This Week in Data Science (March 15, 2016)

Once again, here’s this week’s news in Data Science and Big Data.

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Interesting Data Science Articles and News

• Detecting Emotion in Faces Using Geometric Features – Learn how Carlos Argueta used basic geometry and machine learning in order to detect and recognize human emotions.
• Using Big Data To Predict The Election – OpenText’s U.S. Election Tracker helps U.S. voters make informed decisions by analyzing election data using natural language processing, semantic processing, and sentiment analysis. This is one many examples showing the potential for insights we can learn from big data.
• Size Matters: Why You Need Big Data Analytics – Businesses are realizing that they can apply analytics to their data in order to discover patterns and correlations and identify new opportunities.
• Introducing GraphFrames, a Graph Processing Library for Apache Spark – This is an introduction to Spark’s GraphFrames, a powerful new graph processing library.
• Fraud Detection with Deep Learning at Paypal
  – Venkatatesh Ramanathan talks about PayPal-Fraud Detection with H2O Deep Learning. He introduces the experimental process used in building the Deep Learning model.
• The Five Different Types of Big Data – Big Data is a vast term. Michael Kanellos helps to further clarify the differences between subsections of Big Data by explaining the five different types including fast data, lost data, and new data.
• Project Aims to Make Breathing Easier by Mapping Air Quality – In order to help asthma sufferers, allergy sufferers, and other people sensitive to air quality manage their health, researchers at the University of Texas, Dallas are working on the Geolocated Allergen Sensing Platform (GASP). It analyzes data from air-quality sensors and maps concentration of allergens and pollutants in real time.
• The Rise Of The Robot – Google’s Boston Dynamic’s robot can trudge through snow, carry boxes, and get up when knocked down. What are the implications of the rise of increasingly humanoid robots?
• Beginner’s Guide to the History of Data Science – Data Science is not as new as many think. Read through the most important events in data science history.
• Eyeglasses That Can Focus Themselves Are on the Way – Deep Optics, an Israeli startup, is developing eyeglasses that automatically adjust and focus themselves by sensing and analyzing data about the wearer’s line of vision. They are developing this to help people with vision disorders.
• A (small) introduction to Boosting – Learn about boosting, a machine learning meta-algorithm that iteratively builds a group of weak learners in order to generate a strong overall model.
• President Obama’s new open data initiative could help cities help themselves – President Obama is launching The Opportunity Project, a new initiative that that releases government data to the public and inspires developers to use federal and city data to build tools that can greatly help decision makers.
• IBM Expands Data Science Education around the World – IBM expands its academic initiative by helping professors teach students who don’t necessarily have an in-depth knowledge of data science how to learn from data. This program incorporates natural-language-based Watson Analytics into the classroom.
• You Must Allow Me To Tell You How Ardently I Admire and Love Natural Language Processing – Julia Silge uses natural language processing in order to analyze the sentiments involved in Jane Austen’s Pride and Prejudice.
• R or Python? Consider learning both – Instead of mastering one language, a data scientist should focus on learning data science concepts while expanding their programming toolbox. They should understand when it is best to use R and when it is best to use Python.
• Understanding Popularity on Reddit – The data analytics game Guess the Karma uncovers insights on what makes something go viral.

Upcoming & Recent Data Science Events

• Analytics & Technology Summit 2016 – Attend the A&T Summit on June 27th to June 28th.It’s a great way to network and share business and/or technology solutions before peers, decision-makers and a wide range of influential audiences.
• Big Data, Big Impact – Learn about using data science to evaluate United Nations Sustainable Development Goals on March 17th in Vancouver. Everyone is welcome.
• Data Science Methodology and Classification –Learn about data science methodology, the steps involved in converting a business problem into a data problem, on March 29th in Toronto
• Introduction to Python for Data Science – Learn about Python data science programming in Washington on March 29th.