Show Notes:

• (2:16) Jim recalled his experience getting a Bachelor Degree in Chemistry at the University College of London.


• (4:01) Jim talked about the analytical skills that he got out from his Chemistry degree.


• (5:09) Jim gave a brief background overview of his employer KPMG, one of the big 4 consulting firms.


• (6:56) Jim shared the major challenges of applying scientific rigor to identify and quantify business opportunities using data.


• (9:42) Jim reflected on his professional growth working after 3 years working as a data analyst at KPMG.


• (12:23) Jim explained his motivation behind his decision to pursue a Masters in Business Analytics at Imperial College of London.


• (16:27) Jim recalled the most useful courses he took during his Master degree (Graph Analysis on the technical side and Marketing on the business side).


• (18:40) Jim talked about the importance of learning econometrics for a data scientist.


• (21:22) Jim talked about the benefit of teaching materials to other people that contribute significantly to his career, which he wrote about his post “Lessons learned teaching R.”


• (23:51) Jim recently wrote a blog post about his experience attending the RStudio Conference at Austin in January, in which he shared several principles for teaching.


• (28:35) Jim started working at the KPMG office in Atlanta starting January 2018.


• (31:10) Jim talked about his blog post called “Do the simple things first,” in which he argued that “a complex method is never justified until a simple one has been tried first.”


• (35:21) Jim talked about the use of machine learning for his projects at KPMG.


• (37:03) Jim shared some resources to learn data engineering, including learning SQL and reading “R For Data Science.”


• (40:40) Jim shared the key developments in the R ecosystem in 2019 that he’s most excited about, including caret and tidyverse.


• (44:46) Jim gave his prediction on how data science will evolve in the next 5 years.


• (49:04) Jim anticipated his career trajectory.


• (49:49) Closing segments.

His Contact Info:

• LinkedIn


• Twitter


• Website


• GitHub

His Recommended Resources:

• R For Data Science Learning Community


• R For Data Science Slack Channel


• "What’s in a name” from Lyft Engineering Blog


• Spotify


• RStudio


• DataCamp


• “Thinking, Fast and Slow” by Daniel Kahneman

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