Data Science at the Command Line

Facing the Future with Time-Tested Tools

Jeroen Janssens (Autor)

Buch | Softcover

212 Seiten

2014
O'Reilly Media (Verlag)
978-1-4919-4785-2 (ISBN)

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This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.

To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.

Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.

Obtain data from websites, APIs, databases, and spreadsheets
Perform scrub operations on plain text, CSV, HTML/XML, and JSON
Explore data, compute descriptive statistics, and create visualizations
Manage your data science workflow using Drake
Create reusable tools from one-liners and existing Python or R code
Parallelize and distribute data-intensive pipelines using GNU Parallel
Model data with dimensionality reduction, clustering, regression, and classification algorithms

Jeroen Janssens is a senior data scientist at YPlan in New York City. His specialties are in machine learning, anomaly detection, and data visualization. Jeroen is passionate about building open source tools for doing data science. He obtained a B.Sc. in Life Sciences and an M.Sc. in Artificial Intelligence, both cum laude from Maastricht University, the Netherlands. Jeroen completed his Ph.D. in Machine Learning at the Tilburg center for Cognition and Communication, Tilburg University. Outside of work, you may find him biking the Brooklyn Bridge, beatboxing, or eating stroopwafels.

Chapter 1Introduction
Overview
Data Science Is OSEMN
Intermezzo Chapters
What Is the Command Line?
Why Data Science at the Command Line?
A Real-World Use Case
Further Reading
Chapter 2Getting Started
Overview
Setting Up Your Data Science Toolbox
Essential Concepts and Tools
Further Reading
Chapter 3Obtaining Data
Overview
Copying Local Files to the Data Science Toolbox
Decompressing Files
Converting Microsoft Excel Spreadsheets
Querying Relational Databases
Downloading from the Internet
Calling Web APIs
Further Reading
Chapter 4Creating Reusable Command-Line Tools
Overview
Converting One-Liners into Shell Scripts
Creating Command-Line Tools with Python and R
Further Reading
Chapter 5Scrubbing Data
Overview
Common Scrub Operations for Plain Text
Working with CSV
Working with HTML/XML and JSON
Common Scrub Operations for CSV
Further Reading
Chapter 6Managing Your Data Workflow
Overview
Introducing Drake
Installing Drake
Obtain Top Ebooks from Project Gutenberg
Every Workflow Starts with a Single Step
Well, That Depends
Rebuilding Specific Targets
Discussion
Further Reading
Chapter 7Exploring Data
Overview
Inspecting Data and Its Properties
Computing Descriptive Statistics
Creating Visualizations
Further Reading
Chapter 8Parallel Pipelines
Overview
Serial Processing
Parallel Processing
Distributed Processing
Discussion
Further Reading
Chapter 9Modeling Data
Overview
More Wine, Please!
Dimensionality Reduction with Tapkee
Clustering with Weka
Regression with SciKit-Learn Laboratory
Classification with BigML
Further Reading
Chapter 10Conclusion
Let’s Recap
Three Pieces of Advice
Where to Go from Here?
Getting in Touch
Appendix List of Command-Line Tools
alias
awk
aws
bash
bc
bigmler
body
cat
cd
chmod
cols
cowsay
cp
csvcut
csvgrep
csvjoin
csvlook
csvsort
csvsql
csvstack
csvstat
curl
curlicue
cut
display
drake
dseq
echo
env
export
feedgnuplot
fieldsplit
find
for
git
grep
head
header
in2csv
jq
json2csv
less
ls
man
mkdir
mv
parallel
paste
pbc
pip
pwd
python
R
Rio
Rio-scatter
rm
run_experiment
sample
scp
scrape
sed
seq
shuf
sort
split
sql2csv
ssh
sudo
tail
tapkee
tar
tee
tr
tree
type
uniq
unpack
unrar
unzip
wc
weka
which
xml2json
Appendix Bibliography

Erscheint lt. Verlag	7.10.2014
Verlagsort	Sebastopol
Sprache	englisch
Maße	182 x 233 mm
Gewicht	352 g
Einbandart	Paperback
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Mathematik / Informatik ► Mathematik ► Statistik
Schlagworte	Datenanalyse
ISBN-10	1-4919-4785-3 / 1491947853
ISBN-13	978-1-4919-4785-2 / 9781491947852
Zustand	Neuware