Today I was introduced to many new things - and these discoveries have me excited!
Just going to note before hand, that these check-ins will often be short - for I am trying to learn as much as possible in the time I have before and after work and around other responsibilities and extra curriculars!
Learning R#
Today I officially started Harvard’s Data Science Professional Certificate course to get me started in R. So far, learning R has felt like a breeze since I am so use to learning new syntax in different languages.
I downloaded the R software onto my Macbook Air - then downloaded R studio. Once I opened those up and started learning some code, I began to really feel like I falling into the flow of self-learning again.
Look above! My first data visualization in R was taught to me by Professor Rafael A. Irizarry.
It looks like the course is currently on the first edition of his textbook Introduction to Data Science. Therefore, I will also be reading his second edition on the side, while completing the course assignments. It is split into two parts: Data Wrangling and Visualization with R and Statistics and Prediction Algorithms Through Case Studies.
I find Harvard’s method of learning very impactful and motivational for me, because I’d rather work with actual case studies (interesting) instead of things that are made up (not interesting). I remember part of the reason I had struggled with math in middle school was because of the lack of real-life use cases. However, as soon as I found something on the internet that was using real-life examples - I was earning an A+ on the next exam! I am so excited to continue.
Neuroimaging and Data Science?#
The book is called, well - you guessed it: Data Science for Neuroimaging. I admit, I was not able to read much of it today - but it seems to be easily digestible for someone who at least has a background in programming and computer science. I am excited to read more and give some more thoughts on this tomorrow.
I found this book to be specifically helpful for figuring out the kind of datasets I may want to do a personal project on in the near future. Two quotes in the introduction have made me really want to continue reading this book.
we can gain a lot from borrowing methods from other fields in which data has become ubiquitous, or from fields that are primarily interested in data, such as statistics, and some parts of computer science and engineering. These fields have developed a lot of interesting methods for dealing with large and high-dimensional datasets, and these interdisciplinary exchanges have proven very powerful.
This book focuses on neuroimaging, but large, complex, and impactful datasets are not unique to neuroscience. Datasets have been growing in many other research fields – arguably, in most research fields in which data is being collected. Here, we will present just a few examples, with a focus on datasets that contain images, and therefore, use analysis tools and approaches that intersect with neuroimaging data science in significant ways.
I just want to thank Ariel Rokem and Tal Yarkoni in advance for producing material that will make learning data science all the more interesting.
