with Tags:
big_data
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## What Is Machine Learning?
Can you explain me what machine learning is? I often get this question from colleagues and customers, and answering it is tricky. What is tricky is to give the intuition behind what machine learning is really useful for. I'll review common answers and give you my preferred one. Cognitive Computing The first category of answer to the question is what IBM calls cognitive computing . It is about building machines (computers, software, robots, web sites, mobile apps, devices, etc) that do not need to be programmed... [More]
Tags: big_data analytics machine_learning |

## Installing PyCUDA On Anaconda For Windows
PyCUDA is a great library if you want to use gpu computing with NVIDIA chips. If you want a more portable approach or if you have ATI chips instead of NVIDIA, then you might consider PyOpenCl instead of PyCUDA. I provided instructions on how to install PyOpenCl on Anaconda for Windows in a previous entry . Installing PyCUDA on Anaconda for Windows can be tricky. Here is what you can do, it worked fine for me. I am using the latest Anaconda distribution with Python 3.5 in it.... [More]
Tags: python big_data pycuda machine_learning anaconda analytics |

## Top Posts For 2015
I wish all my readers, their families, and their friends, all the best for 2016. May your dreams come true. I also want to warmly thank you, my readers, for your continued interest. This led me write more entries than ever, with 54 entries in 2015. I still blogged on optimization and how it fits within the analytics and data science landscape, but I added two more streams in 2015: Emerging technologies for cloud computing, like Docker. Python as a language of choice for data science and technical computing. These streams... [More]
Tags: docker anaytics data_science optimization python cloud big_data machine_learning |

## Analytics Landscape
A great way to explain the value of analytics is to speak about the analytics maturity model . This model contains two pieces. First, analytics is a two step process: insights are generated from data, then decisions are made based on these insights. Second, we distinguish four maturity levels, depending on how much of the analytics process is automated: descriptive, diagnostic, predictive, and prescriptive. Descriptive Analytics answers: What happened? What is happening now? It makes data visible to human decision... [More]
Tags: big_data data_science analytics |

## Actionable Insights
It is good practice to eat your own food. I should be no exception. In my post on the role of data science I was blaming data scientists who left business users without any clue about how to use the insights they produce. I should do the same, and help businesses use the advice I gave in that post: Data science role is to enable data based decision making. What does it mean in practice for a business? It means that data scientists should not only provide interesting insights, but they also should care... [More]
Tags: big_data analytics decision data_science optimization |

## The Role Of Data Science
I am sure I'll get flamed for this post, given how hyped data science is. Let me first say that I do not pretend to define what data science is, others, probably more qualified than me, have done it well. For instance, I like this definition from Dawen Peng, as it speaks to an Operations Research person like me. I will rather focus on the role data science can have for business. What I see the most is data scientists analyzing data then publishing reports on insights they found in data. Just browse over... [More]
Tags: analytics big_data data_science |

## Optimization Is Ready For Big Data: Part 4, Veracity
Big Data promise is to enable better decisions based on data. The idea seems appealing yet there is a caveat: is the data reliable enough to base decisions on it? Question is to what extent can we trust data? My experience shows that cleaning data can take up to 80% of an analytics project. This is well known, and is often called the veracity dimension of Big Data . Point is that most data in the Big Data era is uncertain, see for instance the figure below, taken from a post by John Poppelaars... [More]
Tags: optimization analytics uncertainty big_data |

## Optimization Is Ready For Big Data: Part 3, Variety
A colleague of mine once told me that Big Data should be called "All Data". Indeed, one of the key dimension of Big Data is to apply analytics techniques to all kind of data. Other dimensions include volume and velocity of data. Can optimization be applied to all sorts of data? I'd say yes despite the fact that optimization primarily deals with numerical data. Indeed, optimization has already been applied to a wide variety of data, much more than common knowledge may suggest. Let's see a few... [More]
Tags: big_data optimization analytics |

## Optimization Is Ready For Big Data: Part 2, Velocity
Proponents of Big Data boast about how it might help get personalized behavior from all the things and systems people interact with (web sites, mobile apps, customer support services, internet of things, etc) . These systems have to deal with data in motion such as web interaction, sensor feeds (eg body temperature), video, social media feeds, etc. Dealing with such data is the velocity dimension of Big Data . I have discussed how optimization could be applied to another big data dimension, namely large volume of data, in my... [More]
Tags: big_data prescriptive optimization analytics |

## Optimization Is Ready For Big Data: Part 1, Volume
I had the honor to give a tutorial at a Big Data and Optimization seminar, thanks to an invitation from John Poppelaars , One of the topics I discussed seemed to resonate well. Let me try to explain it here. The first thing people think of when they hear about Big Data is large data volume. There are other dimensions than volume in Big Data, see Big Data For Dummies for instance, but let's focus on large data sets. Is current optimization technology ready for the huge data sets available now... [More]
Tags: optimization predictive prescriptive analytics big_data |

## 2015 Prediction: Prescriptive Analytics Will Make It
This is prediction season. I never played that game so far, but felt compelled to do so after reading quite a few predictions about what will happen in 2015 around Analytics and Big Data. I won't repeat what seems to be a consensus, and will refer to two specific lists that I found more interesting than others. The first list is by Nathan Brixius, from Microsoft. Here are the top items, I'll let you read Nathan's blog to get the meat behind the titles. Adoption of higher productivity analytics programming... [More]
Tags: big_data prescriptive analytics optimization |

## Why Users Cannot Help You Improve Your Products
Making decision based on data seems a good idea, doesn't it? After all, this is the value promised by all Big Data promoters out there. Let's look at a real use case to understand better what might go right or wrong. I will focus on the decisions product managers must make when they think of the next version of their product. Should they base product evolutions on customer feedback? Let's first address the case of disruptive technologies. It is (now) (well) known that the answer to the above... [More]
Tags: decision big_data sampling analytics design |

## Price Optimization
Did you know that the price at which you buy your Coke bottle (pick any brand you'd like here) at your nearest retail store was probably set by a process that involved mathematical optimization? If not, then learn how it was probably done. Let me first say that the idea of optimizing prices isn't really new. The airline industry has rolled out techniques called revenue management in the 90s, where the number of seats offered at a given rate was modified according to the demand. In a nutshell, if a given flight... [More]
Tags: analytics optimization commerce big_data |

## Un Peu de Math
The following was triggered by a mathematical problem proposed by Vincent Granville. The problem was to compute the maximum value q(n) of a function related to the well known metrics Spearman's footrule , or L1. This would then be used in a new stat isti cal corr elat io n based on ranked variables that would be very useful for Big Data applications. I'll refer readers to Granville's article for more details. At first sight this seemed quite diffficult, and Granville launched a... [More]
Tags: analytics big_data mathematics |

## Analytics Is A Mean To An End
Unless you've been unplugged for a couple of years, you have certainly witnessed the buzz around Big Data and Analytics . It is difficult to open a newspaper without seeing references to it on a monthly, if not weekly, basis. This is understandable, given the spread of analytics applications. It is now becoming common knowledge that analytics can help, whatever activity you are engaged with. The more I read, the more I see new analytics projects at IBM or at customer companies, the more I feel compelled to... [More]
Tags: analytics big_data optimization |