Datacast Episode 30: Data Science Evangelism with Parul Pandey

Datacast Episode 30: Data Science Evangelism with Parul Pandey

Parul Pandey is a Data Science Evangelist at H2O.ai. She combines Data Science, evangelism and community in her work. Her emphasis is to break down the data science jargon for the people. Prior to H2O.ai, she worked with Tata Power India, applying Machine Learning and Analytics to solve the pressing problem of load sheddings in India. She is also an active writer and speaker and has contributed to various national and international publications including Towards Data Science, Analytics Vidhya, and KDNuggets and Datacamp.

Datacast Episode 29: From Bioinformatics to Natural Language Processing with Leonard Apeltsin

Datacast Episode 29: From Bioinformatics to Natural Language Processing with Leonard Apeltsin

Dr. Leonard Apeltsin is a research fellow at the Berkeley Institute for Data Science. He holds a Ph.D. in Biomedical Informatics from UCSF and a BS in Biology and Computer Science from Carnegie Mellon University. Leonard was a Senior Data Scientist & Engineering Lead at Primer AI, a machine learning company that specializes in using advanced Natural Language Processing Techniques to analyze terabytes of unstructured text data. As a founding team-member, Leonard helped expand the Primer AI team from four employees to over 80 people. Outside of Data Science and ML, Leonard enjoys scuba diving, salsa dancing, and making short documentary films.

Datacast Episode 28: Excelling in Data Analytics with Vincent Tatan

Datacast Episode 28: Excelling in Data Analytics with Vincent Tatan

Vincent Tatan is a Data and Technology enthusiast with relevant working experiences from Google LLC, Visa Inc., and Lazada to implement microservice architectures, business intelligence, and analytics pipeline projects. Vincent is a native Indonesian with a record of accomplishments in problem-solving with strengths in Full Stack Development, Data Analytics, and Strategic Planning. He has been actively consulting Singapore Management University’s Business Intelligence and Analytics Club, guiding aspiring data scientists and engineers from various backgrounds and opening up his expertise for businesses to develop their products. Vincent also opens up his one on one mentorship service to coach on landing your dream Data Analyst/Engineer Job at Google, Visa, or other large tech companies.

Recommendation System Series Part 4: The 7 Variants of Matrix Factorization for Collaborative Filtering

Recommendation System Series Part 4: The 7 Variants of Matrix Factorization for Collaborative Filtering

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 4 looks into the nitty-gritty mathematical details of matrix factorization, arguably the most common baseline model for recommendation system research these days.

Datacast Episode 27: Feature Engineering with Ben Fowler

Datacast Episode 27: Feature Engineering with Ben Fowler

Ben Fowler has been in the field of data science for over five years. In his current role at Southeast Toyota Finance, Ben leads the end to end model development process to solve the problem of interest. Ben holds a Master of Science in Data Science from Southern Methodist University, graduating in 2017. Following graduation, Ben has been a guest speaker to the SMU program multiple times. Additionally, Ben has spoken at the PyData Miami 2019 and PyData LA 2019 Conferences and has spoken multiple times at the West Palm Beach Data Science Meetup.

Datacast Episode 26: From Cognitive Neuroscience to Reinforcement Learning with Arthur Juliani

Datacast Episode 26: From Cognitive Neuroscience to Reinforcement Learning with Arthur Juliani

Arthur Juliani is a Senior Machine Learning Engineer at Unity Technologies, where he has worked as a founding member of the ml-agents GitHub project as well as the leader of the Obstacle Tower project. He is also currently a Ph.D. candidate in Cognitive Neuroscience at the University of Oregon, where he studies computation models of spatial representation learning in humans.