
asemakula

asemakula
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Portfólio
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Educação
Bachelor of Computer Science
2003 - 2006 (3 years)Qualificações
Content Strategy for Professionals (2013)
Northwestern UniversityOnline Certificate Course on Content Strategy. The Content Strategy MOOC is for professionals at all levels of a for-profit, non-profit, volunteer or government organization who want to significantly improve their abilities to understand audiences and develop strategic words, pictures, graphics, and videos to convey their organization’s most important goals.
Human Computer Interaction (2013)
University of California, San DiegoThe course involved learning how to design technologies that bring people joy, rather than frustration. Learning several techniques for rapidly prototyping and evaluating multiple interface alternatives -- and why rapid prototyping and comparative evaluation are essential to excellent interaction design. Learning how to conduct fieldwork with people to help you get design ideas. How to make paper prototypes and low-fidelity mock-ups that are interactive -- and how to use these designs to get feedback from other stakeholders like teammates, clients, and users. Learning principles of visual design so that you can effectively organize and present information with your interfaces. Learning principles of perception and cognition that inform effective interaction design. And you'll learning how to perform and analyze controlled experiments online. In many cases, we used Web design as the anchoring domain.
Introduction to Data Science (2014)
University of WashingtonPart 0: Introduction Examples, data science articulated, history and context, technology landscape Part 1: Data Manipulation at Scale Databases and the relational algebra Parallel databases, parallel query processing, in-database analytics MapReduce, Hadoop, relationship to databases, algorithms, extensions, languages Key-value stores and NoSQL; tradeoffs of SQL and NoSQL Part 2: Analytics Topics in statistical modeling: basic concepts, experiment design, pitfalls Topics in machine learning: supervised learning (rules, trees, forests, nearest neighbor, regression), optimization (gradient descent and variants), unsupervised learning Part 3: Communicating Results Visualization, data products, visual data analytics Provenance, privacy, ethics, governance Part 4: Special Topics Graph Analytics: structure, traversals, analytics, PageRank, community detection, recursive queries, semantic web Guest Lectures
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