What do we mean by Data Science?

Data Science is a process of finding meaningful insights from data to give constructive solutions to various business problems. During this process, we not only discover answers to questions that need answering but also new questions that could give the means to address crucial business problems. Data Science consists of five sub-processes: Collect, Store, Process, Describe, Model.
Collect: The task of collecting data varies depending on the type of data that we are looking for. Eg. If we are trying to perform sentiment analysis to check user feedback on a particular product on amazon, we already have the data on the web. All we have to do is scrape it. But in applications like agriculture, we have to venture out to collect data on different agricultural fields about the yield, type of fertilizers used and so on.
Store: The type of data that we want to store decides the storing mechanism. Structured and Operational data that we need in our day to day tasks are stored in relational databases. Data Warehouses are used for storing structured data from which insights can be drawn in the long run. Data lakes are storage mechanisms used for storing all kinds of incoming data regardless of their current usefulness. They will be used in case we need it in the future.
Process: Most of the real-world data needs processing before they can be used for further modeling. This includes Data Wrangling, Data Cleaning, Normalisation, and Standardisation.
Describe: Data Visualisation is very important to gain a better understanding of the data. It also helps in communicating the insights gained from data in a more visually pleasing manner.
Model: Modeling is the most important step in Data Science. It helps us understand more about the underlying distribution of the data and make useful decisions that could solve some important business problems.