Having babies faster with Big Data

A new application for iPhone and iPad, Ovuline’s Smart Fertility, uses machine learning techniques and big data to help women to become pregnant.

There are many smartphone apps that let women track their menstrual cycle. But the app from Ovuline company, a startup based in Cambridge (Massachusetts, USA), founded in 2012, is unlike any other in the use of massive data analysis to predict the best time to conceive.

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According to CEO, Paris Wallace, "One of the big reasons why women take, on average, 4 to 6 months to conceive is because they are not accurately predicting ovulation. Of our users who have reported pregnancy, they're doing so in about 60 days, which is about 3 times faster than the national average [of US]. So our algorithm and prediction engine seems to be working."

Ovuline app can be used by any woman, but seems to be especially attractive to couples who wait until their 30s or 40s to have children. "When they want to have a child,", says Wallace, "they want to get pregnant immediately. And then when it doesn't work over the course of a few weeks or a few months, they turn to Ovuline to help them conceive."

The numbers 

In the world of clinical decision support, one of the main obstacles to getting good reliable diagnostic and predictive tools with generalization power is to have a high enough volume of cases. The people behind Ovuline seem to have found a simple formula to get them: The app is totally free. "We really want to have as many consumers as possible sign up for our service and use it, obviously, because the more data we get, the better our service becomes, and the better our predictions are using our machine learning algorithms".

Thus, at the time of writing Ovuline already has over 5 million data records of more than 80,000 women. The information collected is from data on blood pressure, weight, cervical fluids, menstrual cycle, body temperature, and even sexual intercourse. To collect such data, they do not only use the app for iPhone, but also wearable quantified self devices for self-monitoring.

With the rapid growth of the volume of data they are experiencing, Ovuline is already developing other health applications. In autumn they will launch a system to monitor the pregnancy. "Women will enter information, and we'll also collect (data) on the quantified self devices."

Via:
[informationweek]

The sexyest job of the 21th century

On October, 2012 an article by Thomas H Davenport and DJ Patil entitled "Data Scientist: sexiest work of the century" was published in Harvard Business Review.

I recognize that this not so new to be news, but it is still a real trend: According to data collected in NatureJobs blog and jobs trend data from Indeed.com, the demand for such professionals has soared a 15,000% between the summers of 2011 and 2012.

"Data Scientist" Job Trends

"data Scientist" jobs

But what is a Data Scientist? 

The very definition of Data Scientist is controversial. This term was coined by DJ Patil and Jeff Hammerbacher and this is the story told in the epub by Patil "Building Data Science Teams" on how they came across with this term when they were building their respective data teams for LinkedIn and Facebook back in 2008:

“When Jeff Hammerbacher and I talked about our data science teams, we realized that as our organizations grew, we both had to figure out what to call the people on our teams. “Business analyst” seemed too limiting. “Data analyst” was a contender, but we felt that title might limit what people could do. After all, many of the people on our teams had deep engineering expertise. “Research scientist” was a reasonable job title used by companies like Sun, HP, Xerox, Yahoo, and IBM. However, we felt that most research scientists worked on projects that were futuristic and abstract, and the work was done in labs that were isolated from the product development teams. It might take years for lab research to affect key products, if it ever did. Instead, the focus of our teams was to work on data applications that would have an immediate and massive impact on the business. The term that seemed to fit best was data scientist: those who use both data and science to create something new.” 

What defines a Data Scientist?

 Although there is no standard definition of this term, nor is there (for now) a college to train data scientists (for now come from different areas and scientific disciplines), the mini-book Building Data Science Teams does yield a number of features that define them:

• Technical expertise: the best data scientists typically have deep expertise in some scientific discipline.
• Curiosity: a desire to go beneath the surface and discover and distill a problem down into a very clear set of hypotheses that can be tested.
• Storytelling: the ability to use data to tell a story and to be able to communicate it effectively.
• Cleverness: the ability to look at a problem in different, creative ways  

Wordcloud of the desired skills in a Data Scientist. [link]

What a Data Scientist looks for?

According to Davenport and Patil's article, salary is important to a Data Scientist but not everything. If you work in a well-established company, a good salary is a sign that you value their work, if they work for a start-up, which usually can not afford such high salaries, a data scientist appreciates that the company offers him some shares (benefits, stock-options) as a way to recognize his/her importance in the business.

But what really movesa data scientist are the challenges that improve the entire business. They need you to ask the right questions, that you point to what needs to be improved in the company or, much better, to be allowed to find out for themselves. And for that, they demand to be given freedom to maneuver to find the solution: This does not means that they are free from deadlines to report results, but they have grant access to every data in the company thay might be relevant to get the answers.

Maybe the concept of data scientist is a fad. And it may be temporary, as were the Yupies, or the Wall Street sharks back in the happy nineties, but what is certain is that there is a real need for most companies to analyze massive data from diverse sources. There is the technological framework that allows it (processing and storage capacity virtually unlimited) and now is is the time to get someone to do so. And here's when the Data scientists become sexy..

There's a new hero in the city. Half genius and half sexy: DataScienceMan [link]