Five Steps of Data Science
Data Science Process:
The term "data science process" is used to describe the methodical steps taken to gain understanding through data analysis. Data Scientists follow a rigorous procedure for collecting, organizing, cleaning, and analyzing information. Data scientists can better use the resources at their disposal and provide the business with useful insights by adhering to a data science process. This helps organizations save money by keeping more of their current customers and attracting new ones. In addition, using a data science process aids in unearthing previously unknown relationships between previously unrelated datasets. The procedure aids in finding a remedy by approaching the business issue as a project. Let's dive into the specifics of a data science process and find out what goes into one.
Steps of Data Science Process
While the exact order of operations may change from one company to the next or from one person to another, there are generally five phases to any data science project.
1. Asking the Question and Problem Definition: The first step involves clearly understanding and defining the problem or question the user wants to address using data science. This starts with asking the appropriate question regarding the data. Also, it involves collaborating with stakeholders to identify the business or research objectives, as well as understanding the available resources and constraints.
Before solving a problem, the pragmatic thing to do is to know what exactly the problem is. Data questions must be first translated into actionable business questions. People will more than often give ambiguous inputs on their issues.
For Example, for a Business, the following questions can be asked:
- Who the customers are?
- How to identify them?
- What is the sale process right now?
- Why are they interested in your products?
- What products they are interested in?
You will need much more context from numbers for them to become insights. At the end of this step, you must have as much information at hand as possible.
2. Data Acquisition and Understanding: In this step, you gather the relevant data required to address the defined problem. This can involve acquiring data from various sources such as databases, APIs, or external datasets. Once the data is obtained, you explore and understand its structure, quality, and variables to identify any issues or limitations that may affect the subsequent analysis.
3. Data Preparation and Cleaning: This step involves transforming and cleaning the data to make it suitable for analysis. It may include tasks like handling missing values, removing outliers, normalizing or scaling variables, and feature engineering. Data preparation is critical for ensuring the data quality and reliability for subsequent modeling.
4. Data Modeling and Analysis: In this step, you apply statistical, machine learning, or other analytical techniques to develop models and extract insights from the prepared data. This can include tasks such as exploratory data analysis, feature selection, algorithm selection, model training, and evaluation. The goal is to build models that can effectively address the defined problem and provide valuable insights.
5. Interpretation, Communication, and Data Visualisation: Once you have obtained results from your data analysis, it is crucial to interpret and communicate those findings effectively. This step involves analyzing the model outputs, interpreting the results in the context of the problem, and communicating the insights to stakeholders. Visualization techniques, storytelling, and data visualization tools are often used to present the findings in a clear and understandable manner.
It's important to note that the data science process is iterative and non-linear, meaning that you may need to revisit previous steps as you gain more insights or encounter new challenges along the way. Additionally, deployment and monitoring of models are often considered additional steps in the data science lifecycle, ensuring that the developed solutions are implemented in real-world scenarios and monitored for performance and impact.