Data Science has emerged as one of the hottest careers in recent years. It has been touted as the "Sexiest Job of 21st Century." In this post, we will explore what Data Science entails...
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Data Science is a budding interdisciplinary field that brings together Statistics, Computer Science, and any field to which the first two can be applied. Data scientists use the killer combination of Statistics and Computer Science to analyze and find patterns in any large swath of data, which gives invaluable insights to people in pretty much every field.
What is Data Science?
In simple terms, Data Science is the study of data that has been collected, recorded, organized, and stored in order to examine patterns in the data by applying mathematical and computational methods and the specific domain knowledge to which the data pertains.
The role of data scientists is to apply machine learning algorithms to different data types, including but not limited to numbers, texts, images, and videos, to create artificial systems that perform tasks which require human intelligence to record the pattern. These artificial systems help extract insights about the data and make future predictions simpler to drive decision-making in various situations.
Key Components of Data Science
Based on the definition of data science, it is clear that the following three pillars form the backbone of data science:
Mathematics and Statistics Expertise
Computer Science Skills
Business Knowledge
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These components are essential to work with data or to get insights from it. Having established that, let's dive into how these areas are important in data science.
Mathematics and Statistics Expertise
In order to find answers, you need more than just collection of data. With the help of statistics, one can not only better understand the underlying structure of the data and find connections between seemingly unrelated pieces of data, but also gain meaningful insights that can be applied to solve problems in a more targeted manner. Statistics also helps to present data in a more structured manner using data visualization tools such as line plots, pie charts and histograms.
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Furthermore, the creation and development of machine learning algorithms relies heavily on mathematics and statistics. A mastery of the fundamentals of these subjects is essential to be successful as a data scientist.
Computer Science Skills
The importance of computer science cannot be overstated in today's world, where we are all too aware that data volumes are currently at unprecedented levels and are only expected to grow in the years ahead. As a result, it is increasingly difficult to process data manually and deriving useful insights from data rapidly and accurately necessitates the use of specialized software.
To address this, many useful packages and libraries have been developed for various programming languages like Python and R that facilitate data collection, cleaning, analysis, visualization, and prediction. In order to quickly glean insights from data, data scientists must be proficient in the use of these packages.
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Business Knowledge
The mathematical portion of problem-solving is covered by statistics, and computer science aids in the creation of software and programs that can speed up and simplify the process. However, in order to determine whether the data-driven insights are accurate, it is important to have a thorough understanding of the industry and the problem at hand.
For example, banks collect data from customers in order to determine the loan amounts that can be sanctioned to them. However, banks need to know what type of field they should ask for to classify this problem before they begin collecting data. Only a data scientist with a solid business understanding of all the fields that can affect the classification, will be able to handle this.
To this end, businesses often employ subject matter experts from relevant fields to work alongside data scientists in identifying problems, defining requirements, and designing the entire data flow for a project.
Since data scientists can find work in many different fields, they must be prepared to learn the nuances of many different industries. What makes a great data scientist stand out is domain expertise, or the ability to show that they are well-versed in their chosen industry, can communicate effectively in the industry's language, and can aid a company in reaching its goals by identifying and solving the most pressing problems it is facing.
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Key Takeaways:
The goal of Data Science is to predict future outcomes by examining the behavior of collected data that has been recorded, organized and stored.
To be successful, Data scientists need expertise in mathematics and statistics, computer science, as well as the industry they are working in.
References
3 Key Components of the Interdisciplinary Field of Data Science.
Rebecca Nugent. TEDx Talks. (2017, May 19). We're All Data Scientists | RebeccaNugent | TEDxCMU [Video]. YouTube. https://youtu.be/YMnqPTLoj7o
Steyn, P. (2022, May 17). Why are statistics important in Data Science, Machine Learning and Analytics?
Why is Statistics Important in Data Science?