Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data both structured and unstructured.
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It is related to big data and data mining. Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science and asserted that “everything about science is changing because of the impact of information technology” and the data deluge. In 2015, the American Statistical Association identified database management, statistics, and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.
Data science is related to computer science but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.
Because of the large amounts of data, modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.
Why data science is important?
Without the expertise of professionals who turn cutting-edge technology into actionable insights, Big Data is nothing. Today, more and more organizations are opening up their doors to big data and unlocking its power increases the value of a data scientist who knows how to tease actionable insights out of gigabytes of data.
It is becoming clear by the day that there is enormous value in data processing and analysis and that is where a data scientist steps into the spotlight. Executives have heard of how data science is a good industry, and how data scientists are like modern-day superheroes, but most are still unaware of the value a data scientist holds in an organization. Let’s take a look at the benefits of data science.
Data Science vs Data Mining
Data science is often confused with data mining. However, data mining is a subset of data science. It involves analyzing large amounts of data (such as big data) to discover patterns and other useful information. Data science covers the entire scope of data collection and processing.
The field of Data Science is massive and has its fair share of advantages and limitations. So, here we will measure the pros and cons of Data Science. This article will help you to assess yourself and take the right course in the field of Data Science.
Advantages of Data Science
The various benefits of Data Science are as follows:
- It’s in Demand
- Abundance of Positions
- A highly paid career
- Data Science is Versatile
- Data Science makes data better
- Data Scientists are highly prestigious
- No more boring tasks
- Data Science makes products smarter
Disadvantages of Data Science
While Data Science is a very fruitful career option, there are also various disadvantages to these fields. Data Science has some limitations and they are as follows:
- Data Science is a blurry term
- Mastering data science is near to impossible
- A large amount of domain knowledge is required
- Arbitrary data may yield unexpected results
- The problem of data privacy
Data science education is well into its formative stages of development; it is evolving into a self-supporting discipline and producing professionals with distinct and complementary skills relative to professionals in the computer, information, and statistical sciences. However, regardless of its potential eventual disciplinary status, the evidence points to the robust growth of data science education that will indelibly shape the undergraduate students of the future.
After weighing the pros and cons of Data Science we can contemplate the full picture of this field. While Data Science is a field with many fruitful advantages, it also suffers from its disadvantages. Being a less-saturated, high paying field that has revolutionized several walks of life, it also has its backdrops when considering the enormity of the field and its cross-disciplinary nature. Data Science is an ever-evolving field that will take years to gain proficiency. In the end, it is up to you to decide whether the pros of Data Science motivate you to take this up as your future career or the cons that help you make a careful decision.