It is said that the demand for data scientists outpaces supply. Companies are hiring data scientists to tackle what they perceive to be their toughest problems. But what if that's not the right strategy? I believe that companies are usually not facing one massive problem to be solved by highly skilled data scientists, but likely are facing many small to medium sized interconnected problems. Rather than depending on data scientists, I believe that these problems and potential solutions are more likely to be uncovered by subject matter experts who have been enabled to explore the data themselves. This strategy will not only build capacity but will ensure sustainability.
I started my analytic journey with databases and I've been hooked ever since. As a natural progression, I moved from databases to business intelligence and dashboarding. Being able to more easily visualize the data allowed me to consider broader questions like "how different are these trends really?" and "how can we use this data to better perform?" This led me into the path of data science. The popular notion that predictive models and statistical methods could definitively answer complex problems was alluring. I have since learned a wide range of statistical models and techniques. I believe this has allowed me to demystify the concepts to understand both their value and constraints.
Laura Ellis is a data geek who aims to make data science and analytics accessible to everyone.
Tools and Languages
R, Python, SPSS Statistics & Modeler, SAS, Advanced Excel Scripting (Visual Basic), Chartio, Cognos, Postgres, IBM Certified DB2 Database Associate, Amplitude, Segment, Certified Project Management Professional (PMP®)
Data Analysis, Statistical Analysis, Predictive Modelling, Business Intelligence & Dashboarding, Data Applications, Project Management & Tooling, Operations, Process Engineering, Partner Marketing Programs, Technical Training
Master of Science in Predictive Analytics - Northwestern University
Bachelor of Engineering Science, Software Engineering - University of Western Ontario
I have worked at IBM in various positions within the data related area for 13 years. Thoughts and opinons experssed are my own.