Review: Data Science at the Command Line

Admission: I didn’t really know how computers worked until around 2012.

For the majority of my career, I’ve worked for large companies with centralized IT functions. Like many statisticians, I fell into a comfortable position of learning SAS in a Windows environment, had Ops people to fix any Unix problems I’d run into and DBAs to load data into a relational database environment.

Then I became a consultant at a boutique digital analytics firm. To say I was punching above my weight was an understatement. All of the sudden it was time to go into various companies, have a one-hour kickoff meeting, then start untangling the spaghetti mess that represented their various technology systems. I also needed to figure out the boutique firm’s hacked together AWS and Rackspace infrastructure.

data-science-command-line

I’m starting off this review with this admission, because my story of learning to work from the command line parallels Data Science at the Command Line author Jeroen Janssens:

Around five years ago, during my PhD program, I gradually switched from using Microsoft Windows to GNU/Linux…Out of necessity I quickly became comfortable using the command line. Eventually, as spare time got more precious, I settled down with a GNU/Linux distribution known as Ubuntu…

  • Preface, pg. xi

Because a solid majority of people have never learned anything beyond point-and-click interface (Windows or Mac), the title of the book Data Science at the Command Line is somewhat unfortunate; this is a book for ANYONE looking to start manipulating files efficiently from the command line.

Getting Started, Safely

One of the best parts of Data Science at the Command Line is that it comes with a pre-built virtual machine with 80-100 or more command line tools installed. This is a very fast and safe way to get started with the command line, as the tools are pre-installed and no matter what command you run while you’re learning, you won’t destroy a computer you actually care about!

Chapters 2 and 3 move through the steps of installing the virtual machine, explaining the essential concepts of the command line, some basic commands showing simple (but powerful!) ways to chain command line tools together and how to obtain data. What I find so refreshing about these two chapters by Janssens is that the author assumes zero knowledge of the command line by the reader; these two chapters are the most accessible summary of how and why to use the command line I’ve ever read (Zed Shaw’s CLI tutorial is a close second, but is quite terse).

The OSEMN model

The middle portion of book covers the OSEMN model (Obtain-Scrub-Explore-Model-iNterpret) of data science; another way this book is refreshing is that rather than jump right into machine learning/predictive modeling, the author spends a considerable amount of time covering the gory details of real analysis projects: manipulating data from the format you receive (XML, JSON, sloppy CSV files, etc.) and taking the (numerous) steps required to get the format you want.

By introducing tools such as csvkit (csv manipulation), jq (JSON processor), and classic tools such as sed (stream editor) and (g)awk, the reader gets a full treatment of how to deal with malformed data files (which in my experience are the only type available in the wild!) . Chapter 6 (“Managing Your Data Workflow”) is also a great introduction into reproducible research using Drake (Make for Data Analysis). This is an area that I will personally be focusing my time on, as I tend to run a lot of one-off commands in HDFS and as of now, just copy them into a plain-text file. Reproducing = copy-paste in my case, which defeats the purpose of computers and scripting!

An Idea Can Be Stretched Too Far

Chapters 8 and 9 cover Parallel Processing using GNU Parallel and Modeling Data respectively. While GNU Parallel is a tool I could see using sometime in the future, I do feel like building models and creating visualizations straight from the command line is getting pretty close to just being a parlor trick. Yes, it’s obviously possible to do such things (and the author even wrote his own command line tool Rio for using R from the command line), but with the amount of iteration, feature building and fine-tuning that goes on, I’d rather use IPython Notebook or RStudio to give me the flexibility I need to really iterate effectively.

A Book For Everyone

As I mentioned above, I really feel that Data Science at the Command Line is a book well suited for anyone who does data analysis. Jeroen Janssens has done a fantastic job of taking his original “7 command-line tools for data science” blog post and extending the idea to a full-fledged book. This book has a prominent place in my work library next to Python for Data Analysis and in the past two months I’ve referred to each book at roughly the same rate. For under $30 for paperback at Amazon, there’s more than enough content to make you a better data scientist.

  • RSiteCatalyst Version 1.4.13 Release Notes
  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • Google Analytics Individual Qualification (IQ) - Passed!
  • Google Analytics SEO reports: Not Ready For Primetime?
  • An Afternoon With Edward Tufte
  • Google Analytics Custom Variables: A Page-Level Example
  • Xchange 2011: Think Tank and Harbor Cruise
  • Google Analytics for WordPress: Two Methods
  • WordPress Stats or Google Analytics? Yes!
  • Parallelizing Distance Calculations Using A GPU With CUDAnative.jl
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS