Data Science

Data science is a field of study which is a great combination of domain expertise, programming skills, and knowledge of mathematics and statistics which provide enormous help in the extraction of meaningful data and information.

Data science follows the practices of the Machine Learning algorithm over the text, videos, images, and audio which will generate the Artificial Intelligence system which will be capable of performing the task by using human intelligence.

The history of data science starts more than 50 years back, which was provided by Peter Naur in 1960. After a few years in 1974, Peter published a summarised review of the Computer Techniques which define data science as the modern processing of data. Later in 1997, C.F. Jeff Wugiven had given a lecture on data science and said it is the statistical form of Data Science.

In 2001, William S. Cleveland published an article on Data Science as a Self Governing field which was published in the International Statistical Review and 2002, ICS ( International Council of Science) will focus on the problems associated with Data Science which will have explanations like data systems, publication on the web, application, etc.
Then, in 2003, Columbia University started the journal for the data science platform for the data staff. And in 2005, the National Science Board published a collection of Digital Data.

Data scientists are the experts who have the knowledge and experience to manage the data and also to understand the market trends with the processing of the data. Data scientists have the analysis skill that will make the easy understanding of the problem will have the solution and also helps in the better decision-making process.

Importance of the Data Science

Data science has various importance which are listed below:

  1. The companies will have the capability to understand their clients with the help of data science because clients have the most important role which decides the success and failure of the products.
  2. Data science improves the quality and power of the product which will be capable of building the base of any relationship with the client for every single company.
    Data science provides the solution for all the problems of the different types of industries like travel, healthcare and education by analysing the obstacles easily.
  3. In the world at present there is a vast amount of data, if it is used properly then it would decide the future of the product and whether it will be a success or a big failure.
  4. Big Data is growing rapidly and with the use of the various tools in the market will be able to solve the issues related to Information Technology, Human Resources, and management.
  5. Data science can also work as an excellent combination of the Knowledge-based System and Decision-Making System.

Life Cycle of the Data Science

The lifecycle of data science has 5 major stages and each stage has its specified task which is listed below:

  • Capture: This stage involves gathering raw data and unstructured data together. The major tasks performed are Data Acquisition, Data Entry, Signal Reception, and Data Extraction.
  • Maintain: Includes the major tasks like Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture. This stage takes all the raw data and transforms data into meaningful ones so that it can be used in the right direction.
  • Process: In this stage, the major task is Data Mining, Clustering/Classification, Data Modeling, and Data Summarization which examine the pattern, range and biases to define the analysis data.
  • Analyse: The analysis of the data includes tasks like Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, and Qualitative Analysis.
  • Communication: In the final stage the analyst prepares the data in a readable format for which charts, graphs and reports are used with a task like Data Reporting, Data Visualization, Business Intelligence, and Decision Making.

Data Science Prerequisites

To start the learning process of data science one must be familiar with the following concepts:

  • Machine Learning: Machine Learning is the heart of Data science. Machine Learning is the child area of Artificial Intelligence. Machine learning is a method of understanding and building to “LEARN”.
  • Modelling: The Mathematical method/model avail the easy and quick calculation and prediction of the data. Modelling leads every individual to identify the best suitable algorithm to solve any kind of problem.
  • Statistic: The core of data science is statistics which allows you to manage and extract more accurate, intelligent and meaningful data.
  • Programming: To process anything in the computing machine it requires a language that would be understandable by them. Python and R is the most popularly used programming language due to the reason that it is easy to learn and offers varieties of libraries.
  • Databases: It is the area where data has been saved and also offers the opportunity to its user to easily extract the data from the database. It makes the individual understand the data in the right direction.

Application of the Data Science

Data science is a progressing field. Every day there is something new in this field that’s why it is creating a buzz. Since we all know that data is the latest oil. That is why data science can be used in any field. It has an enormous list of use cases and features:

1. The Users’ Perspective

Data science uses different types of algorithms to make the pattern to predict the result. These can be used to create data sets and by analysing these data sets we can understand more about our users. We can learn more about the user interaction with the product. We can understand user habits better and can create more personalised products for the user. Using these all points we can achieve and improve the following :

  • Better user experience
  • Personalised ads
  • Better marketing
  • High revenue

2. Managing finances and marketing

Finance and banking are the earliest adopters of data science. Data science has revolutionised the banking field. It helps the banks to learn more about the pattern and usage of the customer. They are also used in the following fields:

  • Fraud detection
  • Risk analysis
  • Consumer analysis

3. The healthcare sector

Data science uses many datasets to analyse the report. It is faster than the traditional method. It can be the most accurate. It can also predict the possible ailment which can happen to the patients.


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