What is Data Science? | What is Data science course?

What is Data Science?

Data science is the branch of computer science in which we collect data from various sources like facebook,and E-commerce website etc. and storing   sepreatly all data by which we can easely precess data and desribe and create model.

Hence Data Science  is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data. 

Data science is apply for large amount of data like million terabyte (TB), petabyte (PB), exabyte (EB), zettabyte (ZB) and yottabyte (YB).

Why Data Science?

Here some points is given by which Data Science is most in demand

1. Data is the oil for today's world. With the right tools, technologies, algorithms, we can use data and convert it into a distinctive business advantage
2. Data Science can help you to detect fraud using advanced machine learning algorithms
3. It helps you to prevent any significant monetary losses
4. Allows to build intelligence ability in machines
5. You can perform sentiment analysis to gauge customer brand loyalty
6. It enables you to take better and faster decisions
7. Helps you to recommend the right product to the right customer to enhance your business

Steps involved in Data Science    

1. Collecting Data
2. Storing Data
3. Processing Data
4. Describing Data
5. Modelling Data

1. Collecting Data

      Data collection is defined as the procedure of collecting, measuring and analyzing accurate insights for research using standard validated techniques. A researcher can evaluate their hypothesis on the basis of collected data. In most cases, data collection is the primary and most important step for research, irrespective of the field of research. The approach of data collection is different for different fields of study, depending on the required information.

2.Storing Data

Analysis of data  by machine learning algorithm analys hug amount of data which not possible store in normal storage. for storing this hug amounts of data stored in database. this large amount of data is known as big data. extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. 

3.Process Data

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output.

Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

4.Describe Data

Often we wish to describe a set of data with a single number, or a small set of numbers, in such a way that these values will yield enough information about the content of the data that we can produce a means of generating a similar set of data from this description. One manner in which this can be done is by specifying values that describe the numerical center of the set of data, which may be defined in various ways. They are measures of the centeral tendency of the data.we can also describe the data by how it is dispersed around a particular measure of centeral tendency. A third manner in which we can describe data is by how it tends to accumulate with respect to the centeral tendency such as whether it tends to accumulate immediately to the left or to the right of the numerical center

5.Modelling of data

Modelling is the process of findout the relationship between variable of data. it is the process of creating a data model for the data to be stored in a Database. This data model is a conceptual representation of Data objects, the associations between different data objects and the rules. Data modeling helps in the visual representation of data and enforces business rules, regulatory compliances, and government policies on the data. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data. 

Hence this is the all process of data science, I hope that this post is usefull for you.