A data scientist uses data to solve problems, make decisions and predict the future. They perform different tasks and roles.
A data scientist collects, cleans and analyzes data. Then a data scientist will perform exploratory data analysis and look for patterns in data.
The components involved in data science are:

• Data: Unprocessed information.
• Statistics: the skills used for analyzing and interpreting data.
• Programming: Languages used to manipulate data like python.
• Relational Database Management System(RDBMS): They play a role in how data is stored, managed and accessed.
• Machine learning: Algorithms that allow computers to learn from and make predictions on the provided data.

What is the data science process?
Data scientists follows a series of steps and procedures to extract meaningful information from data.
The following are the steps followed:

Defining the problem
This involves understanding the problem you are trying to solve. The problem could be predicting the customer behaviours, identifying the key market trends etc. This step is critical as it guides on the methods to use during the subsequnt processes.

Data Collection
It involves gathering data from various sources. These sources could be internal databases, APIs, web scraping etc. When collecting data a data scientist should ensure quality and relevance to the problem as this lays a foundation for the subsequent process.

Data cleaning and preparation
Data cleaning involves identifying missing values and outliers and removing them. It also involves handling duplicate values.
This process is so critical to making data suitable for analysis.

Expaltory Data analysis(EDA)
Using the statistical methods and visualization tools to understand distribution and outliers in the data.
It at this step that trends are identified as well as discovering the underlying data structures.

Feature engineering
Feature engineering is about creating new variables or features for better performance of the model. This step uses domain knowledge to identify which features are relevant to the problem at hand. It involves the normalization and standardization of categorical variables.

Model Building
Data scientist chooses the modeling techniques to apply based on the problem at hand and the data characteristics.
It involves training multiple models to compare their performance.

Model Evaluating and Tuning
Evaluating models using the relevant metrics like accuracy. The models may be tuned to improve performance.

Deployment
The best-performing model is deployed to perform the required task.

Monitoring and Maintenance
This involves updating the model.

Conclusion
Data science is dynamic and requires certain skills, tools and methodologies. A data scientist should understand each phase of of data science process and apply it effectively.