Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , Leave a comment on Preparing Data for Supervised Classification

Preparing Data for Supervised Classification

Nina Zumel has been polishing up new vtreat for Python documentation and tutorials. They are coming out so good that I find to be fair to the R community I must start to back-port this new documentation to vtreat for R.

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Posted on Categories Practical Data Science, Statistics, TutorialsTags , , , , , Leave a comment on The Advantages of Record Transform Specifications

The Advantages of Record Transform Specifications

Nina Zumel had a really great article on how to prepare a nice Keras performance plot using R.


Keras plot

I will use this example to show some of the advantages of cdata record transform specifications.

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Posted on Categories Administrativia, Opinion, Practical Data Science, StatisticsTags , , 2 Comments on Practical Data Science with R update

Practical Data Science with R update

Just got the following note from a new reader:

Thank you for writing Practical Data Science with R. It’s challenging for me, but I am learning a lot by following your steps and entering the commands.

Wow, this is exactly what Nina Zumel and I hoped for. We wish we could make everything easy, but an appropriate amount of challenge is required for significant learning and accomplishment.

Of course we try to avoid inessential problems. All of the code examples from the book can be found here (and all the data sets here).

The second edition is coming out very soon. Please check it out.

Posted on Categories Administrativia, data science, Opinion, Pragmatic Data Science, Pragmatic Machine Learning, TutorialsTags , , , , , , , , Leave a comment on Introducing data_algebra

Introducing data_algebra

This article introduces the data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases.

In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and rqdatatable).

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Posted on Categories Pragmatic Data Science, Pragmatic Machine Learning, TutorialsTags , , , , , Leave a comment on What is vtreat?

What is vtreat?

vtreat is a DataFrame processor/conditioner that prepares real-world data for supervised machine learning or predictive modeling in a statistically sound manner.

vtreat takes an input DataFrame that has a specified column called “the outcome variable” (or “y”) that is the quantity to be predicted (and must not have missing values). Other input columns are possible explanatory variables (typically numeric or categorical/string-valued, these columns may have missing values) that the user later wants to use to predict “y”. In practice such an input DataFrame may not be immediately suitable for machine learning procedures that often expect only numeric explanatory variables, and may not tolerate missing values.

To solve this, vtreat builds a transformed DataFrame where all explanatory variable columns have been transformed into a number of numeric explanatory variable columns, without missing values. The vtreat implementation produces derived numeric columns that capture most of the information relating the explanatory columns to the specified “y” or dependent/outcome column through a number of numeric transforms (indicator variables, impact codes, prevalence codes, and more). This transformed DataFrame is suitable for a wide range of supervised learning methods from linear regression, through gradient boosted machines.

The idea is: you can take a DataFrame of messy real world data and easily, faithfully, reliably, and repeatably prepare it for machine learning using documented methods using vtreat. Incorporating vtreat into your machine learning workflow lets you quickly work with very diverse structured data.

Worked examples can be found here.

For more detail please see here: arXiv:1611.09477 stat.AP (the documentation describes the R version, however all of the examples can be found worked in Python here).

vtreat is available as a Python/Pandas package, and also as an R package.

(logo: Julie Mount, source: “The Harvest” by Boris Kustodiev 1914)

Some operational examples can be found here.

Posted on Categories Administrativia, Pragmatic Data ScienceTags , , , , Leave a comment on Speaking at BARUG

Speaking at BARUG

We will be speaking at the Tuesday, September 3, 2019 BARUG. If you are in the Bay Area, please come see us.

Nina Zumel & John Mount
Practical Data Science with R

Practical Data Science with R (Zumel and Mount) was one of the first, and most widely-read books on the practice of doing Data Science using R. We have been working hard on an improved and revised 2nd edition of our book (coming out this Fall). The book reflects more experience with data science, teaching, and with R itself. We will talk about what direction we think the R community has been taking, how this affected the book, and what is new in the upcoming edition.

Posted on Categories OpinionTags , Leave a comment on Florence Nightingale, Data Scientist

Florence Nightingale, Data Scientist

Florence Nightingale, Data Scientist.

In 1858 Florence Nightingale published her now famous “rose diagram” breaking down causes of mortality.

Nightingale mortality

By w:Florence Nightingale (1820–1910). – http://www.royal.gov.uk/output/Page3943.asp [dead link], Public Domain, Link

For more please here.

Posted on Categories OpinionTags , , , , Leave a comment on PyCharm Video Review

PyCharm Video Review

My basic video review of the PyCharm integrated development environment for Python with Anaconda and Jupyter/iPython integration. I like the IDE extensions enough to pay for them early in my evaluation. Highly recommended for data science projects, at least try one of the open-source or the trial versions.

Posted on Categories OpinionTags , , 4 Comments on A Comment on Data Science Integrated Development Environments

A Comment on Data Science Integrated Development Environments

A point that differs from our experience struck us in the recent note regarding doing data science in Python:

A development environment [for Python] specifically tailored to the data science sector on the level of RStudio, for example, does not (yet) exist.

“What’s the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA” Amit Ghosh

Actually, Python has a large number of very capable integrated development environments, some of which are specifically tailored for data science. Please read on for a small list of tools, and my recommendations for a specific data science in Python toolchain.

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Posted on Categories Opinion, StatisticsTags , ,

Technical books are amazing opportunities

Nina and I have been sending out drafts of our book Practical Data Science with R 2nd Edition for technical review. A few of the reviews came back from reviewers that described themselves with variations of:

Senior Business Analyst for COMPANYNAME. I have been involved in presenting graphs of data for many years.

To us this reads as somebody with deep experience, confidence, and bit of humility. They do something technical and valuable, but because they understand it they do not consider it to be arcane magic.

In this note we describe might can happen if such a person (or if a junior version of such a person) acquires 1 or 2 technical books.

Continue reading Technical books are amazing opportunities