About this course
Data Science is like triathlon. Programming is cycling, by far the biggest investment is required in hardware and software. Running is domain expertise and communication skills and, swimming is mathematics, statistics and modelling. There are competitions in each of these disciplines cycling, running and swimming (and there always will be), but the need for super athletes who can do all 3 is growing. An athlete who is brilliant at one discipline can learn the other two and succeed in triathlon.
No matter your core discipline(s), this course will take you through a short triathlon. You will define a business problem, establish the data required to solve it, you will write scripts in R programming language to build a model to give you insights and you will learn to present your findings in a business format to a business audience.
Students will benefit from having a basic understanding of Statistics. Students with a basic knowledge of how Search Engines work may have a better appreciation for the case study context however, this is not a requirement.
Fireside Analytics Inc.
Shingai Manjengwa (@Tjido) is the Director of Insights and Analytics at Fireside Analytics Inc. An NYU Stern alum, she graduated from the Stern Business Analytics Masters program in 2014 and founded Fireside Analytics the following year. Fireside Analytics is a data analytics consulting company that makes data analytics and data science skills accessible to private sector companies, non-profits and education institutions. Fireside Analytics works with clients to build their data science capabilities and train their staff and stakeholders using customized case studies. Connect with us on Facebook, Twitter and LinkedIn.
Frequently Asked Questions
What web browser should I use?
The Open edX platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above.
Will I have to install new software in this course?
All the software you will need can be accessed through your browser at www.datasicentistworkbench.com. You will need to register and create an account. If you choose to not use Data Scientist Workbench, you may need to install R on your local computer.