What is Data Science in Simple Words

March 15, 2021

Business People in a Meeting and Data Concepts

What is data science in simple words?

Data science is the study of data. It’s also a journey from unstructured to structured data. This is an interdisciplinary field that uses scientific methods, statistical processes, algorithms, and mathematical systems to extract knowledge and insights from structured and unstructured data. In other words, Data science is related to data mining, machine learning, and big data.

It involves developing methods of recording data, storing and analyzing it to extract useful information to improve the efficacy of an organization. Data science is very closely related to the field of Statistics, which includes the collection, organization, analysis, and presentation of data.

Data science incorporates skills from various fields such as computer science, mathematics, and statistics. Graphic design, information visualization, communication, and business are some other skills/fields. , drawing on Ben Fry, also Data science links to human-computer interaction according to Statistician Nathan Yau. The ability to Intuitively control and explore data is the essence of data science.

However, there is no consensus to date on the definition of data science. For several people, it is considered to be a buzzword.

What does a data scientist do?

Data scientists extract useful information by analyzing available and collected sets of data. These give deep insights for decision-making. Data scientists help organizations to solve annoying problems by extrapolating and sharing these insights. Making objective decisions is crucial for the success of any organization. With a good sense of business, data-scientists uncover the answers to major questions necessary for taking objective decisions. A data scientist combines computer science, modeling, statistics, analytics, and mathematical skills……. to reach the goal.

Table of Contents

A career in Data Science

Demand for data scientists is on the rise because of the massive potential of data science. They are highly sought after because they are deemed to have magical power. Organizations consider them as if they can create value from data for data-driven organizations. Hence, data science, as a career, is booming exponentially across the globe.

The internet, today, has provided an opportunity for almost everyone to become a data scientist. The beauty is that one doesn’t need a Ph.D. in Deep Learning or a college degree in Computer Science to be a data scientist. Companies and organizations consider you a data scientist if you can do what companies/organizations want you to do. You have to proffer valuable insights from data that help the organizations make decisions and grow.

What is the use of data science?

Data science, nowadays, is the leading field in computer science and information technology. It is the interdisciplinary branch in which data scientists can derive information from non-structured or structured data. It helps organizations to turn their concern into research initiatives. Further, these research initiatives can be converted into a realistic approach.

Data Science helps to shape an efficient world around us. It helps in identifying real-world problems and opportunities in an objective way. This identification helps governments, altruists, and companies to understand where exactly the action requires. The action taken, then, drives to a bigger impact. In short, data science is a field that takes an interdisciplinary approach to make sense of the data and extract actionable insights.

Data Science examples and Real-world applications

There is a wide range of uses and applications of Data Science in a real-world scenario. Some of the fields are as follow:

Search engines
Virtual assistants
Identification and prediction of disease
Optimizing shipping and logistics routes in real-time
Detection of frauds
Healthcare recommendations
Automating digital ads
Recommendations (Especially in eCommerce)
Autocorrect and autocomplete
Manufacturing industries etc.

Industries like education, banking and finance, eCommerce also use data science extensively. These are a few of the innumerable applications that changed everything forever and made life easier and comfortable. However, these are only examples; hundreds and thousands of fields use data science in ways beyond imagination.

Now, the technological world has been enjoying the ease and comfort with extensive uses of data science. In fact, there is enormous data generated and collected so far and is increasing exponentially. The situation calls for a better implementation of data science.

Big Data

The story of data science can’t be completed without referring to Big Data. The availability and interpretation of big data helped to alter the old business model. It has enabled industries to shape new models. It has also been helping in creating new industries. It is very quickly becoming a vital tool for governments, organizations, businesses, and companies of all sizes.

Data-driven businesses have already achieved about 300 percent growth in the last five years. It has grown from $333 billion in 2015 to $1.2 trillion collectively in 2020. It is obvious that big data will continue to have a major impact on the world in various ways and remain very important in near future. Moreover, there is a close relationship between data science and big data; and data scientists are instrumental in breaking down big data into usable information. This usable information initiates the creation of algorithms that help developing software. Governments, companies, and organizations determine optimal operations using that software.

Relationship to statistics

This is a debatable issue whether data science is statistics or something new. According to statistician Nate Silver, data science is not a new field, but only another name for statistics. However, many others do not agree with the point. They argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data.

There is a contrast between data science and statistics as per Vasant Dhar. Statistics emphasizes quantitative data and description whereas data science deals both with quantitative and qualitative data and emphasizes prediction and action.

Statistics is a nonessential part of data science, describes Vincent Granville, a data scientist, and Andrew Gelman, Columbia University. Several graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program.

Data science is not distinguished from statistics by the size of datasets or use of computing, says David Donoho, professor, Stanford University. He describes data science as an applied field growing out of traditional statistics. In a nutshell, data science can be described as an applied branch of statistics.

Data science, Machine learning, and Artificial intelligence

In the above paragraphs, we have learned about Data science, its applications, and its relationship with statistical methods. Data science is also closely connected to Machine learning and Artificial intelligence. Hence, we have to know about Machine learning to understand its relationship with data science.

so, what is Machine learning? Machine learning is a method of data analysis that automates analytical model building. It is a branch of Artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. As Machine learning is a method of Data analysis, an interrelationship with Data science is clearly established.

Machine learning is part of data science that draws aspects from statistics and algorithms to work on the data generated and extracted from multiple resources.

What happens most often is What happens if data is generated in massive volumes? It becomes a tiresome job for a data scientist to work on. Here, machine learning has to play its role. Machine learning is the ability given to a system to learn and process data sets autonomously without human intervention using complex algorithms and techniques like regression, Naïve Bayes, supervised clustering, and more.

Artificial intelligence and Machine learning are also connected very closely. Hence, there is a correlation among Data Science, Machine learning, and Artificial intelligence.

To elaborate, artificial intelligence, machine learning, big data, data science, deep learning are all closely interconnected. However, each has a distinct purpose and functionality.

Essential Data Science Ingredients

A data scientist or a developer needs several tools and platforms to work on Data science. These are prebuilt handy software that simplifies a data scientist or a developer’s work. What are these tools and platforms?