Course Description

 

Keynotes

  • Courses



  • Maria Girone
    (European Organization for Nuclear Research) [-]
    Big Data Challenges at the CERN HL-LHC


    Paolo Addesso
    (University of Salerno) [introductory/intermediate]
    Data Fusion for Remotely Sensed Data


    Thomas Bäck & Hao Wang
    (Leiden University) [introductory/intermediate]
    Data Driven Modeling and Optimization for Industrial Applications


    Paul Bliese
    (University of South Carolina) [introductory/intermediate]
    Using R for Mixed-effects (Multilevel) Models


    Altan Cakir
    (Istanbul Technical University) [intermediate]
    Big Data Analytics with Apache Spark


    Edward Chang
    (Stanford University) [intermediate]
    Artificial Intelligence for Disease Diagnosis and Precision Surgery


    Michael X. Cohen
    (Radboud University Nijmegen) [introductory]
    Dimension Explosion and Dimension Reduction in Brain Electrical Activity


    Ian Fisk
    (Flatiron Institute) [introductory]
    The Infrastructure to Support Data Science


    Michael Freeman
    (University of Washington) [intermediate]
    Interactive Data Visualization Using D3 + Observable


    David Gerbing
    (Portland State University) [introductory]
    Derive Meaning from Data with R Visualizations


    Yifan Hu
    (Yahoo Research) [introductory/advanced]
    Data Visualization and Machine Learning


    Rafael Irizarry
    (Harvard University) [introductory]
    Data Science for Statisticians (tidyverse, ggplot, wrangling)


    Wagner A. Kamakura
    (Rice University) [intermediate]
    Advanced Business Analytics using Excel Addins


    Ravi Kumar
    (Google) [intermediate/advanced]
    Clustering for Big Data


    Victor O.K. Li
    (University of Hong Kong) [intermediate]
    Deep Learning and Applications


    Panos Pardalos
    (University of Florida) [intermediate/advanced]
    Optimization and Data Sciences Techniques for Large Networks


    Valeriu Predoi
    (University of Reading) [introductory]
    A Beginner's Guide to Big Data Analysis: How to Connect Scientific Software Development with Real World Problems