Data science is revolutionizing the way businesses operate, and at its core is the Scientific Method that is used by data scientists. Undoubtedly, many business professionals work with data, but it is the use of the Scientific Method that distinguishes data scientists from others who work with data. Indeed, it is data scientists who harness the scientific method, analytics, and advanced AI tools to transform raw data into powerful insights and software for businesses. In this article, I’ll share with you the steps involved in the Scientific Method as used by all scientific disciplines. Also, I’ll provide you a Scientific Method example of how data scientists uniquely apply each step of the Scientific Method.

The Scientific Method Is The Standard For All Scientific Work.

The increasing adoption of Data Science in the corporate world is notable, especially since it employs the Scientific Method – a systematic process used to investigate natural phenomena. Specifically, this method involves observing, questioning, hypothesizing, and testing through experiments, ultimately contributing to scientific knowledge. Additionally, data scientists, like their counterparts in other scientific disciplines, use this methodology to conduct research, refine existing knowledge, and accumulate new insights. Surprisingly, the Scientific Method is a process that has been formalized since the 17th century. See below for a description of each step within the Scientific Method.

Standard Steps Used In The Scientific Method

1. Make an Observation. First, a scientist begins by observing a phenomenon or problem that they want to investigate.
2. Ask a Question. Next based on their observation, the scientist asks a question that they want to answer through their investigation.
3. Do Background Research. Before conducting an experiment, the scientist researches what is already known about the topic to inform their hypothesis and experimental design.
4. Form a Hypothesis. As a result, the scientist develops a hypothesis, an educated guess, about what will happen in the experiment based on the background research and observation.
5. Conduct an Experiment. Next, the scientist designs and conducts experiments to test their hypothesis and collect data.
6. Analyze Results and Draw a Conclusion. After collecting data, the scientist analyzes it to draw conclusions about whether or not their hypothesis was supported by the evidence.
7. Report Your Results. Finally, the scientist reports their findings through scientific publications or presentations to share their knowledge with others in the scientific community.

For more on the standard scientific method used by scientists, see UntamedScience’s What is the scientific method? and Britannica’s Scientific Method. Also, click here on more about what Data Science is and is not.

Data Science Definition – The Truth About This Discipline And Its Massive Growth

Essential Prerequisites For Data Scientists To Achieve Success Using the Scientific Method.

When you apply the scientific method to data science, there are several factors that will influence its effectiveness. These essential prerequisites for data scientists to succeed include:

• The quality and quantity of data available. A robust dataset is critical for deriving meaningful results.
• Choosing the right statistical methods and techniques. This is crucial for accurately analyzing and interpreting data.
• Account for biases and assumptions. This is critical during the hypothesis formulation and testing stages. This is because these can significantly impact your conclusions that were based on your data sets.

“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.”

Jean-Paul Isson

To detail, below is a scientific method example that highlights how a data scientist’s approach can vary from one project to another.

A Scientific Method Example

1. First, Stakeholders Initiate The Scientific Method With A Question.

In data science, the business stakeholders normally formulate a question that initiates the scientific method. This is because the stakeholders are looking for a deliverable that will result in better decision-making or for a new business software application.

2. More Data Cleansing To Create Robust Data Sets.

Next, data scientists spend a significant amount of time cleaning and preparing data to ensure that it is accurate and reliable for analysis.

3. Use Of Data Tools And Automation To Support Every Step Of The Scientific Method.

Data scientists use a variety of statistical methods and data tools to support every step of the scientific method. Specifically, these tools and automation support everything from hypothesis testing to data modeling to interpreting the results.

4. Create Software Tools For Visualizing Results And Apps For On-going Business Use.

Lastly, data scientists will often create software tools for visualizing their results and software applications for ongoing business use. For example, these can be analytical dashboards that allow stakeholders to interact with data in real-time. Additionally, data scientists can leverage AI and machine learning (ML) to develop software applications for business use. 

Also as a side note, data science is different that data analytics. For more discussion on this topic, see my article, Data Analytics vs Data Science – Know the Most Important Differences.

Data Analytics vs Data Science – Know the Most Important Differences

A Unique Scientific Method Example Used By Data Scientists.

Recently, data scientists have begun to document the unique way that they use the Scientific Method within the Data Science community. As with any scientific process, the process starts with a statement of the problem. Moreover, this problem is usually a business problem where the business stakeholders have tasked the data scientist to research and come up with a possible solution. Below is an example of a data science process called OSEMN.

Step 1: O – Obtain Data.

First, scientists will acquire data from existing sources, newly acquired sources, or from the internet. For example, data scientists can get data from internal or external databases, company software, web server logs, social media, or from third-party sources.

“We have to learn to interrogate our data collection process, not just our algorithms.”

Cathy O’Neil

Step 2: S – Scrub Data.

Data scrubbing is the process of making data consistent. Specifically, this includes fixing errors, handling missing data, and removing data outliers. For example, changing all date values to the same format, fixing spelling mistakes, and fixing mathematical inaccuracies.

Step 3: E – Explore Data.

Data exploration is a way to analyze data before carrying out more detailed studies. For instance, data scientists can use descriptive statistics and data visualizations to gain an understanding of the data. Then they look for patterns that can be explored or used to make decisions.    

Step 4: M – Model Data.

Scientists use software such as machine learning algorithms to gain insights and make predictions. For example, data scientists can use different techniques such as association, classification, and clustering to apply to the data. Further, the model is tested on specific data to check accuracy and can be tweaked to get better results.

Step 5: N – Interpret Results.

Data scientists take data and turn it into useful insights and software for businesses to use. Specifically, they create diagrams, graphs, and charts to show trends and predictions. Additionally, software can be developed based on their research efforts. As a result, this helps stakeholders understand and use the data in order to take action within the business world.

“Data are becoming the new raw material of business.”

Craig Mundie

This OSEMN process described above is a data scientific method from AWS. Several other organizations have defined stages or steps of a data scientific method, but they all follow the same steps more or less as described above. For example, see Simplilearn’s Description Of The 5 Stages Of The Data Science Process and Berkeley School Of Information’s Data Science LifeCycle.

Lastly for more detailed discussion on what is Data Science, see my article, What Is Data Science? Its Focus, Applications, 6 Components Explained.

What Is Data Science? Its Focus, Applications, 6 Components Explained

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