As both popular pastimes and fast-growing industries, electronics and robotics are extremely prevalent in modern society. Whether you wish to become a hardware engineer, or just want to explore a new passion, today's conversation has a lot to offer.

As a self-taught electronics hobbyist and high school physics teacher, Gaven MacDonald has had a lot of experience working with robotics and the Raspberry Pi. Being today's guest, Mr. MacDonald explains how he got started in electronics and how that led him to creating his own Raspberry Pi tutorial videos. Having seen great success with his YouTube channel, Mr. MacDonald shares inspiration and advice on how to get started creating your own content. In this wide-ranging conversation, we discuss his shift from theoretical to application-based knowledge, different approaches to getting started in robotics, and how to develop an audience for your content.

Relevant links:

As citizens of the digital age, it is no secret that we are surrounded by data. From predicting the results of the Oscars to modelling COVID-19 re-opening frameworks, we see the relevancy of data science in just about, well, everything. But, wait a minute — what is data science, and what is data? These seemingly-simple questions have sparked a lot of discussion, and they're questions that today's guest, Xiao-Li Meng, Professor of Statistics at Harvard University, has thought a lot about. 

As a researcher and professional statistician, Prof. Xiao-Li Meng has made many significant contributions to the field of data science, one of the most notable being founding the Harvard Data Science Review (HDSR). Gleaning inspiration from the Harvard Business Review and Harvard Law Review, Xiao-Li created the HDSR with the goal of it being three things: a popular magazine, a leading education publication, and a premier research journal. Having achieved those aspirations with much success, Xiao-Li speaks to his philosophy behind publishing articles in the HDSR that can be enjoyed by everyone, no matter if you're a professional data scientist, philosopher, quantum physicist, or just someone with an interest in data. In this episode, Xiao-Li shares stories from his academic background and walks us through his training as a pure mathematician while debunking common misconceptions about data science. 

Relevant links:

Harvard Data Science Review (Specific columns mentioned: Minding the Future, Recreations in Randomness) 

Professor Meng's Personal Academic Site

If you search up the word 'scientist' on Google Images, it can be guaranteed that you will see a plethora of white lab coats, pipettes and Erlenmeyer flasks. Now, search up the term 'data scientist,' and the images are much less robust. This simple comparison accurately portrays the depiction of data scientists in modern society. While there exists preconceived notions on what other types of scientists are, the public knowledge becomes much more obscure when it comes to data scientists — what exactly do they do, and what do they look like?

Coined by the Harvard Business Review as the 'sexiest job of the 21st Century,' there is a growing need for professionals who are able to work with, analyze, and make sense of data. In this episode, Laura Gutierrez-Funderburk from Callysto offers insight into what she does in a day in her life as a data scientist, how she developed the soft and technical skills required for the occupation, as well as how she found communities through attending hackathons and conferences. Laura also discusses how being a woman and a member of a minority group affected her experience in tech, and the importance of trusting the process and listening to your gut feeling.

From politics to literature, statistics and data are all around us. As an ambassador from Callysto and a Wikipedia-enthusiast, our guest in this episode is certainly no stranger to data science education. Through his experience as an educator, David Hay discusses the mission of the Callysto project as well as how students and teachers can use their (free!) online resources. In this conversation, David shares what data science education looks like from elementary-high school, what reform is needed to advance statistics and data-literacy curriculums, as well as how students can expand their learning beyond the classroom. 

Websites mentioned in this episode: Callysto Project, OpenParliament.ca

Ever wanted to get into artificial intelligence but unsure where to start? Have an interesting research question but don't know how to share your findings? This episode's guest Jeffrey Pan is an avid programmer, an artificial intelligence enthusiast, and on the side, a high school student. As a believer in the power of Generation Z, Jeffrey is the founder of Youth AI Lab, an organization created to build AI communities among youth and make technology accessible to everyone. In this episode, Jeffrey recounts his experiences in research, discusses his motivation behind founding Youth AI Lab, and offers pertinent advice to students of all ages who wish to pursue their interests in programming and technology.

Relevant links: Youth AI Lab Website, Youth AI Lab YouTube Channel

It's easy to think of statistics as nothing more but finding the mean, median and mode: after all, that is all that is taught in elementary school data-management curriculums. This doesn't faze our guest today, Mr. Dash Young-Saver, AP Statistics teacher and founder of Skew the Script. Mr. Young-Saver founded Skew the Script with a mission to make accessible and engaging AP Statistics lessons through using real-world scenarios to teach statistical concepts. In this episode, I sit down with Mr. Young-Saver to discuss his journey in data science, the value of data science education for high school students, and how data can be used as tool to both make new inferences as well as refine existing viewpoints. Throughout this wide-ranging conversation, we touch on the difference (or the lack thereof) between traditional statistics and data science, as well as the shift from math being a science of complete predictability to being comfortable with uncertainty and naming that you are uncertain. Mr. Young-Saver also shares some advice to current precollege students on how they can explore their passions in data science outside the classroom. 

Links mentioned in this episode:

Skew the Script

From tomorrow's weather forecast to astrophysics, data science is everywhere. So it comes as no surprise that despite being a new field, the need for professionals in data science has grown tremendously in the past decade. As our society becomes increasingly more data-driven, the gap between the demand for data scientists and the number of students pursuing the field is becoming more and more pronounced. Why is the growing need for data scientists not accompanied by an equal amount of growth in the number of data science students? How can learning statistics change your outlook on life? What is the value in being data-literate, even if you're not planning on pursuing a degree in data science? 

Our guest in this episode offers his thoughts on all these questions. Radu Craiu, Professor and Chair of the Department of Statistical Sciences at University of Toronto, joins us on Behind the Curves as we discuss the importance of early exposure to statistics, the value of probabilistic thinking in our everyday lives and the need for a change in high-school curriculums as the world around us changes as well. 

Websites mentioned in this episode:

Harvard Data Science Review, Prof. Craiu's Personal Site