Social media has the potential to facilitate much closer relationships between libraries and their patrons. Current usage of social media by the library community generally remains ad hoc and somewhat experimental, but the uptake of these tools is accelerating, and they will likely play an increasingly important role in library service provision and outreach in the future. Taylor & Francis has produced a white paper that analyzes current practices relating social media’s use in the library and how this differs by librarian job role. The sample was taken from academic librarians around the world, which also allows us to examine differences by geographic location. The goal: to establish how librarians are currently using social media in their roles, the most useful social media tools and best applications for these tools in a library setting.
explores a variety of social media tools in terms of how they can be used to organize information and communities. Together, you will survey and use a variety of social media tools, such as Delicious, Diigo, Facebook, Goodreads, Google Hangouts, LibraryThing, Pinterest, Storify, Twitter, and more! You will also explore how social media tools can be used to organize and disseminate information and how they can be used to foster and sustain communities of learning.
With the widespread use of library technology that incorporates social media components, intelligent objects, and knowledge-sharing tools comes the ability of libraries to provide greater opportunities for patron engagement in those discovery systems through user-generated content. These features may include the ability of users to contribute commentary such as reviews, simple point-and-click rating systems (e.g. one star to five stars), or to engage in extensive discussions or other social interactions. This kind of content could transform authoritative files, alter information architecture, and change the flow of information within the library discovery system.
Revenues for self-paced e-learning in 2016 are heavily concentrated in two countries — the United States and China. The growth rate in the U.S. is at -5.3 percent, representing a $4.9 billion drop in revenues by 2021, while in China, the rate is at -8.8 percent, representing a $1.9 billion drop by 2021. The e-learning market in China has deteriorated rapidly in just the last 18 months, the report said.
Of the 122 countries tracked by Ambient Insight, 15 have growth rates for self-paced e-learning over 15 percent during the next five years. These countries are heavily concentrated in Asia and Africa, with the two outliers being Slovakia and Lithuania.
Eleven of the top 15 growth countries will generate less than $20 million by 2021. Of the top 15, Slovakia and Lithuania are anticipated to generate the highest revenues for self-paced products by 2021, at $55.4 million and $36.5 million, respectively.
The growth rates are negative in every region except Africa, where the growth is flat at 0.9 percent. The steepest declines are in Asia and Latin America at -11.7 percent and -10.8 percent, respectively. The economic meltdowns in Brazil and Venezuela are major inhibitors in Latin America.
There are 77 countries with flat-to-negative growth rates. The countries with the lowest growth rates are Yemen (-18.7 percent), Brazil (-19.8 percent), Qatar (-23.5 percent) and Venezuela (-26.8 percent).
Self-paced e-learning products include online courses, managed education services, managed training, e-books and learning management systems, according to the report. The author does not consider mobile and game-based learning, which are growing, to be in the self-paced e-learning category.
The news on the self-paced e-learning industry is so bad, Ambient Insight will no longer publish commercial syndicated reports on the industry, the firm says on its website and in the report.
W3Schools – Fantastic set of interactive tutorials for learning different languages. Their SQL tutorial is second to none. You’ll learn how to manipulate data in MySQL, SQL Server, Access, Oracle, Sybase, DB2 and other database systems.
Treasure Data – The best way to learn is to work towards a goal. That’s what this helpful blog series is all about. You’ll learn SQL from scratch by following along with a simple, but common, data analysis scenario.
10 Queries – This course is recommended for the intermediate SQL-er who wants to brush up on his/her skills. It’s a series of 10 challenges coupled with forums and external videos to help you improve your SQL knowledge and understanding of the underlying principles.
TryR – Created by Code School, this interactive online tutorial system is designed to step you through R for statistics and data modeling. As you work through their seven modules, you’ll earn badges to track your progress helping you to stay on track.
Leada – If you’re a complete R novice, try Lead’s introduction to R. In their 1 hour 30 min course, they’ll cover installation, basic usage, common functions, data structures, and data types. They’ll even set you up with your own development environment in RStudio.
Advanced R – Once you’ve mastered the basics of R, bookmark this page. It’s a fantastically comprehensive style guide to using R. We should all strive to write beautiful code, and this resource (based on Google’s R style guide) is your key to that ideal.
Swirl – Learn R in R – a radical idea certainly. But that’s exactly what Swirl does. They’ll interactively teach you how to program in R and do some basic data science at your own pace. Right in the R console.
Python for beginners – The Python website actually has a pretty comprehensive and easy-to-follow set of tutorials. You can learn everything from installation to complex analyzes. It also gives you access to the Python community, who will be happy to answer your questions.
PythonSpot – A complete list of Python tutorials to take you from zero to Python hero. There are tutorials for beginners, intermediate and advanced learners.
Read all about it: data mining books
Data Jujitsu: The Art of Turning Data into Product – This free book by DJ Patil gives you a brief introduction to the complexity of data problems and how to approach them. He gives nice, understandable examples that cover the most important thought processes of data mining. It’s a great book for beginners but still interesting to the data mining expert. Plus, it’s free!
Data Mining: Concepts and Techniques – The third (and most recent) edition will give you an understanding of the theory and practice of discovering patterns in large data sets. Each chapter is a stand-alone guide to a particular topic, making it a good resource if you’re not into reading in sequence or you want to know about a particular topic.
Mining of Massive Datasets – Based on the Stanford Computer Science course, this book is often sighted by data scientists as one of the most helpful resources around. It’s designed at the undergraduate level with no formal prerequisites. It’s the next best thing to actually going to Stanford!
Hadoop: The Definitive Guide – As a data scientist, you will undoubtedly be asked about Hadoop. So you’d better know how it works. This comprehensive guide will teach you how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Make sure you get the most recent addition to keep up with this fast-changing service.
Online learning: data mining webinars and courses
DataCamp – Learn data mining from the comfort of your home with DataCamp’s online courses. They have free courses on R, Statistics, Data Manipulation, Dynamic Reporting, Large Data Sets and much more.
Coursera – Coursera brings you all the best University courses straight to your computer. Their online classes will teach you the fundamentals of interpreting data, performing analyzes and communicating insights. They have topics for beginners and advanced learners in Data Analysis, Machine Learning, Probability and Statistics and more.
Udemy – With a range of free and pay for data mining courses, you’re sure to find something you like on Udemy no matter your level. There are 395 in the area of data mining! All their courses are uploaded by other Udemy users meaning quality can fluctuate so make sure you read the reviews.
CodeSchool – These courses are handily organized into “Paths” based on the technology you want to learn. You can do everything from build a foundation in Git to take control of a data layer in SQL. Their engaging online videos will take you step-by-step through each lesson and their challenges will let you practice what you’ve learned in a controlled environment.
Udacity – Master a new skill or programming language with Udacity’s unique series of online courses and projects. Each class is developed by a Silicon Valley tech giant, so you know what your learning will be directly applicable to the real world.
Treehouse – Learn from experts in web design, coding, business and more. The video tutorials from Treehouse will teach you the basics and their quizzes and coding challenges will ensure the information sticks. And their UI is pretty easy on the eyes.
Learn from the best: top data miners to follow
John Foreman – Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
DJ Patil – Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
Nate Silver – He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
Andrew Ng – As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
Bernard Marr – He might know pretty much everything there is to know about Big Data.
Gregory Piatetsky – He’s the author of popular data science blog KDNuggets, the leading newsletter on data mining and knowledge discovery.
Christian Rudder – As the Co-founder of OKCupid, Christian has access to one of the most unique datasets on the planet and he uses it to give fascinating insight into human nature, love, and relationships
Dean Abbott – He’s contributed to a number of data blogs and authored his own book on Applied Predictive Analytics. At the moment, Dean is Chief Data Scientist at SmarterHQ.
Practice what you’ve learned: data mining competitions
Kaggle – This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
Stack Overflow – The best way to learn is to teach. Stackoverflow offers the perfect forum for you to prove your data mining know-how by answering fellow enthusiast’s questions.
TunedIT – With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
DrivenData – You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
Quora – Another great site to answer questions on just about everything. There are plenty of curious data lovers on there asking for help with data mining and data science.
Meet your fellow data miner: social networks, groups and meetups
Facebook – As with many social media platforms, Facebook is a great place to meet and interact with people who have similar interests. There are a number of very active data mining groups you can join.
LinkedIn – If you’re looking for data mining experts in a particular field, look no further than LinkedIn. There are hundreds of data mining groups ranging from the generic to the hyper-specific. In short, there’s sure to be something for everyone.
Meetup – Want to meet your fellow data miners in person? Attend a meetup! Just search for data mining in your city and you’re sure to find an awesome group near you.
By enabling the development and creation of big data for non-commercial use only, the European Commission has come up with a half-baked policy. Startups will be discouraged from mining in Europe and it will be impossible for companies to grow out of universities in the EU.
Beyond social media, there is also a clear disconnect between how college admins reach out to their students and what students actually pay attention to. While the average college admin, like most adults, is used to reading and sending emails, students are quickly moving away from using email in their daily lives and getting them to check it regularly is painful.
A fantastic New York Times article in the fall examined college student use of technology and the results were fascinating.
While some faculty members are hesitant to contact students on whichever social media platform is in vogue, others have explored texting as an alternative to email.
The paper, which is being presented at next month’s Information and Telecommunications Education and Research Association conference, also recommends colleges should consider using texting and social media platforms to reach students. However, the findings still suggest email can be an effective method of communication.
How Millennials use and control social media, Published
Social Media Usage Trends Among Higher Education Faculty ;
The proposed social media privacy law, scheduled to be considered by the state Senate Wednesday, bars any institution from asking or requiring an applicant or enrolled student to disclose a user name or password for a personal social media account.
Under the bill, a student could also not be prevented from participating in extracurricular activities if they refuse to disclose social media accounts or provide a list of contacts associated with those accounts.