Python and R are the leading open source languages for data science and machine learning, but getting comfortable with both of these languages requires grappling with different syntaxes, conventions, and terminology. Pairs of ostensibly comparable packages from PyPI and CRAN often have fundamentally different interfaces, and APIs connecting Python and R to the same external systems are often incongruous. Furthermore, when data scientists attempt to scale workflows from smaller local datasets to larger distributed datasets, they must contend with additional frameworks and interfaces with idiosyncrasies beyond those in the core Python and R ecosystems. But these differences belie a set of fundamental abstractions common to these systems.
Ian Cook illuminates the underlying commonalities of these systems through intuitive explanations and straightforward demonstrations. You’ll learn how:
By exploring and running Python and R code in Cloudera Data Science Workbench (CDSW), you’ll gain familiarity with these these two languages and their ecosystems of data science tools, plus SQL, Spark, and TensorFlow. By practicing on sets of equivalent data science and machine learning workflows implemented using these different languages and frameworks, you’ll overcome the obstacles to getting started using these tools.
Ian Cook is a data scientist at Cloudera and the author of several R packages, including implyr. Previously, he was a data scientist at TIBCO and a statistical software developer at AMD. Ian is a cofounder of Research Triangle Analysts, the largest data science meetup group in the Raleigh, North Carolina, area, where he lives with his wife and two young children. He holds an MS in statistics from Lehigh University and a BS in applied mathematics from Stony Brook University.
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