Let’s explore the fascinating world of data science, where cutting-edge scientific methods and tools are harnessed to transform raw data into powerful insights. Indeed, true data scientists meticulously apply the scientific method, tailored uniquely to this domain, to achieve success within their scientific discipline. Explore this article to understand how data science serves as an excellent scientific method example while setting itself apart from other scientific fields.

The Scientific Method Is The Standard For All Scientific Work.

The scientific method is a process that all scientists use to investigate and understand natural phenomena. It involves observing the world, asking questions about what has been observed, formulating a hypothesis to explain those observations, and then testing that hypothesis through experiments. The results of these experiments either support or refute the hypothesis, and ultimately contribute to the development of scientific knowledge. Since the 17th century, scientists have formalized the scientific method as the best way to accumalate knowledge.

Additionally, the scientific method is normally an on-going process where scientist conduct basic research on a subject. Moreover as scientists go through each cycle, they add to the body of knowledge by affirming, disputing, or refining previous knowledge on the subject. To list, below is a summary of the typical scientific method steps used by scientists across multiple scientific disciplines. 

Standard Steps Used In The Scientific Method

1. Make an Observation. Scientists begin by observing a phenomenon or problem that they want to investigate.
2. Ask a Question. Based on their observation, scientists ask a question that they want to answer through their investigation.
3. Do Background Research. Before conducting an experiment, scientists research what is already known about the topic to inform their hypothesis and experimental design.
4. Form a Hypothesis. A hypothesis is an educated guess about what will happen in the experiment based on the background research and observation.
5. Conduct an Experiment. Scientists design and conduct experiments to test their hypothesis and collect data.
6. Analyze Results and Draw a Conclusion. After collecting data, scientists analyze it to draw conclusions about whether or not their hypothesis was supported by the evidence.
7. Report Your Results. Finally, scientists report 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.

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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. 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 interepting the results.
4. Create Software Tools For Visualizing Results And For On-going Business Use. Data scientists often create software tools for visualizing their results and 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 real-time software applications for business use. 

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A Unique Scientific Method Example Used By Data Scientists.

Recently, data scientists have begun to document the unique scientific method for data science. 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.

O – Obtain Data.

Scientists can 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

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.

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.    

M – Model Data.

Software and machine learning algorithms are used 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.

N – Interpret Results.

Data scientists take data and turn it into useful insights. Specifically, they create diagrams, graphs, and charts to show trends and predictions. As a result, this helps stakeholders understand and use the data in order to take action

“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.

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For more information from SC Tech Insights on data analytics and data science, click here.