Datacast Episode 12: Data Science in Consulting with Jim Leach
In the 12th episode of Datacast, I had a conversation with Jim Leach, a data scientist at the consulting firm KPMG. Give a listen to learn about challenges in applying data science in consulting, his experience getting a Master degree in Business Analytics at Imperial College of London, the benefits of teaching to others, and many more.
Jim Leach is a data scientist, originally from the North of England, but now based in Atlanta, Georgia. After studying chemistry at university, Jim joined the consulting firm KPMG and began his career as a data analyst. He later returned to university, taking a sabbatical to study for a Master in Business Analytics at Imperial College, London. In his work, he is a passionate R user and developer and enjoys thinking about data visualization and how to communicate effectively using data. In his free time, he enjoys board games, music, cooking, and being outdoors.
- (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
His Recommended Resources
- R For Data Science Learning Community
- R For Data Science Slack Channel
- “What’s in a name” from Lyft Engineering Blog
- “Thinking, Fast and Slow” by Daniel Kahneman
If you enjoyed this piece, I’d love it if you hit the clap button 👏 so others might stumble upon it. You can find my own code on GitHub, and more of my writing and projects at https://jameskle.com/. You can also follow me on Twitter, email me directly or find me on LinkedIn.