branch with a large number of applications and interdisciplinary linkages. In the present times, data science is expanding its scope and dimensions like data analytics, data management, data mining, business analytics and even social data science are gaining momentum.
In the coming times, we might expect the scope of data science to expand further, both in theoretical and practical aspects, and contribute towards the further development of computer science engineering. Moreover, the gradual development of data science studies and research would influence the growth of other disciplines like human-centered artificial intelligence, decision science, computer vision, human-computer interaction, cybersecurity, extreme machine learning, cognitive science, cloud computing and the like.
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