Our Data Analytics Instructors
Our team of experienced, full-time instructional faculty utilize real-world case studies to teach best practices in statistical analysis, machine learning, natural language processing and data visualization that will prepare you for a successful career in Data Analytics.
Juliana is a data and computational scientist as well as an experienced instructor. She received her Ph.D. in computational chemistry from the University of Texas at Austin where she developed a novel saddle point finding method, applied machine learning techniques to accelerate molecular dynamic simulations, and used computational methods to simulate chemical processes. As an Assistant Professor of Practice at UT Austin, Juliana led and created all curricula for an undergraduate computational science research program. She also led a research group that used high throughput computing and machine learning to find novel materials for catalysis, and explored global optimization methods.
Michelle Hoogenhout is a behavioral data scientist with a background in genetic and cognitive neuroscience. Previously, as the senior data scientist and tech team lead at Umuzi Academy, South Africa, she developed and taught the data science curriculum, implemented the candidate selection and data ETL process and led a team of 7 web developers, data scientist and data engineers. Michelle holds a PhD (Psychology) from the University of Cape Town and a postdoctoral training fellowship from the Broad Institute of Harvard and MIT. Her doctoral work focused on predicting empathy in autism spectrum disorders through measures of autonomic nervous system and muscle reactivity. She has published and blogged on topics such as statistics and data management, data science training methods, cognitive assessment, and child development.
Rongpeng (Ron) Li
Ron is a data analytics instructor and a senior data scientist at Galvanize. Ron received two master's degrees from USC, one in electrical engineering and another in physics. Before joining Galvanize, Ron was a research programmer at Information Science Institute. Ron has research/engineering experience in computational physics, institutional research, quantum computation, knowledge graph and finance. Ron authored/co-authored several peer-reviewed publications and a book called Statistics for Data Science.
Ron has been an avid mentor and instructor in data science communities both online and offline. Ron is conversant with data science but his happiest teaching experience is solving a student's question that he can't answer immediately.
Take your first step toward a new career and start the conversation about Data Analytics.