A typical data science life cycle consists of the following stages:
- Data acquisition: The primary step in the life cycle of any data science project is to acquire the right data from multiple sources. Data acquisition involves acquiring data from different internal and external sources that can help answer business questions. Data can be extracted from various sources, such as logs from web servers, social media data, online repositories, or databases.
- Data preparation: Often referred to as data cleaning or data wrangling, it is a critical step in the life cycle. The data collected from different sources is frequently messy and is typically missing various values. Therefore, it is crucial to clean this data to derive value from it.
- Data exploration: After cleaning the data, you can perform hypothesis testing and visualize the data to understand the data better. Data exploration is sometimes called data mining. It is used to identify patterns in your data set and find important potential features with statistical analysis.
- Predictive modeling: To train your machine to make predictions, you need to build predictive models. For this, you have to choose the right algorithm on which the machine is to be trained. Historical data is then split into training and validation sets. The model is trained using the training set. The trained model is validated using the validation dataset, and the model is then evaluated for accuracy and efficiency.
- Model interpretation and deployment: After a rigorous evaluation of the model, you can deploy into a production-like environment for final user acceptance. You’ll want to present your model to a non-technical person and convey the actionable insights derived from the data.
Now that we have looked at the different data science life cycle stages let’s look at some of the data science algorithms that can help you solve complex business problems.