Thu. Jul 25th, 2024
What Is Data Science?What Is Data Science?

For the first time, the term ‘Data Science’ was coined by William S. Cleveland in 2001 and from then onwards various publications and journals were released on the concept which led to its popularity across the world. It was around this time only when the “dot-com” bubble was as well in buzz which eventually led to adoption of internet worldwide and through that a huge amount of data was generated. In addition to this, with advancement in technology and faster and cheaper computation, the concept of “Data Science” was officially introduced to the world. Later, the acclaimed admiration for Data Science coined the term as a buzzword in 2012 when Harvard Business Reviews mentioned it to be “The Sexiest Job of the 21st Century”. 

But many of you may be wondering what is data science actually, and what its role is in the internet-driven society that makes it so famous. So, let us understand this concept in detail.

I. What is Data Science?

Data Science is an interdisciplinary domain that uses mathematic algorithms and scientific interface to take out meaningful insights and knowledge from a huge amount of structured as well as unstructured data. The algorithms used in this field are executed via computer programs that run on robust functioning hardware since it requires a bulk of processing.

Data Science pertains to be a unique combination of computer science, data analysis and visualization, machine learning, statistical mathematics, and domain knowledge. As its name suggests, the most critical component of Data Science is its ‘Data’ and data can be of various types so you have to have proper data to draw meaningful insight through algorithm computation.

II. Big Data’s Function in Data Science

Big Data is the term used to describe a huge collection of heterogeneous structured, semi-structured, or unstructured data. Usually, Databases are not capable of handling such a large collection of datasets and this is where Big Data plays its role. It is distinguished by its volume and variety, two characteristics that are crucial for data science since “the more data, the greater the insights,” In this way, Data Science draws the complex patterns from the Big Data by building Machine learning algorithms and models.

 III. Applications of Data Science

For the purpose of resolving complicated data-related problems, the field of data science is applicable to practically every other business. In order to find solutions to their various difficulties, every business applies this notion to a distinct application. While some of the businesses totally rely on data science and machine learning solutions to solve their problems. Some of these data science applications, along with the organizations that support them, are listed below:

  1. Internet Search Results (Google): For each search on Google, sophisticated Machine Learning algorithms rate the pages to determine which ones are the most pertinent to the search term(s) entered.
  2. Spotify’s recommendation engine uses data gathered over time from individual users to develop a more precise understanding of a user’s musical preferences and present them with related songs in the future.
  3. Intelligent digital assistants, such as Google Assistant, that can convert speech to text, comprehend its context, and deliver pertinent information.
  4. Autonomous Driving Vehicle (Waymo): High resolution cameras and LIDARs sensors are used to capture live video and 3D pictures of surrounding and feed it through ML algorithms to assist in the autonomous driving of the vehicle.
  5. Spam Filter (Gmail): Data Science algorithms put the filters to use in order to separate the spam emails from the rest.
  6. (Facebook) Hate Speech and Abusive Content Filter: Social media platforms like Facebook, these services automatically filter away age-restricted, abusive, and hate speech from the unintended audience using data science and machine learning algorithms.

 IV. The Life Cycle of Data Science

Data science is not a single step process-based field. There are various steps involved in generating the desired result from the data fed which are listed below:

  1. Project Analysis: This step involves Project Management and Resource Assessment to determine the data requirements of a project and successfully complete the same.
  2. Data Preparation: This involves the conversion of raw data into structured data to get clean data. After this step, programming languages are used to achieve the desired results for big datasets.
  3. Exploratory Data Analysis (EDA): In this step, Data Scientists explore the data from different angles and draw initial conclusions. It involves the data visualization process, rapid prototyping, Feature Selection, and Model Selection.

4.     Model Building: In this step, the EDA is used to define the first type of model that will be utilised in the procedure, and resources are then directed toward the building of the model with the optimal hyperparameters.

5.     Deployment: This step involves bringing out the model to the real world from its sandbox which is called model deployment.

6.     Real World Testing and Results: After the model has been deployed, this stage requires constant model output monitoring in order to identify situations where the model fails and those where it succeeds.

V. Concluding Remarks

It is no doubt that Data Science is an advanced complex field of study. It is quite hyped because it truly delivers the solution to problems as required. Some of the fields of Data Science have begun to even outperform humans and this trend is expected to grow in the coming times. Data Science definitely defines the bleeding edge of technology in present times and promises more technological advancements to come in the coming future. No wonder why it is one of the highly in-demand and high-paying jobs in the industry. So, if you are interested in becoming Data Scientist, you must take training from a well-recognized and accredited source to build a strong career in this field.

By admin