Colleen M. Farrelly is a senior data scientist whose academic and
industry research has focused on topological data analysis, quantum
machine learning, geometry-based machine learning, network science,
hierarchical modeling, and natural language processing. Since
graduating from the University of Miami with an MS in
biostatistics, Colleen has worked as a data scientist in a vari-
ety of industries, including healthcare, consumer packaged goods,
biotech, nuclear engineering, marketing, and education. Colleen
often speaks at tech conferences, including PyData, SAS Global,
WiDS, Data Science Africa, and DataScience SALON. When not working,
Colleen can be found writing haibun/haiga or swimming.
Yaé Ulrich Gaba completed his doctoral studies at the University
of Cape Town (UCT, South Africa) with a specialization in topology
and is currently a research associate at Quantum Leap Africa (QLA,
Rwanda). His research interests are computational geometry, applied
algebraic topology (topologi- cal data analysis), and geometric
machine learning (graph and point-cloud representation learning).
His current focus lies in geometric methods in data analysis, and
his work seeks to develop effective and theoretically justified
algorithms for data and shape analysis using geometric and
topological ideas and methods.
"The title says it all. Data is bound by many complex relationships
not easily shown in our two-dimensional, spreadsheet filled world.
The Shape of Data walks you through this richer view and
illustrates how to put it into practice."
—Stephanie Thompson, Data Scientist and Speaker
“The Shape of Data is a novel perspective and phenomenal
achievement in the application of geometry to the field of machine
learning. It is expansive in scope and contains loads of concrete
examples and coding tips for practical implementations, as well as
extremely lucid, concise writing to unpack the concepts. Even as a
more veteran data scientist who has been in the industry for years
now, having read this book I've come away with a deeper connection
to and new understanding of my field."
—Kurt Schuepfer, Ph.D., McDonalds Corporation
“A great source for the application of topology and geometry
in data science. Topology and geometry advance the field of machine
learning on unstructured data, and The Shape of Data does a great
job introducing new readers to the subject.”
—Uchenna “Ike” Chukwu, Senior Quantum Developer
"See how data looks not just as lists of numbers but as plots and
graphs. The Shape of Data shows the reader how to visualize data
sets and discover relations hidden in the numbers and sets. . . .
In this age of large data sets and deep learning, data graphics are
essential to scientists and engineers—just like this book."
—David S. Mazel, Principal/Manager Systems Engineer,
Regulus-Group
"Everyone who works at the border of geometry and Data Science will
find the book and invaluable resource and source of inspiration. It
is considerate that the R-codes used in the book have readily
accessible python codes. "
—Geoffrey Mboya, DPhil (Oxon), Director at Mfano Africa
"Comprehensive and exceptionally well written, The Shape of Data:
Geometry-Based Machine Learning and Data Analysis in R is
impressively 'reader friendly' in organization and presentation,
making it an ideal instructional resource for anyone with an
interest in topology, computer hacking, or mathematical/statistical
computer software."
—Midwest Book Review
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