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What is Data Science? History, Lifecycle & Applications

What is Data Science?

Data science is a field where the use of data assists us to make predictions and conclude decisions. With the means of raw data analysis and usage of combined procedure of statistical analysis, data analysis, and machine learning algorithms, diverse insights are generated to use as knowledge.

How does Data Science work? 

It all starts with data, which helps in making easier strategic decisions.

Data Science Working Lifecycle

Data Science Working Lifecycle

Data Collection:

In this process, a data team identifies the type of data that helps to get the desired results. After identifying multiple data from various sources, they are structured into a common database to analyze and simplify it.

Data Cleaning:

Raw collection of data is incomplete and has multiple points of failure. Therefore, the data team fills up the missing data in a strategic way. That’s why they remove Multi outliers of data that distort the result. Data team efforts to make the data consistent and perfect aid the further analysis step.

Data Analyzing

In this step, the data team visualizes the data to identify different underlying patterns and data gems. Multiple creative approaches to the data analysis process creation help to obtain a better solution.

Data Modelling

This process starts after the data team applies the core of data, its science and machine learning to find the initial essential features from the sample data. After that, the data team uses data modeling to get the best data prediction.

Data Storytelling

Finally, after the data model is ready, the data team utilizes the results to straddle the data insights to make better strategic decisions for the company.
The insights are usually presented in user-friendly language and visualized so that the decision-taker can reference them and use them to accelerate the company growth.

History of Data Science

Danish Computer Scientist Peter Naur introduced the term in 1974. Before the term was named, John Tukey, a mathematician, and statistician mentioned data analysis used statistics and computers to get a measurable solution to a problem that would take longer than manual calculations.

Similarly, In 1977, The IASC, International Association for Statistical Computing was established with a mission to link traditional statistical methodology with modern computer technology to generate outcomes that are converted into knowledge to help make a decision.

The first workshop for the Knowledge Discovery in Databases, later known as the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, was created in 1989.

In 1999, the CAO of DMWAY analytics, Jacob Zahavi noticed and realized the need to store as well as handle large-scale data. Moreover, Software-as-a-Service (SaaS) was created in 2000 to enable the new world of cloud-based applications.

Some major marks in data science history were in:

The year 2002: 

The International Council for Science: Committee on Data for Science and Technology began publishing the Data Science Journal.

The year 2006:

Hadoop, an open-source, non-relational database, was released

The year 2009:

NoSQL was reintroduced.

The Year 2011:

Job Listings for Data scientists increased by 150 times, and data science was the buzzword.

The Year 2015:

Using Machine Learning, Google’s speech recognition had a significant improvement.

Data Science Applications

In the modern digital world, there are enormous applications of the field. We have mentioned a few of the important ones below:

Health Care:

Data science plays a leading role in healthcare advancements. Different tools and machine learning solutions have helped medical operators detect several diseases before its critical. It has helped save the lives of thousands of individuals through early diagnosis of different heart and lung diseases.

Digital Advertising

Digital advertising is upgrading to a whole new standard with the help of data along with machine learning. The machine learning algorithm models target the audience interested in the advertised products or services. Almost every social media and search engine utilize data to target their advertisements.

Fraud detection

Data science has helped hundreds of online businesses together with individuals by rejecting suspicious transaction attempts through fraud detection. Also, multiple email services use fraud detection algorithms to analyze spam emails which helps to minimize fraud.

Recommendation Systems

Social media, e-commerce, OTT, and Music Streaming software use data generating tools with machine learning to recommend the most likely results that fulfill the user’s intent. Reportedly, with the help of the recommender system, amazon sales have increased by over 29%.
Machine learning together with Data Science can upgrade almost any digital system. Other applications of recommender systems are image detection, autopilot in cars, smartwatch health systems, etc.


Wrapping it up. Data science is being utilized by almost every large company to its advantage. This field of education has great potential and bright career path to choose if you are interested.

Do read our other blogs to understand more about data science. 

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