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Updated in [June 30th, 2023]

This course, Data Science: Computational Thinking with Python, introduces students to the skills necessary to work with large amounts of data. Students will learn basic programming skills for manipulating data, and how to use Python to organize and visualize data. No prior experience with programming or Python is necessary, nor is any statistics background.

The course emphasizes learning through doing, and students will work on large real-world data sets through interactive assignments to apply the skills they learn. Examples from a variety of domains will be used to illustrate the power of data science. Throughout, the underlying thread is that data science is a way of thinking, not just an assortment of methods. Students will also hone their interpretation and communication skills, which are essential skills for data scientists.

[Applications]
Upon completion of this course, students will be able to apply the computational thinking and data science skills they have learned to their own projects. They will be able to use Python to organize and manipulate data, visualize data effectively, and interpret and communicate data science results. Students will also have a better understanding of the underlying principles of data science and be able to apply them to a variety of domains.

[Career Path]
One job position path recommended for learners of this course is Data Scientist. Data Scientists are responsible for analyzing large amounts of data to uncover trends and insights, and then using those insights to inform decisions and strategies. They use a variety of tools and techniques, including programming languages such as Python, to analyze data and develop models. They also need to be able to communicate their findings to stakeholders in a clear and concise manner.

The development trend for Data Scientists is to become more specialized in their field. As data becomes more complex and the demand for data-driven decisions increases, Data Scientists are expected to become more knowledgeable in specific areas such as machine learning, natural language processing, and deep learning. They will also need to be able to work with a variety of data sources, such as unstructured data, and be able to interpret and explain their findings to stakeholders.

[Education Path]
The recommended educational path for learners of this course is to pursue a degree in Data Science. This degree typically involves a combination of courses in computer science, mathematics, and statistics, as well as courses in data analysis and visualization. The degree also typically includes courses in machine learning, artificial intelligence, and natural language processing. As the field of data science continues to evolve, the degree may also include courses in data engineering, data governance, and data ethics. The degree culminates in a capstone project, which allows students to apply the skills they have learned to a real-world problem.