*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
Critical thinking is the ability to think clearly and rationally while identifying and understanding logical connections between ideas. In data science, critical thinking is crucial to drawing actionable insights and improving business operations. Critical thinking also plays a significant role in data science, and you can learn how to improve your skills with these tips.
Critical thinking is a crucial skill in data science and analytics. Critical thinking is arguably one of data science’s most essential abilities. It strengthens their ability to dig deeper into information and extract the most meaningful insights.
In what ways does critical thinking impact data science? What makes it such a crucial skill for a data scientist? Here is what you need to know.
Table of Contents
The field of data science is continuously evolving and becoming a part of daily operations within various industries, from customer service to healthcare. Data science deals with large data sets and uses machine learning algorithms to detect patterns, pull insights, and make predictions.
By combining math, statistics, specialized programming, AI, and machine learning, data scientists can uncover actionable insights from a company’s data.
“These insights can indicate how to reach a target audience better, what products sell best, and how to enhance marketing efforts.”
This leads to more efficient, data-driven business decisions.
When working with data science, there’s a distinct lifecycle to the process, consisting of five stages, each with its specific tasks, including
This first stage is simply just about gathering raw data. The data can come from various sources, including websites and customer engagement analytics on social media. Often, this stage is also called data acquisition or data entry.
Critical thinking is vital here. You take the raw captured data and transform it into an efficient and accessible form. This stage is also known as data warehousing, data processing, or data cleansing because you clean up the data for practical applications.
Here, data scientists will take the data you organized and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis. This processing stage is also called data mining or data modeling.
This predictive analysis stage is the bulk of the data science lifecycle. It is where your data scientist will perform various analyses on the data, depending on what your goals for the data are.
Lastly, data scientists and analysts will prepare the analyses they completed and any findings from the data in an easily readable format (chart, graph, report, etc.). This is also called data visualization.
Critical thinking is the ability to think clearly and rationally to understand the logical connections between ideas. Critical thinking isn’t a novel concept, as it has been around since the days of ancient Greek philosophers like Socrates and Plato.
Critical thinking can also be described as engaging in reflective and independent thinking while being an active learner rather than passively receiving information. Someone skilled in critical thinking often questions ideas and assumptions rather than accepting what they’re told at face value.
Critical thinkers are also much more systematic and analytical in problem-solving. They aren’t ones to act on intuition or instinct alone. Necessary thinking skills are instrumental on the job, whether working in an entry-level position or as a top executive.
“Good critical thinking skills allow you to objectively analyze the facts to solve problems, yielding more sound and effective decision-making.”
Whether you’re at work or in another setting, there are general steps to take when using critical thinking to problem-solve, including:
- Identify the Problem
- Determine Why the Problem Exists and How it Can Be Solved
- Research the Issue and Collect Data or Information
- Organize the Data and Findings
- Develop and Execute Solutions
- Analyze Which Solutions Worked and Which Didn’t
- Identify Any Ways to Improve
Critical thinking is generally a broad term. Professional data scientists also have these necessary thinking skills. They can objectively analyze data and draw thoughtful insights. There are many critical thinking skills to tap into, but six of the most essential skills to the necessary thinking process include:
- Identifying Biases
- Judging Relevance
Critical thinking is supposed to be objective and fact-driven, so it’s crucial to identify when you or others have a cognitive bias. It’s also vital to determine when the data or information you’re analyzing may also be biased.
Biases can influence how you understand and respond to information presented to you. However, critical thinking encourages you to question yourself and consider those alternate points of view you may have. Identifying biases can be helpful when analyzing company data, deciding what advertisement to run, and even making hiring decisions.
This is the ability to draw conclusions based on your given information. Without the ability to form the findings, it’d be hard to act after analyzing facts and data. Creating inferences is all about processing information and is a big part of being good at critical thinking.
Because critical thinking is objective and analytical, you need solid research skills to discover the facts and figures required to make your decisions or arguments. Every situation won’t require you to research the problem and potential solutions. However, strong research skills and deciphering information sources can ensure you gather the right stuff.
While it’s a little similar to researching and forming inferences, the identification skill is more about being able to identify problems as well as what may be influencing that problem. Without the identification skill, you likely won’t know when you’re in a situation where it may be beneficial to think critically. It’s also hard to solve a problem when you can’t identify what caused it in the first place.
Critical thinking is about questioning everything, and curiosity plays a significant role. While some people are naturally curious, it’s also a skill that can be learned. To practice curiosity and build up your skill, try approaching all situations with a “beginner’s mindset,” meaning you’re brand new to the case and know nothing about it. You’re there to learn with an open mind. This allows you to absorb further information and perceive things you likely didn’t notice before.
Throughout the critical thinking process, you’ll likely encounter loads of information when trying to solve problems. Still, not all of it will be relevant to your specific goals. This is where the skill of judging relevance comes in.
For example, when researching a topic online, Google will give you thousands of search results on just about everything that has to do with an issue. However, you won’t use all or even half of it. You’re constantly judging the relevance of the information presented to you to determine what’s valuable. Without this skill, you’d be wasting time on irrelevant details that prevent you from concluding.
Critical thinking is a worthwhile skill for any professional to master. Yet, it’s precious to data scientists working in the data science field. Critical thinking can have varying applications depending on the industry you’re in. There are essentially two aspects to learn:
- Developing a Question
- Questioning the Data
When you begin pursuing data science and set goals for gathering actionable insights, determine what question you want to answer or what problem you’re looking to solve. For example, the question could be, “How do we better reach our customers on social media?”
The ability to develop a question will likely require extensive research and interviewing to grasp what problems you’re hoping to solve. It’s essentially a collaboration between data scientists who thoroughly understand the data and business owners who thoroughly understand the business goals.
It takes solid critical thinking skills to develop a suitable question to test.
“The data scientist needs to be able to sift through the information while judging relevance when presenting a company’s goals.”
Critical thinking is like developing a scientific hypothesis. It needs to be testable, applicable, and efficient to work, and it takes strong skills to make that determination.
The second aspect of data science critical thinking is questioning the data. This is where many of the excellent essential thinking skills come into play. Experienced data scientists don’t dump data into spreadsheets or software and wait to see what the output is. Professional data scientists with strong critical thinking skills will analyze the data to discover adjustments, misleading errors, biases, or other distracting factors.
The ability to question data ensures that the data and insights gathered are the best they can be. Without challenging the data and thoroughly evaluating it, the insights you pull could be ineffective or false. Biases, input errors, and general holes in the data can all be vetted during the questioning process.
Missing this crucial step can result in embarrassing moments for your company. For example, suppose your data team shares insights on data they didn’t question. In that case, they could disclose incorrect information that jeopardizes your company’s reputation. Or these poor insights could lead you to spend money on projects that aren’t guaranteed to have the expected results.
In terms of the soft, non-technical skills needed to be successful in data science, critical thinking is often at the top of the list. Critical thinking can lead to better, well-informed, more precise decisions based on facts.
“Businesses need data scientists who can appropriately frame questions and understand the results they’re gathering to turn their findings into actions.”
Strong critical thinking skills allow you to see all angles of a problem. It is precious to business owners looking to level up their data usage and make more data-driven decisions.
When looking for data scientists, business owners will be looking for people who can maintain a detailed understanding of the business and its goals. They favor objectively analyzed data sets with the business’s best interests. This is becoming especially important in big data, as countless businesses are looking to tap into their data sources for insights.
Critical thinking skills are going to come into play here. Much like curiosity, some people are natural critical thinkers. However, it’s also a skill that can be learned and mastered over time by employing these practices:
- Asking Basic Questions
- Questioning Assumptions
- Being Aware of Your Mental Processes
- Try Reversing Problems
- Evaluate Evidence
- Think Independently
When you’re deep in the trenches of critical thinking or data analysis, things can get pretty complex, and you can lose sight of your original question.
To avoid this, ask yourself basic questions like, what do you already know, and how do you know that? What are you overlooking? This will help you reframe the original question or problem you set out to solve and keep you on your toes.
When building up your critical thinking skills, it’s important not to get complacent with the information around you. Just because something has always been one way doesn’t necessarily mean that’s the best way.
To generate new insights and dig deeper into the information around you, start questioning your basic assumptions about the world. This may be where you make new, innovative discoveries by examining the information already out there.
This is where your cognitive biases come into effect. Everyone has biases in their thinking. It’s a natural part of being human. However, being objective is a massive part when trying to think critically. So, taking stock of your biases and how they may influence decisions is essential.
Sometimes you may get stuck on a problem after you’ve spent ample time on it. Situations like this can be frustrating. However, you may be able to reposition the problem by reversing it. For example, say you’re trying to determine why one thing causes another to happen, and you can’t seem to figure it out. Try reversing the factors to see if that helps rewire your thought process and point you toward a solution.
When setting out to solve a problem, evaluating existing evidence to see what work has already been done in the area can be helpful. It removes some workloads and helps you learn how to problem-solve efficiently.
While researching and analyzing data and information is a big part of critical thinking, it’s crucial to remember to still think for yourself.
Critical thinking is a crucial skill in data science and analytics. Critical thinking is arguably one of the most essential skills a data scientist can offer. It strengthens their ability to dig deeper into the data to extract the most meaningful insights.
Critical thinking is a worthwhile skill for any professional to master. However, it’s precious to those in the data science field. Critical thinking can lead to better, well-informed, more precise decisions based on facts and data. It is a fundamental skill for those working in data science to add to their resume.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with the agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on
Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology,
statistics, and real-world applications. As a published author, coursework developer, and
Dissertation Committee Chair Tiffany still finds time for family and hobbies. Her non-linear career path
has given her an exclusive skill set that is virtually impossible to reproduce in another individual.