fbpx
data science

What is Data Science?- Adequate Information About Data Science

Data Science And Digital Marketing

Hey there! I hope all of you fit and fine in this pandemic period. We see a lot of changes in this time period. Lose of life, economic fall, lack of money, lost of our near and peer and so on. In term of technology we see so many new evolution’s in the market. During this evolution we heard about two terms which is now in trend i.e. Data Science and Digital Marketing.

Digital marketing

We well know about digital marketing little bit. The word digital marketing comes in existence from a long time ago. But it is well known in India from 1 or 2 decades before. Now a day’s mostly people know about digital marketing. They start working on it. Digital marketing change the level of marketing. Most of the companies hire many digital marketers. In our previous blogs I am more discussed about digital marketing. (Know more click here).

parts of digital marketing
digital marketing parts

Data Science

Data science is related to data mining, searching, machine learning language and big data. It is a concept to unify statistics, data analysis, informatics, and their related methods in order to understand and analyze actual phenomena with data and utilize it.

Data science incorporates skills from computer science, statistics, information science, mathematics, information visualization, data integration, graphic design and development, complex systems, communication and business.

Components of Data Science:

  • 1. Data Strategy.

  • 2. Data Engineering.

  • 3. Data Analysis and Models.

  • 4. Data Visualization and Operation

data science workingworking process of data science

Storing of data is very important at every step. Stored data use frequently and timely again and again.

Data Strategy: –

Normally managing the data and hold like as assets. It helps to ensuring that the data is use effectively and efficiently. Main focus to store the data for further use. Data strategy helps to use the data as much as possible, across the companies.

Data strategy helps in improved accuracy, access, sharing and reuse. Somewhere it establishes common methods, processes and practices to manage manipulate and share data across the companies, industries or firm in such a manner that it can be use again and again.

Data strategy work on a specific method. For data strategy identify the required data and its resource. Resource is where we get the data or data evolved. Store the data in specific place so that it can be use again and again. It must be necessary to store the data at safe place. Data have the capacity to manipulate the whole idea and process.

Provision is a place where is data use and how to use. It also consist the information from where data is coming to the required location. Now process the data to get result. Getting results now govern on it and distribute to all place.

Data Engineering: –

Focuses on practical applications of data collection and analysis is data engineering. Process of giving answers of all the questions, collecting information and validating it. Collecting and validating is very important part of it. Without collecting and validating whole process can be void. Data engineering helps to convert data science in useful system.

Everything becomes worthless without conceptual understanding of logical operation, flow of information and data model and so on. Artificial intelligence is a part of data engineering. AI is only possible by the data engineering. All the command and information uploaded in robots, by data engineering.

Data Analysis and Model: –

Data analysis is a technique to gain insight into an organization’s data. It helps to analyze the report so that better decision taken. Helpful in finding the queries and merge the data. So that in future it can use effectively. Data modeling is a set of tools and techniques used to understand and analyze how an organization should collect, update, and store data. It is a critical skill for the business analyst who is involved with discovering, analyzing, and specifying changes to how software systems create and maintain information.

Data Visualization and Operation: –

After completing the all above process, this time to visualize the data. Visualization of data may be in the form of graph, charts, maps or other method. Data visualization gives an accessible way to see and understand the pattern of data, trends and outlier. Visualization helps in creates attractive visuals. Data operationalisation helps in production and managing them. Organizations should know the value of data science and machine learning models.

 Connection of Data Science and Digital Marketing

Digital marketing and data science both are two different terms. But both are related on the point of data. We use data science in digital marketing effectively.

Data science work to collect and analyze data so that it use more effectively and efficiently. On the other hand performing digital marketing different data is required related to the customers. The collection of data is process by the data science.

What Should I Choose:-

Data science and digital marketing both are different trend and trade. Comparing basis of technical aspect, data science is more technical and cannot easy to understand. Data science consists of programming language and different source of collecting data. Whereas digital marketing is all internet work. It is required to learn a language or programming language. Data science is more technical, it doesn’t mean that digital marketing is easy. It is a technical world and spoon feeding is not allowed.

choosing one way
choosing one way

Choosing of trend depend upon many factors like what the persons needs and wants. It also depends on, What is his/her passion? Which interesting factor supports him to learn any one of them? Rather then it, it is clear that both are now in trending. Future and scope of these trends is very bright. Both industries never fall down as technology and demand of people change day by day,  and moment by moment.

Short View on Data Science

For data science a lot of knowledge of different programming language is required. Programming language helps machine to understand the condition and helps think like a human. This all be done by using different algorithms and using different condition. Programming language like C++, C, python, Fortran etc all helps in programming for the machine to take command and perform action.

Data science fills the gap between human being and machine. Today 95% people uses mobile, data science help to understand mobile what function we perform and what result we see. This is just because of data science.

Related Terms:-

Leave a Comment

Your email address will not be published. Required fields are marked *