Archive of ‘digital identity’ category

text and data mining

38 great resources for learning data mining concepts and techniques

Learn data mining languages: R, Python and SQL

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!
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners – This book is a must read for anyone who needs to do applied data mining in a business setting (ie practically everyone). It’s a complete resource for anyone looking to cut through the Big Data hype and understand the real value of data mining. Pay particular attention to the section on how modeling can be applied to business decision making.
Data Smart: Using Data Science to Transform Information into Insight – The talented (and funny) John Foreman from MailChimp teaches you the “dark arts” of data science. He makes modern statistical methods and algorithms accessible and easy to implement.
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
Reddit – Reddit is a forum for finding the latest articles on data mining and connecting with fellow data scientists. We recommend subscribing to r/dataminingr/dataisbeautiful,r/datasciencer/machinelearning and r/bigdata.
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.

8 fantastic examples of data storytelling

8 fantastic examples of data storytelling

Data storytelling is the realization of great data visualization. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can get it.

Google’s Cole Nussbaumer provides a friendly reminder of what data storytelling actually is, it’s straightforward, strategic, elegant, and simple.



more on text and data mining in this IMS blog

your privacy and Google

How to see everything Google knows about you


more on privacy in this IMS blog

hackers versus crackers: Guccifer

Hackers versus crackers

Federal court sentences original Guccifer

By Mark Rockwell Sep 02, 2016


more in this blog on hackers and crackers


Finding and Using E-Government Tools and Resources

Librarianship in the Modern Era

Cutting the Red Tape: Finding and Using E-Government Tools and Resources
Diane Kovacs

4-week eCourse
Beginning Monday, September 12, 2016

E-government tools and resources bring many people to your library for such activities as filing and paying taxes online, locating Medicare/Medicaid providers and reviews, checking student loan status, tracking regulatory changes for industries, monitoring ongoing legislation as well as codified law and court rulings, and much more. This hands-on eCourse also explores the information published online by the U. S. federal government through the Government Printing Office and specific agencies and government branches.
Experienced online instructor and consultant Diane Kovacs covers the best sites to begin researching for government information in general and specifically for business, healthcare, genealogy, history, current government, legal, regulatory, taxes, retirement, insurance, and state and local government information.

social media and democracy

The biggest threat to democracy? Your social media feed

Vyacheslav PolonskiNetwork Scientist, Oxford Internet Institute
Yochai Benkler explains: “The various formats of the networked public sphere provide anyone with an outlet to speak, to inquire, to investigate, without need to access the resources of a major media organization.”
Democratic bodies are typically elected in periods of three to five years, yet citizen opinions seem to fluctuate daily and sometimes these mood swings grow to enormous proportions. When thousands of people all start tweeting about the same subject on the same day, you know that something is up. With so much dynamic and salient political diversity in the electorate, how can policy-makers ever reach a consensus that could satisfy everyone?
At the same time, it would be a grave mistake to discount the voices of the internet as something that has no connection to real political situations.
What happened in the UK was not only a political disaster, but also a vivid example of what happens when you combine the uncontrollable power of the internet with a lingering visceral feeling that ordinary people have lost control of the politics that shape their lives.

social media and democracy

Polarization as a driver of populism

People who have long entertained right-wing populist ideas, but were never confident enough to voice them openly, are now in a position to connect to like-minded others online and use the internet as a megaphone for their opinions.

The resulting echo chambers tend to amplify and reinforce our existing opinions, which is dysfunctional for a healthy democratic discourse. And while social media platforms like Facebook and Twitter generally have the power to expose us to politically diverse opinions, research suggests that the filter bubbles they sometimes create are, in fact, exacerbated by the platforms’ personalization algorithms, which are based on our social networks and our previously expressed ideas. This means that instead of creating an ideal type of a digitally mediated “public agora”, which would allow citizens to voice their concerns and share their hopes, the internet has actually increased conflict and ideological segregation between opposing views, granting a disproportionate amount of clout to the most extreme opinions.

The disintegration of the general will

In political philosophy, the very idea of democracy is based on the principal of the general will, which was proposed by Jean-Jacques Rousseau in the 18th century. Rousseau envisioned that a society needs to be governed by a democratic body that acts according to the imperative will of the people as a whole.

There can be no doubt that a new form of digitally mediated politics is a crucial component of the Fourth Industrial Revolution: the internet is already used for bottom-up agenda-setting, empowering citizens to speak up in a networked public sphere, and pushing the boundaries of the size, sophistication and scope of collective action. In particular, social media has changed the nature of political campaigning and will continue to play an important role in future elections and political campaigns around the world.


more on the impact of technology on democracy in this IMS blog:


digital humanities resources

more on digital humanities in this IMS blog:

pop computing

American schools are teaching our kids how to code all wrong

Idit Harel CEO, Globaloria, May 25, 2016

The light and fluffy version of computer science—which is proliferating as a superficial response to the increased need for coders in the workplace—is a phenomenon I refer to as “pop computing.” While calling all policy makers and education leaders to consider “computer science education for all” is a good thing, the coding culture promoted by and its library of movie-branded coding apps provide quick experiences of drag-and-drop code entertainment.

playing with coding apps as compared to learning to design an app using code. Building an app takes time and requires multi-dimensional learning contexts, pathways and projects.

Computing and computer science is the equivalent of immersing in a thicker study of music—its origins, influences, aesthetics, applications, theories, composition, techniques, variations and meanings. In other words, the actual foundations and experiences that change an individual’s mindset.

As noted by MIT’s Marvin Minsky and Alan Kay, computational innovation and literacy have much in common with music literacy. Just as would-be musicians become proficient by listening improvising and composing, and not just by playing other people’s compositions, so would-be programmers become proficient by designing prototypes and models that work for solving real problems, doing critical thinking and analysis, and creative collaboration—none of which can be accomplished in one hour of coding. In other words, just as a kid playing Guitar Hero wouldn’t be considered a musician, someone playing with coding apps isn’t exactly a coder or computer scientist.

more on coding in this IMS blog:

gen z coming to campus

Survey: What Gen Z Thinks About Ed Tech in College

A report on digital natives sheds light on their learning preferences.
Like the millennials before them, Generation Z grew up as digital natives, with devices a fixture in the learning experience. According to the survey results, these students want “engaging, interactive learning experiences” and want to be “empowered to make their own decisions.” In addition, the students “expect technology to play an instrumental role in their educational experience.”
to cater to the digital appetites of tomorrow’s higher education learners, technology in education will need to play a bit of catch-up, states the New Media Consortium’s 2015 Course Apps report. According to NMC’s analysts, digital-textbook adoption was one of the leading trends helping to reinvent how higher education students learn. But publishers have not captured the innovations happening elsewhere in the digital marketplace.

The Generation Z report ranked the effectiveness of 11 education technology tools:

  1. Smartboards
  2. Do-It-Yourself Learning
  3. Digital Textbooks
  4. Websites with Study Materials
  5. Online Videos
  6. Game-Based Learning Systems
  7. Textbook
  8. Social Media
  9. Skype
  10. Podcasts
  11. DVD/Movies

more on Gen Z in this blog:

Generation Z bibliography


1 2 3 6