In the initial stages, the problem data industries faced was the storage of data. Later on, a few companies such as Hadoop came up with the solutions to these problems of data storage. After that, the focus was shifted from the storage of data to the processing of data and converting it into meaningful information. Data science can add a lot of value to businesses. Many in-demand and potential industries such as artificial intelligence are also dependent on data science.
Data science provides information and input for artificial intelligence to process and act upon. The data that was previously obtained was mostly structured and the size of it was small. In the modern age, the size of the data obtained is very huge and conventional tools are not an option anymore. In these days, 80% of data is unstructured and raw. Data science helps in analyzing the data and convert it into meaningful information that businesses can use to predict customer behaviour or target the right customers.
Data science is a growing industry and is in demand more than ever. Various career options in data science are available. B. Tech and M.Tech courses are offered in Data Science by various universities in India.
There are different stages in Data Science process in its whole life cycle.
- Discovery – In this step, we need to understand the various needs of the project and how to prioritize them. The budget for the whole project also affects the process. Here we assess if we have all the resources that we need to start the process and run it successfully.
- Sandbox – In the step, we need to prepare the analytical sandbox in which we analyze the data for the whole project. We need to explore the data and it’s segmented before the next step.
- Modeling – As we know, there are multiple variables that affect the algorithm of data science. Determining the method, techniques and the relationship between different variables is done in this step.
- Model – In this step, we determine if the existing tools will suffice the need for this project or if we have to upgrade our tools. The training and testing of a few model sets are done in this phase.
- Operationalising – In this step, we test out a pilot project based on the input given and make any changes that are necessary. All the necessary tweaking to the code and relationship algorithm is done in the space.
- Results – In this step, we analyze all the results that have been gathered based on the input that is provided. We identify all the key findings and communicate the results and declare if the project is a failure or success.
We can conclude that data science is an ever-growing field and as long as businesses need to understand and predict consumer behaviour. They will have to use data science as a part of their operations. Doing M.Tech course in Data Science will provide great insight into the subject and a lot of employment opportunities for any aspirant looking to enter the field.
