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Top 7 Business Strategy Models

UPDATED POST- Some Models I use for Business Strategy- to analyze the huge reams of qualitative and uncertain data that business generates. I have added a bonus the Business canvas

  1. Porters 5 forces Model-To analyze industries
  2. Business Canvas
  3. BCG Matrix- To analyze Product Portfolios
  4. Porters Diamond Model- To analyze locations
  5. McKinsey 7 S Model-To analyze teams
  6. Gernier Theory- To analyze growth of organization
  7. Herzberg Hygiene Theory- To analyze soft aspects of individuals
  8. Marketing Mix Model- To analyze marketing mix.

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Data Frame in Python

Exploring some Python Packages and R packages to move /work with both Python and R without melting your brain or exceeding your project deadline

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If you liked the data.frame structure in R, you have some way to work with them at a faster processing speed in Python.

Here are three packages that enable you to do so-

(1) pydataframe http://code.google.com/p/pydataframe/

An implemention of an almost R like DataFrame object. (install via Pypi/Pip: “pip install pydataframe”)

Usage:

        u = DataFrame( { "Field1": [1, 2, 3],
                        "Field2": ['abc', 'def', 'hgi']},
                        optional:
                         ['Field1', 'Field2']
                         ["rowOne", "rowTwo", "thirdRow"])

A DataFrame is basically a table with rows and columns.

Columns are named, rows are numbered (but can be named) and can be easily selected and calculated upon. Internally, columns are stored as 1d numpy arrays. If you set row names, they’re converted into a dictionary for fast access. There is a rich subselection/slicing API, see help(DataFrame.get_item) (it also works for setting values). Please note that any slice get’s you another DataFrame, to access individual entries use get_row(), get_column(), get_value().

DataFrames also understand basic arithmetic and you can either add (multiply,…) a constant value, or another DataFrame of the same size / with the same column names, like this:

#multiply every value in ColumnA that is smaller than 5 by 6.
my_df[my_df[:,'ColumnA'] < 5, 'ColumnA'] *= 6

#you always need to specify both row and column selectors, use : to mean everything
my_df[:, 'ColumnB'] = my_df[:,'ColumnA'] + my_df[:, 'ColumnC']

#let's take every row that starts with Shu in ColumnA and replace it with a new list (comprehension)
select = my_df.where(lambda row: row['ColumnA'].startswith('Shu'))
my_df[select, 'ColumnA'] = [row['ColumnA'].replace('Shu', 'Sha') for row in my_df[select,:].iter_rows()]

Dataframes talk directly to R via rpy2 (rpy2 is not a prerequiste for the library!)

 

(2) pandas http://pandas.pydata.org/

Library Highlights

  • A fast and efficient DataFrame object for data manipulation with integrated indexing;
  • Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
  • Flexible reshaping and pivoting of data sets;
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
  • Columns can be inserted and deleted from data structures for size mutability;
  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
  • High performance merging and joining of data sets;
  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
  • Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
  • The library has been ruthlessly optimized for performance, with critical code paths compiled to C;
  • Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Why not R?

First of all, we love open source R! It is the most widely-used open source environment for statistical modeling and graphics, and it provided some early inspiration for pandas features. R users will be pleased to find this library adopts some of the best concepts of R, like the foundational DataFrame (one user familiar with R has described pandas as “R data.frame on steroids”). But pandas also seeks to solve some frustrations common to R users:

  • R has barebones data alignment and indexing functionality, leaving much work to the user. pandas makes it easy and intuitive to work with messy, irregularly indexed data, like time series data. pandas also provides rich tools, like hierarchical indexing, not found in R;
  • R is not well-suited to general purpose programming and system development. pandas enables you to do large-scale data processing seamlessly when developing your production applications;
  • Hybrid systems connecting R to a low-productivity systems language like Java, C++, or C# suffer from significantly reduced agility and maintainability, and you’re still stuck developing the system components in a low-productivity language;
  • The “copyleft” GPL license of R can create concerns for commercial software vendors who want to distribute R with their software under another license. Python and pandas use more permissive licenses.

(3) datamatrix http://pypi.python.org/pypi/datamatrix/0.8

datamatrix 0.8

A Pythonic implementation of R’s data.frame structure.

Latest Version: 0.9

This module allows access to comma- or other delimiter separated files as if they were tables, using a dictionary-like syntax. DataMatrix objects can be manipulated, rows and columns added and removed, or even transposed

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Modeling in Python

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Analytics 2012 Conference

from http://www.sas.com/events/analytics/us/index.html

Analytics 2012 Conference

SAS and more than 1,000 analytics experts gather at

Caesars Palace
Caesars Palace

Analytics 2012 Conference Details

Pre-Conference Workshops – Oct 7
Conference – Oct 8-9
Post-Conference Training – Oct 10-12
Caesars Palace, Las Vegas

Keynote Speakers

The following are confirmed keynote speakers for Analytics 2012. Jim Goodnight Since he co-founded SAS in 1976, Jim Goodnight has served as the company’s Chief Executive Officer.

William Hakes Dr. William Hakes is the CEO and co-founder of Link Analytics, an analytical technology company focused on mobile, energy and government verticals.

Tim Rey Tim Rey  has written over 100 internal papers, published 21 external papers, and delivered numerous keynote presentations and technical talks at various quantitative methods forums. Recently he has co-chaired both forecasting and data mining conferences. He is currently in the process of co-writing a book, Applied Data Mining for Forecasting.

http://www.sas.com/events/analytics/us/train.html

Pre-Conference

Plan to come to Analytics 2012 a day early and participate in one of the pre-conference workshops or take a SAS Certification exam. Prices for all of the preconference workshops, except for SAS Sentiment Analysis Studio: Introduction to Building Models and the Business Analytics Consulting Workshops, are included in the conference package pricing. You will be prompted to select your pre-conference training options when you register.

Sunday Morning Workshop

SAS Sentiment Analysis Studio: Introduction to Building Models

This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
View outline
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250

Sunday Afternoon Workshops

Business Analytics Consulting Workshops

This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
View outline
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200

Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand

This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
View outline
Sunday, Oct. 7; 1-5 p.m.

Forecast Value Added Analysis

Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
View outline
Sunday, Oct. 7; 1-5 p.m.

SAS Enterprise Content Categorization: An Introduction

This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
View outline
Sunday, Oct. 7; 1-5 p.m.

Introduction to Data Mining and SAS Enterprise Miner

This course serves as an introduction to data mining and SAS Enterprise Miner for Desktop software. It is designed for data analysts and qualitative experts as well as those with less of a technical background who want a general understanding of data mining.
View outline
Sunday, Oct. 7, 1-5 p.m.

Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM

This half-day lecture teaches students how to model, interpret, and predict time series data using UCMs. The UCM procedure analyzes and forecasts equally spaced univariate time series data using the unobserved components models (UCM). This course is designed for business analysts who want to analyze time series data to uncover patterns such as trend, seasonal effects, and cycles using the latest techniques.
View outline
Sunday, Oct. 7, 1-5 p.m.

SAS Rapid Predictive Modeler

This seminar will provide a brief introduction to the use of SAS Enterprise Guide for graphical and data analysis. However, the focus will be on using SAS Enterprise Guide and SAS Enterprise Miner along with the Rapid Predictive Modeling component to build predictive models. Predictive modeling will be introduced using the SEMMA process developed with the introduction of SAS Enterprise Miner. Several examples will be used to illustrate the use of the Rapid Predictive Modeling component, and interpretations of the model results will be provided.
View outline
Sunday, Oct. 7, 1-5 p.m

Using Rapid Miner and R for Sports Analytics #rstats

Rapid Miner has been one of the oldest open source analytics software, long long before open source or even analytics was considered a fashion buzzword. The Rapid Miner software has been a pioneer in many areas (like establishing a marketplace for Rapid Miner Extensions) and the Rapid Miner -R extension was one of the most promising enablers of using R in an enterprise setting.
The following interview was taken with a manager of analytics for a sports organization. The sports organization considers analytics as a strategic differentiator , hence the name is confidential. No part of the interview has been edited or manipulated.

Ajay- Why did you choose Rapid Miner and R? What were the other software alternatives you considered and discarded?

Analyst- We considered most of the other major players in statistics/data mining or enterprise BI.  However, we found that the value proposition for an open source solution was too compelling to justify the premium pricing that the commercial solutions would have required.  The widespread adoption of R and the variety of packages and algorithms available for it, made it an easy choice.  We liked RapidMiner as a way to design structured, repeatable processes, and the ability to optimize learner parameters in a systematic way.  It also handled large data sets better than R on 32-bit Windows did.  The GUI, particularly when 5.0 was released, made it more usable than R for analysts who weren’t experienced programmers.

Ajay- What analytics do you do think Rapid Miner and R are best suited for?

 Analyst- We use RM+R mainly for sports analysis so far, rather than for more traditional business applications.  It has been quite suitable for that, and I can easily see how it would be used for other types of applications.

 Ajay- Any experiences as an enterprise customer? How was the installation process? How good is the enterprise level support?

Analyst- Rapid-I has been one of the most responsive tech companies I’ve dealt with, either in my current role or with previous employers.  They are small enough to be able to respond quickly to requests, and in more than one case, have fixed a problem, or added a small feature we needed within a matter of days.  In other cases, we have contracted with them to add larger pieces of specific functionality we needed at reasonable consulting rates.  Those features are added to the mainline product, and become fully supported through regular channels.  The longer consulting projects have typically had a turnaround of just a few weeks.

 Ajay- What challenges if any did you face in executing a pure open source analytics bundle ?

Analyst- As Rapid-I is a smaller company based in Europe, the availability of training and consulting in the USA isn’t as extensive as for the major enterprise software players, and the time zone differences sometimes slow down the communications cycle.  There were times where we were the first customer to attempt a specific integration point in our technical environment, and with no prior experiences to fall back on, we had to work with Rapid-I to figure out how to do it.  Compared to the what traditional software vendors provide, both R and RM tend to have sparse, terse, occasionally incomplete documentation.  The situation is getting better, but still lags behind what the traditional enterprise software vendors provide.

 Ajay- What are the things you can do in R ,and what are the things you prefer to do in Rapid Miner (comparison for technical synergies)

Analyst- Our experience has been that RM is superior to R at writing and maintaining structured processes, better at handling larger amounts of data, and more flexible at fine-tuning model parameters automatically.  The biggest limitation we’ve had with RM compared to R is that R has a larger library of user-contributed packages for additional data mining algorithms.  Sometimes we opted to use R because RM hadn’t yet implemented a specific algorithm.  The introduction the R extension has allowed us to combine the strengths of both tools in a very logical and productive way.

In particular, extending RapidMiner with R helped address RM’s weakness in the breadth of algorithms, because it brings the entire R ecosystem into RM (similar to how Rapid-I implemented much of the Weka library early on in RM’s development).  Further, because the R user community releases packages that implement new techniques faster than the enterprise vendors can, this helps turn a potential weakness into a potential strength.  However, R packages tend to be of varying quality, and are more prone to go stale due to lack of support/bug fixes.  This depends heavily on the package’s maintainer and its prevalence of use in the R community.  So when RapidMiner has a learner with a native implementation, it’s usually better to use it than the R equivalent.

Interview Rob J Hyndman Forecasting Expert #rstats

Here is an interview with Prof Rob J Hyndman who has created many time series forecasting methods and authored books as well as R packages on the same.

Ajay -Describe your journey from being a student of science to a Professor. What were some key turning points along that journey?
 
Rob- I started a science honours degree at the University of Melbourne in 1985. By the end of 1985 I found myself simultaneously working as a statistical consultant (having completed all of one year of statistics courses!). For the next three years I studied mathematics, statistics and computer science at university, and tried to learn whatever I needed to in order to help my growing group of clients. Often we would cover things in classes that I’d already taught myself through my consulting work. That really set the trend for the rest of my career. I’ve always been an academic on the one hand, and a statistical consultant on the other. The consulting work has led me to learn a lot of things that I would not otherwise have come across, and has also encouraged me to focus on research problems that are of direct relevance to the clients I work with.
I never set out to be an academic. In fact, I thought that I would get a job in the business world as soon as I finished my degree. But once I completed the degree, I was offered a position as a statistical consultant within the University of Melbourne, helping researchers in various disciplines and doing some commercial work. After a year, I was getting bored doing only consulting, and I thought it would be interesting to do a PhD. I was lucky enough to be offered a generous scholarship which meant I was paid more to study than to continue working.
Again, I thought that I would probably go and get a job in the business world after I finished my PhD. But I finished it early and my scholarship was going to be cut off once I submitted my thesis. So instead, I offered to teach classes for free at the university and delayed submitting my thesis until the scholarship period ran out. That turned out to be a smart move because the university saw that I was a good teacher, and offered me a lecturing position starting immediately I submitted my thesis. So I sort of fell into an academic career.
I’ve kept up the consulting work part-time because it is interesting, and it gives me a little extra money. But I’ve also stayed an academic because I love the freedom to be able to work on anything that takes my fancy.
Ajay- Describe your upcoming book on Forecasting.
 
Rob- My first textbook on forecasting (with Makridakis and Wheelwright) was written a few years after I finished my PhD. It has been very popular, but it costs a lot of money (about $140 on Amazon). I estimate that I get about $1 for every book sold. The rest goes to the publisher (Wiley) and all they do is print, market and distribute it. I even typeset the whole thing myself and they print directly from the files I provided. It is now about 15 years since the book was written and it badly needs updating. I had a choice of writing a new edition with Wiley or doing something completely new. I decided to do a new one, largely because I didn’t want a publisher to make a lot of money out of students using my hard work.
It seems to me that students try to avoid buying textbooks and will search around looking for suitable online material instead. Often the online material is of very low quality and contains many errors.
As I wasn’t making much money on my textbook, and the facilities now exist to make online publishing very easy, I decided to try a publishing experiment. So my new textbook will be online and completely free. So far it is about 2/3 completed and is available at http://otexts.com/fpp/. I am hoping that my co-author (George Athanasopoulos) and I will finish it off before the end of 2012.
The book is intended to provide a comprehensive introduction to forecasting methods. We don’t attempt to discuss the theory much, but provide enough information for people to use the methods in practice. It is tied to the forecast package in R, and we provide code to show how to use the various forecasting methods.
The idea of online textbooks makes a lot of sense. They are continuously updated so if we find a mistake we fix it immediately. Also, we can add new sections, or update parts of the book, as required rather than waiting for a new edition to come out. We can also add richer content including video, dynamic graphics, etc.
For readers that want a print edition, we will be aiming to produce a print version of the book every year (available via Amazon).
I like the idea so much I’m trying to set up a new publishing platform (otexts.com) to enable other authors to do the same sort of thing. It is taking longer than I would like to make that happen, but probably next year we should have something ready for other authors to use.
Ajay- How can we make textbooks cheaper for students as well as compensate authors fairly
 
Rob- Well free is definitely cheaper, and there are a few businesses trying to make free online textbooks a reality. Apart from my own efforts, http://www.flatworldknowledge.com/ is producing a lot of free textbooks. And textbookrevolution.org is another great resource.
With otexts.com, we will compensate authors in two ways. First, the print versions of a book will be sold (although at a vastly cheaper rate than other commercial publishers). The royalties on print sales will be split 50/50 with the authors. Second, we plan to have some features of each book available for subscription only (e.g., solutions to exercises, some multimedia content, etc.). Again, the subscription fees will be split 50/50 with the authors.
Ajay- Suppose a person who used to use forecasting software from another company decides to switch to R. How easy and lucid do you think the current documentation on R website for business analytics practitioners such as these – in the corporate world.
 
Rob- The documentation on the R website is not very good for newcomers, but there are a lot of other R resources now available. One of the best introductions is Matloff’s “The Art of R Programming”. Provided someone has done some programming before (e.g., VBA, python or java), learning R is a breeze. The people who have trouble are those who have only ever used menu interfaces such as Excel. Then they are not only learning R, but learning to think about computing in a different way from what they are used to, and that can be tricky. However, it is well worth it. Once you know how to code, you can do so much more.  I wish some basic programming was part of every business and statistics degree.
If you are working in a particular area, then it is often best to find a book that uses R in that discipline. For example, if you want to do forecasting, you can use my book (otexts.com/fpp/). Or if you are using R for data visualization, get hold of Hadley Wickham’s ggplot2 book.
Ajay- In a long and storied career- What is the best forecast you ever made ? and the worst?
 
 Rob- Actually, my best work is not so much in making forecasts as in developing new forecasting methodology. I’m very proud of my forecasting models for electricity demand which are now used for all long-term planning of electricity capacity in Australia (see  http://robjhyndman.com/papers/peak-electricity-demand/  for the details). Also, my methods for population forecasting (http://robjhyndman.com/papers/stochastic-population-forecasts/ ) are pretty good (in my opinion!). These methods are now used by some national governments (but not Australia!) for their official population forecasts.
Of course, I’ve made some bad forecasts, but usually when I’ve tried to do more than is reasonable given the available data. One of my earliest consulting jobs involved forecasting the sales for a large car manufacturer. They wanted forecasts for the next fifteen years using less than ten years of historical data. I should have refused as it is unreasonable to forecast that far ahead using so little data. But I was young and naive and wanted the work. So I did the forecasts, and they were clearly outside the company’s (reasonable) expectations, and they then refused to pay me. Lesson learned. It’s better to refuse work than do it poorly.

Probably the biggest impact I’ve had is in helping the Australian government forecast the national health budget. In 2001 and 2002, they had underestimated health expenditure by nearly $1 billion in each year which is a lot of money to have to find, even for a national government. I was invited to assist them in developing a new forecasting method, which I did. The new method has forecast errors of the order of plus or minus $50 million which is much more manageable. The method I developed for them was the basis of the ETS models discussed in my 2008 book on exponential smoothing (www.exponentialsmoothing.net)

. And now anyone can use the method with the ets() function in the forecast package for R.
About-
Rob J Hyndman is Pro­fessor of Stat­ist­ics in the Depart­ment of Eco­no­met­rics and Busi­ness Stat­ist­ics at Mon­ash Uni­ver­sity and Dir­ector of the Mon­ash Uni­ver­sity Busi­ness & Eco­nomic Fore­cast­ing Unit. He is also Editor-in-Chief of the Inter­na­tional Journal of Fore­cast­ing and a Dir­ector of the Inter­na­tional Insti­tute of Fore­casters. Rob is the author of over 100 research papers in stat­ist­ical sci­ence. In 2007, he received the Moran medal from the Aus­tralian Academy of Sci­ence for his con­tri­bu­tions to stat­ist­ical research, espe­cially in the area of stat­ist­ical fore­cast­ing. For 25 years, Rob has main­tained an act­ive con­sult­ing prac­tice, assist­ing hun­dreds of com­pan­ies and organ­iz­a­tions. His recent con­sult­ing work has involved fore­cast­ing elec­tri­city demand, tour­ism demand, the Aus­tralian gov­ern­ment health budget and case volume at a US call centre.

Interview John Myles White , Machine Learning for Hackers

Here is an interview with one of the younger researchers  and rock stars of the R Project, John Myles White,  co-author of Machine Learning for Hackers.

Ajay- What inspired you guys to write Machine Learning for Hackers. What has been the public response to the book. Are you planning to write a second edition or a next book?

John-We decided to write Machine Learning for Hackers because there were so many people interested in learning more about Machine Learning who found the standard textbooks a little difficult to understand, either because they lacked the mathematical background expected of readers or because it wasn’t clear how to translate the mathematical definitions in those books into usable programs. Most Machine Learning books are written for audiences who will not only be using Machine Learning techniques in their applied work, but also actively inventing new Machine Learning algorithms. The amount of information needed to do both can be daunting, because, as one friend pointed out, it’s similar to insisting that everyone learn how to build a compiler before they can start to program. For most people, it’s better to let them try out programming and get a taste for it before you teach them about the nuts and bolts of compiler design. If they like programming, they can delve into the details later.

We once said that Machine Learning for Hackers  is supposed to be a chemistry set for Machine Learning and I still think that’s the right description: it’s meant to get readers excited about Machine Learning and hopefully expose them to enough ideas and tools that they can start to explore on their own more effectively. It’s like a warmup for standard academic books like Bishop’s.
The public response to the book has been phenomenal. It’s been amazing to see how many people have bought the book and how many people have told us they found it helpful. Even friends with substantial expertise in statistics have said they’ve found a few nuggets of new information in the book, especially regarding text analysis and social network analysis — topics that Drew and I spend a lot of time thinking about, but are not thoroughly covered in standard statistics and Machine Learning  undergraduate curricula.
I hope we write a second edition. It was our first book and we learned a ton about how to write at length from the experience. I’m about to announce later this week that I’m writing a second book, which will be a very short eBook for O’Reilly. Stay tuned for details.

Ajay-  What are the key things that a potential reader can learn from this book?

John- We cover most of the nuts and bolts of introductory statistics in our book: summary statistics, regression and classification using linear and logistic regression, PCA and k-Nearest Neighbors. We also cover topics that are less well known, but are as important: density plots vs. histograms, regularization, cross-validation, MDS, social network analysis and SVM’s. I hope a reader walks away from the book having a feel for what different basic algorithms do and why they work for some problems and not others. I also hope we do just a little to shift a future generation of modeling culture towards regularization and cross-validation.

Ajay- Describe your journey as a science student up till your Phd. What are you current research interests and what initiatives have you done with them?

John-As an undergraduate I studied math and neuroscience. I then took some time off and came back to do a Ph.D. in psychology, focusing on mathematical modeling of both the brain and behavior. There’s a rich tradition of machine learning and statistics in psychology, so I got increasingly interested in ML methods during my years as a grad student. I’m about to finish my Ph.D. this year. My research interests all fall under one heading: decision theory. I want to understand both how people make decisions (which is what psychology teaches us) and how they should make decisions (which is what statistics and ML teach us). My thesis is focused on how people make decisions when there are both short-term and long-term consequences to be considered. For non-psychologists, the classic example is probably the explore-exploit dilemma. I’ve been working to import more of the main ideas from stats and ML into psychology for modeling how real people handle that trade-off. For psychologists, the classic example is the Marshmallow experiment. Most of my research work has focused on the latter: what makes us patient and how can we measure patience?

Ajay- How can academia and private sector solve the shortage of trained data scientists (assuming there is one)?

John- There’s definitely a shortage of trained data scientists: most companies are finding it difficult to hire someone with the real chops needed to do useful work with Big Data. The skill set required to be useful at a company like Facebook or Twitter is much more advanced than many people realize, so I think it will be some time until there are undergraduates coming out with the right stuff. But there’s huge demand, so I’m sure the market will clear sooner or later.

The changes that are required in academia to prepare students for this kind of work are pretty numerous, but the most obvious required change is that quantitative people need to be learning how to program properly, which is rare in academia, even in many CS departments. Writing one-off programs that no one will ever have to reuse and that only work on toy data sets doesn’t prepare you for working with huge amounts of messy data that exhibit shifting patterns. If you need to learn how to program seriously before you can do useful work, you’re not very valuable to companies who need employees that can hit the ground running. The companies that have done best in building up data teams, like LinkedIn, have learned to train people as they come in since the proper training isn’t typically available outside those companies.
Of course, on the flipside, the people who do know how to program well need to start learning more about theory and need to start to have a better grasp of basic mathematical models like linear and logistic regressions. Lots of CS students seem not to enjoy their theory classes, but theory really does prepare you for thinking about what you can learn from data. You may not use automata theory if you work at Foursquare, but you will need to be able to reason carefully and analytically. Doing math is just like lifting weights: if you’re not good at it right now, you just need to dig in and get yourself in shape.
About-
John Myles White is a Phd Student in  Ph.D. student in the Princeton Psychology Department, where he studies human decision-making both theoretically and experimentally. Along with the political scientist Drew Conway, he is  the author of a book published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. He is also working with Mark Hansenon a book for laypeople about exploratory data analysis.John is the lead maintainer for several R packages, including ProjectTemplate and log4r.

(TIL he has played in several rock bands!)

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You can read more in his own words at his blog at http://www.johnmyleswhite.com/about/
He can be contacted via social media at Google Plus at https://plus.google.com/109658960610931658914 or twitter at twitter.com/johnmyleswhite/

Who made Who in #Rstats

While Bob M, my old mentor and fellow TN man maintains the website http://r4stats.com/ how popular R is across various forums, I am interested in who within R community of 3 million (give or take a few) is contributing more. I am very sure by 2014, we can have a new fork of R called Hadley R, in which all packages would be made by Hadley Wickham and you wont need anything else.

But jokes apart, since I didnt have the time to

1) scrape CRAN for all package authors

2) scrape for lines of code across all packages

3) allocate lines of code (itself a dubious software productivity metric) to various authors of R packages-

OR

1) scraping the entire and 2011′s R help list

2) determine who is the most frequent r question and answer user (ala SAS-L’s annual MVP and rookie of the year awards)

I did the following to atleast who is talking about R across easily scrapable Q and A websites

Stack Overflow still rules over all.

http://stackoverflow.com/tags/r/topusers shows the statistics on who made whom in R on Stack Overflow

All in all, initial ardour seems to have slowed for #Rstats on Stack Overflow ? or is it just summer?

No the answer- credit to Rob J Hyndman is most(?) activity is shifting to Stats Exchange

http://stats.stackexchange.com/tags/r/topusers


You could also paste this in Notepad and some graphs on Average Score / Answer or even make a social network graph if you had the time.

Do NOT (Go/Bi) search for Stack Overflow API or web scraping stack overflow- it gives you all the answers on the website but 0 answers on how to scrape these websites.

I have added a new website called Meta Optimize to this list based on Tal G’s interview of Joseph Turian,  at http://www.r-statistics.com/2010/07/statistical-analysis-qa-website-did-stackoverflow-just-lose-it-to-metaoptimize-and-is-it-good-or-bad/

http://metaoptimize.com/qa/tags/r/?sort=hottest

There are only 17 questions tagged R but it seems a lot of views is being generated.

I also decided to add views from Quora since it is Q and A site (and one which I really like)

http://www.quora.com/R-software

Again very few questions but lot many followers

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