Are you getting started in data science and want to know more about what tools are out there? Or maybe you have used the same tools for a while and you want to try something new? Then, get ready for a deep dive into building your very own data science toolkit! This talk is aimed at beginners who are entering this dynamic field and want to expand their data science tool knowledge. I will highlight 8 important components of a data science toolkit (since there are 8 bits in a byte), such as programming languages, integrated development environments (IDEs), text editors, online resources, and more.

You will be guided through essential components of an effective toolkit, such as exploring the differences between your choice of coding playgrounds like Jupyter Notebooks and RStudio. I will navigate through foundational data manipulation libraries such as Pandas and dplyr, and showcase some of my favorite data visualization libraries including Seaborn and ggplot2. As I venture into the realm of machine learning, the audience will gain insights into some of the simplest ways to turn algorithms into models using scikit-learn and caret. The importance of stellar documentation will be emphasized because how else will we be effective technical communicators and collaborators if we cannot explain our work?

At the end of the talk, you can expect to leave with not only a fundamental understanding of each toolkit component but also practical insights and resources so you can embark on your own toolkit journey. So, join me in constructing your data science toolkit and build those data masterpieces!

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