Data Transformers Podcast
Data Transformers Podcast
Combining the passions of Data and Teaching with Laura Ellis
/

Episode Title : Combining the passions of Data and Teaching with Laura Ellis

Episode Summary:

Laura Ellis, IBM Cloud systems Architect, always wanted to be a teacher. A recognition here and an award there in Computer Science got Laura interested in computer science and later a job with IBM. Laura combined her passion for teaching with DB2 and toured the world training others in DB2. As the business intelligence started picking up in 2013, Laura completed a part time MS in Predictive analytics and switched in data science. Laura realized that the organization needed people with other skills in data engineering, data wrangling etc and adapted. Laura started Little Miss Data as a personal project to combine data science with her personal passions such as Peloton R and teaching kids about data science. Laura believes that the future trend is about data security and ethics. 

Topics discussed in this episode:

1:40: Passion for data. Data found Laura. Luck and encouragement. Computer science award in college for a project. Little Miss Data is about teaching people about Data. Ended up getting an interview for a comp science job. Found a job teaching about DB2 / Data warehousing worldwide.

05:00: MS in Predictive data analytics. Why? BI was exploding around 2014 so decided to get a part time MS in predictive analytics at Northwestern.

09:00: Reasons for data analytics explosion around 2014? Availability of data. Availability of compute. People started looking for insights rather than just charts & reporting.

11:00 (Headliner around 13:00): Why predictive analytics? Wanted to learn about statistical modelling. Also, people were asking questions about predictions and differences in data.

14:30 (HEADLINER) : Why are good data scientists leaving the field? Misalignment on expectations. Data scientists don’t expect to do data wrangling and data engineering. Started working on making sure that good data was in good hands. Expectations may be that company needs data engineers, data communicators etc.

18:42 (Headliner): What skills are needed for data scientists? A good level of subject matter of expertise. Gotchas are not in how the models are. Should spend a lot of time understanding the business.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

Ramesh consulting website: https://DigitalTransformationPro,COm

Data Transformers Podcast

Listen Now!

Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.