Deep Learning Summer School, Montreal 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. If that is you, there are plenty of videos to help you learn more.
Professor Norm Matloff from the University of California, Davis has published From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science which is an open textbook. It approaches statistics from a computer science perspective. Dr. Matloff has been both a professor of statistics and computer science so he is well suited to write such a textbook. This would a good choice of a textbook for a statistics course targeted at primarily computer scientists. It uses the R programming language. The book starts by building the foundations of probability before entering statistics.
Want to learn about data science and the Internet of Things (IoT)? Futuretext is about to start Data Science for Internet of Things. It is a course aimed at people looking to learn the topics and transition into IoT and data science careers. Here are some quick highlights about the course.
- Starts Mid March 2016 and lasts through December 2016
- Personalized Course
- Available Online
Below is a list of topics.
- Data Science
- Machine Learning
- Data Science for IoT methodology
- Deep Learning
Do check out Data Science for Internet of Things for more details.
The fine folks at DataCamp, a great site for learning data science right in your browser, have come up with another great infographic. This time it compares some of the many job titles in the data science field.
The infographic lays out the roles and skills needed for the following job titles. Note: not all the job roles can be confused with a data scientist, but all the roles can be important when completing an entire data science project.
- Data Scientist
- Data Analyst
- Data Architect
- Data Engineer
- Database Administrator
- Business Analyst
- Data & Analytics Manager
Silk.co teamed up with LittleBird to create a list of the most influential “data science” accounts on Twitter. LittleBird uses an algorithm to rate an account’s influence based upon retweets from other influencers and network activity.
I was humbled to be included on the list with some very highly influential people. If you are on twitter, the list includes some great people and companies to follow. See the entire analysis at The Data Science Influencers: The Tops in Terms of “Insider Score” According to Influence Mapping Tool LittleBird.
Robin Murphy has one of the coolest job titles I have ever read, Disaster Roboticist. At Texas A&M, she works on developing advanced robots for disaster recovery.
In this Ted talk, she outlines some of the capabilities of the robots and how the robots work. One quote at the end really caught my attention.
So really, “disaster robotics” is a misnomer. It’s not about the robots. It’s about the data.
The robots are collecting data and that data needs analysis!
The National Football League begins its regular season tonight. One feature you might not hear about is the addition of 2 RFID sensors on every player. Each stadium is equipped with receivers (not wide receivers) to capture the data emitted from the RFID tags. When the data is collected, it will be able to track players position, movement, speed, and acceleration. A company called Zebra Technologies is implementing the system.
It is a bit early to know exactly what the NFL teams will do with the data, but I think the NFL should open up the data. Analysis could be done for fantasy football. Data scientists could come up with some creative data visualizations. Plus, I think it contains great academic research potential.
As a side note, I am sure someone would start building some apps for the Microsoft Surface tablets.
See more at The IoT comes to the NFL
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