Data science is one of the most used words in the tech world now. Well, this emerging trend of opting for data science as a career is worth the hype. Data science has proved its importance in various fields from logistics to genetics. Every company generates a large quantity of data. Data in simpler terms can be any meaningful information. The increase in data generated led to the new field of study based on gathering information, processing it, and generating an outcome from it.

What is data science exactly?

Data science is not merely writing code and developing a complex model or visualizations. Data science in simpler terms is gathering data, analyzing, and producing an outcome that creates the maximum impact possible for the company. Data science is becoming a vital technology as there is a huge amount of data being produced every day and it is essential for a company to keep track of the company’s data and their consumer's data. Have you ever wondered how Facebook and Instagram show a stat on how much time you spend on these apps, well that’s pretty much an example of how data science works? As a data scientist you should solve the real problems of a company, these companies generally use the data collected from the users to enhance their products and operational tools

How does data science work?

The initial step towards implementing data science is the need for a business problem. A data scientist understands the real problems and their requirements keenly and prepares to gather the suitable data

The second step would be data acquisition, gathering the right set of data is important. Data acquisition can be done from web servers, logs, databases, APIs, online data repositories. Determining the right set of data requires a lot of time and effort

The succeeding step would be data preparation, data cleaning, and data transformation. Data cleaning involves removing noises, missing values, misspelled attributes, inconsistent data types. Data transformation modifies the data based on mapping rules

In order to develop accurate models, it is necessary to select the correct feature variables which can be done using exploratory data analysis. EDA is the most important and necessary step in the data science model life cycle

The Core step of data science is data modeling, the data scientist uses various MLT techniques and identifies the best model for the business requirement. The data model is tested using training data sets and the best performing model is selected

The following step would be data visualization, where the data are represented using powerful reports or relative formats using tools like tableau, and data scientists meet the clients to communicate business findings in order to convince the stakeholders.

The final step is Deploying and maintaining the model is necessary, the data scientist first tests the model in a pre-productive environment and deploys it to the actual production environment. After deploying the model, with the use of insights and reports the model is maintained for future updates of the product

Data science is revolutionizing the world in all fields. The recent development of data science in logistics has led to more productivity. Thus data science is necessary for the advancements of technology in every sector