Data Science Process
Data Science Process
Data Science can assist you to detect fraud using advanced machine learning algorithms. It helps you to actually prevent any significant monetary losses. Moreover, it also allows building intelligence ability in machines. You can actually perform sentiment analysis to gauge the loyalty of customer brand. It enables you to actually take better and faster decisions. It even helps you to recommend the right product to the right customer to enhance your business.
Now in this article, we will learn the Data Science Process:
The steps involved in Discovery are acquiring data from all the identified internal as well as external sources which assist you to answer the business question.
The data can be:
- Logs from webservers
- Data gathered from social media
- Census datasets
- Data streamed from online sources using APIs
Data can have numerous inconsistencies like missing values, blank columns, an incorrect data format which must be cleaned. You actually need to process, explore, and condition data before modeling. The more cleaner your data, the much better your predictions.
In this stage, you require to determine the method and technique to draw the relation between input variables. Moreover, planning for a model is performed by using various statistical formulas and visualization tools. SQL analysis services, R, and SAS/access are several of the tools that are used for this specific purpose.
In this particular step, the actual model building process begins. Here, a Data scientist distributes datasets for training as well as testing. Techniques such as clustering, association, and classification are actually applied to the training data set. Once the model is prepared, it is tested against the “testing” dataset.
In this stage, you deliver the ultimate baselined model with reports, code, and technical documents. Furthermore, the model is deployed into a real-time production environment after thorough testing.
In this stage, the key findings are communicated to each and every stakeholder. This helps you to choose if the results of the project are a success or a failure completely based on the inputs from the model.