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test or debug their programs.
At Bentley University, our approach to
teaching Python evolved as we realized why students are taking the course. Python is known for being a language that supports data analytics applications. Many introductory Python college textbooks don’t teach this topic, opting instead for chapters on turtle graphics or user interface development. We omitted these topics to include instead units on some of the data analysis and graphing modules, enabling students to develop practical and employable skills using Python.
CT: What are a few more of the skills your students learn that factor strongly into developing computational thinking? What are some of the applications they prepare for that frame business problems using a computational thinking approach?
Frydenberg: Students can learn computational thinking skills without learning Python specifically (or any coding language, for that matter). Computational thinking focuses on automating solutions to real problems and determining that those solutions work correctly.
While computational thinking is an important skill when learning to code, the ability to solve problems from several domains and disciplines will benefit both students and professionals in the workplace. Business today relies on data and the ability to make sense of it quickly. Knowledge professionals evaluate everything from the performance of stocks, to voting patterns of different demographic groups, to analyzing the impact of news
headlines on social media.
Tracking customer behavior, determining
factors that lead to a more marketable product (decomposition and abstraction), evaluating similarities between products or social media trends (pattern matching) and predicting, interpreting and visualizing sales data (algorithms) are all examples of the application of computational thinking skills.
CT: Will data skills be recognized as valuable assets as our students approach their first professional experiences?
Frydenberg: Sure. For example, selecting the appropriate type of graph to help illustrate data will be crucial to being able to interpret data from different disciplines using many formats. Storytelling with data is an important skill especially for those who are exploring data science or data analytics fields as possible career choices.
As the role of data becomes more valuable to organizations, students need be able to analyze data and then present their findings or conclusions in plain language for everyone to understand. Computational thinking skills help students master analyzing the data and sharing their findings.
Computational thinking and data skills will be well recognized professional skills as our students begin to make connections between how they approach problems and develop solutions in a variety of disciplines. Mastering problem-solving skills will impact student success throughout their college careers and as they take their places as information workers.
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