With the increased need of many industries to adopt data science and machine learning techniques in their daily business, one has to wonder what it takes for an individual to become a valuable resource and fill in the skill shortages we hear so much about.
Between now and July 21, reply to this post with your questions on how to build a successful data science and AI profile. Questions are welcome on the following topics:
- How to seek out essential experiences to prepare you for this profession – including how to find a mentor, how to seek exposure to data, and how to participate in projects/work with others
- How to expand your analytic tool kit: Python, SPSS, SAS, R, etc.
- Expected techniques such as basic classification methods and data visualization tools
About the Q&A Hosts
Sepi is a Data Scientist/Machine Learning Engineer on the IBM Data Science Elite (DSE) team, based in San Francisco. After completing her PhD at the Center for Pattern Analysis and Machine Intelligence at University of Waterloo, she started her journey as a Big Data & Analytics consultant. Sepi joined IBM as an open source solution engineer, working for IBM Canada for 2 years where she was honored to be awarded “The Best of IBM” 2018. She really enjoys being on the DSE team and applying AI in real world applications to help customers on their data science journey. She enjoys working with DSE team members who are top talent in the field.
Sepi is a speaker at conferences, meetups and summits, and she is passionate about sharing recent trends in technology with everyone. She mostly uses open source tools in Watson Studio, with her recent passion being around detecting model bias using Watson Open Scale to ensure Machine Learning models provide fair decisions with trust and transparency.
In her free time, Sepi goes hiking, scuba diving, and biking. She has a passion for technology and always tries to keep herself up to date about the recent trends and innovations in the field.
Carmen-Gabriela Stefanita, PhD
Carmen has a background in physics and advanced mathematics spanning work on three continents in different countries. After her PhD in physics from Queen’s University in Ontario, Canada, she continued to work in academia before finding her way back to industry. Her academic projects covered areas of stochastic analysis in nondestructive testing, modeling and building of sensors and devices, as well as quantum computation. In her journey to data science, Carmen has built AI models in e-commerce, ad-tech and fintech. As a senior member of the Data & AI Elite team at IBM, Carmen continues to help customers develop machine learning solutions for real life applications with models in telecommunications and manufacturing.
Carmen is also an author, inventor and entrepreneur with a passion for finding innovative solutions for today’s AI strategy. In her free time, Carmen enjoys swimming and is a devoted world traveler. You can connect with her on LinkedIn www.linkedin.com/in/cgstefanita to continue the conversation.
Andre is part of the IBM Data Science Elite team and supports client engagements that involve machine learning and artificial intelligence tasks.
Andre has a Master’s degree in analytics/data science and almost 10 years of digital analytics experience. He specializes in retail and consumer analytics with experience coming from companies like Zappos, Nike, and SAS. Andre has worked with several data platforms (Oracle, Hadoop, AWS) using a variety of open source tools, primarily R and Python. Andre enjoys building relationships and is very intellectually curious with a passion for solving real world business problems that make impact.
On his off time, Andre enjoys exercising, watching sports, and spending time with his family. He is a frequent walker of various environments (outside, trails, beaches, airports, malls, etc.) and tries to be as active as possible to overcome his uncontrollable sweet tooth.