Innovative Teaching for Tomorrow’s Careers: Insights from Applied Data Science with Python

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Innovative Teaching for Tomorrow’s Careers: Insights from Applied Data Science with Python

By: Talia Kolodny, Partner Community Manager

To continue our series highlighting Coursera’s Outstanding Educator Awards, we’re thrilled to feature Dr. Christopher Brooks, winner of the Outstanding Educator Award for Innovation. Read on to discover the innovative teaching strategies that Dr. Brooks uses in the Applied Data Science with Python Specialization.

#1 Teaching globally, learning locally

“We are teaching data science skills to a global population but doing it by leveraging local data.â€� – Dr. Brooks

The learners in this Specialization are very diverse. They include seasoned veterans of data science, as well as advanced novices, such as learners who finished the Python for Everybody Specialization and want to get into data processing and analysis.

“We’ve heard from learners around the world that this opened up new learning opportunities for them, empowered them as they progressed through traditional higher education, helped them decide what courses to take in university, and led to job mobility both across and within sectors.â€� – Dr. Brooks

What’s unique about the Applied Data Science Specialization is how the team uses real data sets that allow learners to gain practical experience. Using real data sets allows learners to solve problems that are relevant to their own communities. For example, in the Applied Plotting, Charting & Data Representation in Python course, learners are given ten years of weather data and asked to identify record-breaking values from an eleventh year of data. They have to apply solid design principles to make a visually compelling final plot.

#2 Low-stakes practice makes all the difference

Taking full advantage of Coursera’s in-browser coding features, this Specialization helps learners gain real-world data science skills through the power of python coding. By providing opportunities to practice through executable code blocks, these instructors set learners up for success on their scaffolded Jupyter notebook assignments.

“One new feature in the platform we made use of was the ability to ask within a video for learners to solve a small sample of code. Learners loved this functionality, as it ties the practice to the lectures in a way we haven’t been able to do in online settings before.�

One learner from a small town in India wrote to the course team, “I have taken this course out of interest in Data Analytics. I just loved the course. The assignments were real time and encouraging. The assignments were the main factor for me completing the course ahead of time. I would like to thank all the professors who taught this course. Thank you very much!�

#3 Research leads to pedagogical innovation

In this Specialization, the course team used the ability to conduct content A/B testing in order to test the effectiveness of different teaching strategies and reveal various videos’ unconscious effects on a diverse group of learners. This research has the potential to impact a broad range of learner needs and address their difficulties. We can’t wait to see the insights that will come!

“We learned lots of lessons coming out of this experience, but one was the ability to try different pedagogies or approaches across a large population. A/B Testing is nearly impossible to do in a residential campus setting.� -Dr. Brooks

Applied Data Science with Python is impacting aspiring Data Scientists around the world with effective online teaching strategies. We look forward to sharing more stories of success!

 

The post Innovative Teaching for Tomorrow’s Careers: Insights from Applied Data Science with Python appeared first on Coursera Blog.

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