Friday Roundup: What Interested Us the Week of February 2, 2015

Avatar
Posted on Feb 6, 2015

DJ Patil has joined the White House to wrangle data issues

, 2/5/15

DJ Patil, who may be best known for coining the term "data science"  and for calling data science the "sexiest" job of the 21st century, has joined the White House as a data scientist-in-residence.  He will focus on helping handle health care data, according to John Podesta, Counselor to the President.

The announcement was made in a news briefing conference call alongside the announced release of  Big Data: Seizing Opportunities, Preserving Values, a White House progress report on big data and privacy in the federal government.

4 Ways Big Data Is Transforming Healthcare

Bernard Marr, 1/29/15

Bernard Marr says, "It’s hard to think of a more worthwhile use for big data than saving lives", and it's certainly hard to disagree with that.  He discusses four ways in particularly that big data is contributing to the healthcare.
Analysis of claims data helps physicians find patterns in symptoms and treatments that can help make medicine more efficient. Methodist Hospital Health System has used a tool which analyzes Medicare claims data to highlight groups and individuals who may need expensive care in the future, allowing for less costly preventative action at an early stage.
Identifying medical conditions from patients data via text mining of their records has been another useful approach in identifying untreated illnesses. For example, "the American Medical Association reported that analysis of patient records found only 26% of children who had recorded three high blood pressure readings at separate visits to their doctors had been diagnosed as suffering hypertension – highlighting a significant number of failures to spot the condition."

A new trend among pharmaceutical companies to pool data about clinical trials is now possible due to the ability of data scientists to analyze "big data".  With the pooling of patient information have come some surprising results. For example, "the antidepressant desipramine is being trialled for its potential to destroy cancer cells in patients with small cell lung cancer."

Finally there is the area of wearable devices.  At the moment wearable devices are mainly used for personal fitness, but this is set to change.  "Spending on smart watches, wrist bands, running shoes and other wearables is expected to reach $52 million by 2019, according to a study by ABI Research."  But there is more, these new wearables, which are soon to become far more common, can do everything from monitor how well patients are taking care of themselves, to recognizing the onset of symptoms, and even monitoring medication compliance.

Mr. Marr is quite bullish on the relationship between healthcare and big data.  He concludes, "In the future we are likely to recover more quickly from illness and injury, and we will live longer.  New drugs will come into existence and our hospitals and surgeries will operate more efficiently – all thanks to big data."

 

Big Data vs. Fast Data

Michele Goetz, 2/4/15

Michele Goetz, who is  working on an upcoming report (with Noel Yuhanna) on how to develop your data management roadmap, wrote this article for Information Management this week.  She reports that while a recurrent theme in the big data world is "fast data" - fast data is not what we intuitively think of.   The speed of the technology is not the best way to think about what is needed.  Ms. Goetz says, "Today's reality is that we live in a world where it is no longer about first to market. Instead, we have to be about first to value.".

More and more the focus of attention is making the insights from data useful for the business - to add value, not just crunch numbers.  Not even just to come up with insights.  As Ms. Goetz explains the objective, "sending data out faster ignores what business stakeholders and analytics teams want. Speed to the business encompasses self-service data acquisition, faster deployment of data services, and faster changes. The reason: They need to act on the data and insights."

Ms. Goetz'a insights echo what we recently read in the CapGemini report about what it takes for a company to get a return on their investment in the world of data science.  There needs to be a unified vision, and the data scientist needs to work in an integrated way with the business strategists.

Data Science Job Posts

Posted by Matt, 1/24/15

For many employers, who "know" they need a data scientists, the question of what a data scientist really is, can make it difficult to know what to look for when making these hiring decisions.  Matt, from yseam.com has some suggestions.

First the company should be able to clearly describe the business need.  The key value proposition of the data scientist is to improve an existing product/service or to help build a new one.  Many business problems can be solved by a good data analyst.  Second, an employer should be clear about the responsibilities - particularly the "science" part of data scientists.  Companies should look for individuals who know the scientific method and can build experiments.  Third, look at the candidates portfolio of work.  Can this candidate solve the business problem that is required.

After answering these questions, the business also needs to consider if they have a single problem - a project based problem, or whether they truly need a full time data scientist.

 

 

Consumers Vs. Data Science Bad Guys

Levi King, 2/2/15

Levi King writes this week about the current state of privacy in the data world, and his predictions for how things will shake out.  Mr. King notes that "the world continues to spew vitriol at the creepy collection and abuse of data by governments and big businesses".  Nevertheless, the march towards decreases in privacy and information sold for profit continues to forge forward.

On the other hand, Mr.  Levi takes a historical perspective on the topic by looking at past trends, and how government responded to similar cries from consumers.  For example, should consumer outcry over privacy issues reach the levels they did with respect to credit card debt, which he predicts it will, there will soon be privacy laws comparable to the Fair Credit Reporting Act, or even the formation of a Consumer Privacy Protection Bureau like the newly minted Consumer Finance Protection Bureau.  He cites additional examples of laws enacted to protect the consumer, like legislation surrounding phone solicitation or lemon laws.

While Mr. Levi believes these laws are coming soon, he also has a message for new companies that are founded upon the principles of transparency: you will be richly rewarded.

 

 

 

About Author

Related Articles

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp