The data science process consists of the following steps:

1. Data Collection: The first step involves collecting raw data from various sources like databases, files, APIs, web scraping, surveys etc.

2. Data Cleaning: The next step involves cleaning and preprocessing the data. This includes,handling missing values, outliers, incorrect data types etc.

3. Exploratory Data Analysis (EDA):This step involves understanding the patterns and trends in the data by using statistical methods and data visualization.

4. Model Building: Based on the insights gained from EDA, appropriate machine learning models are built. This could involve supervised learning (classification, regression),unsupervised learning (clustering), or reinforcement learning.

5. Model Evaluation: The performance of these models is then evaluated using appropriate metrics like accuracy, precision, recall etc.

6. Model Deployment: Once a model is built and evaluated, it is deployed in the real world. This could be in the form of a web application, API etc.
7. Model Monitoring: Post deployment, the model’s performance is continuously monitored to ensure it is providing the expected results.

Data Science has a wide range of applications including but not limited to healthcare, finance, marketing, social media etc. It’s a rapidly evolving field.