Data science is about solving real-world problems, so it’s unsurprising that certain skills are a valuable asset in their ever-evolving toolkit. Computational thinking should be a core educational path for any aspiring data scientist as it involves basic concepts regarding computer science and how to use abstraction and decomposition when addressing complex problems.
In the digital-first era, computational thinking is a critical skill, not only for future data scientists but for anyone who wants to participate in the computational world. According to the U.S. Bureau of Labor Statistics, demand for data science-related jobs, such as computer and information research scientists, will grow 22 percent from 2020 to 2029 — faster than any other occupation.
To prepare for the future of work and the progression of the job market that is being shaped by pervasive automation, artificial intelligence, and machine learning, computational thinking skills must be emphasized as a core component of education and career development.
Computational Thinking Explained
Occasionally referred to as algorithmic thinking, computational thinking is a systematic approach towards solving a complicated problem by breaking it down into smaller, simpler steps in a way that it can be executed by a computer or machine. Solving a problem in a way that a computer can execute the process is important because it means that the solution can be applied towards similar problems in other scenarios. Part of computational thinking is embracing an agile, creative, and adaptable mindset to work through problems and potential solutions most efficiently, and using and interpreting data effectively.
Computational thinking originates from the type of thinking utilized by computer scientists, but is acknowledged as a method of thinking that anyone can use to solve problems in both their personal or professional life. So the point isn’t to apply reasoning that mimics computers, but to develop problem-solving techniques that computer scientists typically rely on.
Numerous data-intensive and quantitative problems can be solved through computational thinking, which makes this a significant asset for data scientists. This approach can be applied for problem solving across numerous areas like artificial intelligence and mathematics, for example. The programming language Python is also important in this process as it’s used in the statistical analysis process to express the solution on a computer.
Generally, there are four pillars of computational thinking:
- Decomposition: Reducing a complex problem into more negligible parts.
- Pattern recognition: Searching for any similarities throughout the problems.
- Abstraction: Honing in on only the most crucial information and disregarding the more inconsequential pieces of information.
- Algorithms: Creating a deliberate, step-by-step solution or rules to address the problem.
Usually computational thinking can be applied to highly structured problems; for example, the framework for the specific problem demands a highly structured solution, like an algorithm. Recurrent problems are also ideal for a computational thinking approach.
Educational systems are focusing on offering curriculums that incorporate computational thinking practices. New Zealand, Australia, South Africa, and other countries have already started to incorporate computational thinking into curriculums for grades K-12, and educational systems across the U.S. are increasingly featuring courses around computational thinking, as well.
As our world becomes more interconnected and moves towards a digital economy, critical thinking skills and mindsets will be essential to supporting new ways of working and engaging with technology to fuel innovative new services, solutions, and tools in multiple contexts.
Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.
Computational Thinking for an Evolving Future of Work
The next generation of data scientists needs to develop the skills that will help them better adapt to the changing job market and emerging digital economy. Computational thinking will prove to be a valuable resource for future data scientists who must continuously reshape their roles around the progression of technology and closer integration between humans and machines.