Upon completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.
You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. Anyone with a passion for learning can take this Professional Certificate – no prior knowledge of computer science or programming languages required – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist. It’s a myth that to become a data scientist you need a Ph.D. This Professional Certificate will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills. If you were one of the people whose questions I didn’t get to at the end of the webinar, feel free to post below or on RStudio Community.Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. You can watch a recording of the webinar here, and flip through the slides here. In the webinar I went through the logistics of setting up a course in RStudio Cloud, shared practices that worked well for me in mid-to-large sized courses, and walked through some screen captures of the experience of navigating RStudio Cloud from the perspective of the instructor and the student. Circumventing the installation step early in the course by having students access R and RStudio in the cloud can minimize frustration and improve buy in, and RStudio Cloud is one (super convenient!) way of enabling your students to access RStudio in their browser – and you don’t need to know how to set up a server or the help of an IT professional! Last week I gave a webinar on using RStudio Cloud as a lightweight infrastructure solution for Step 1 of Option 2 above. Install the following packages: tidyverse, rmarkdown, etc. If you’ve heard me talk about teaching R, you’ve probably heard me mention the following day one dilemma: Option 1 😰ģ. However a common friction point for getting students (and faculty) started with computing is installation and setup.
Much has been written in statistics and data science education literature about pedagogical tools and approaches to provide a practical computational foundation for students.
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