As the cost of sensors and the connectivity necessary to support those sensors has decreased, this has given rise to a network of interconnected devices. This network is often described as the Internet of Things and it is providing a variety of information management challenges. For the library and publishing communities, the internet of things presents opportunities and challenges around data gathering, organization and processing of the tremendous amounts of data which the internet of things is generating. How will these data be incorporated into traditional publication, archiving and resource management systems? Additionally, how will the internet of things impact resource management within our community? In what ways will interconnected resources provide a better user experience for patrons and readers? This session will introduce concepts and potential implications of the internet of things on the information management community. It will also explore applications related to managing resources in a library environment that are being developed and implemented.
Education in the Internet of Things Bryan Alexander, Consultant;
How will the Internet of Things shape education? We can explore this question by assessing current developments, looking for future trends in the first initial projects. In this talk I point to new concepts for classroom and campus spaces, examining attendant rises in data gathering and analysis. We address student life possibilities and curricular and professional niches. We conclude with notes on campus strategy, including privacy, network support, and futures-facing organizations.
What Does The Internet of Things Mean to a Museum? Robert Weisberg, Senior Project Manager, Publications and Editorial Department; Metropolitan Museum of Art;
What does the Internet of Things mean to a museum? Museums have slowly been digitizing their collections for years, and have been replacing index cards with large (and costly, and labor-intensive) CMS’s long before that, but several factors have worked against adopting smart and scalable practices which could unleash data for the benefit of the institution, its collection, and its audiences. Challenges go beyond non-profit budgets in a very for-profit world and into the siloed behaviors learned from academia, practices borne of the uniqueness of museum collections, and the multi-faceted nature of modern museums which include not only curator, but conservators, educators, librarians, publishers, and increasing numbers of digital specialists. What have museums already done, what are they doing, and what are they preparing for, as big data becomes bigger and ever more-networked? The Role of the Research Library in Unpacking The Internet of Things Lauren di Monte, NCSU Libraries Fellow, Cyma Rubin Fellow, North Carolina State University
The Internet of Things (IoT) is a deceptively simple umbrella term for a range of socio-technical tools and processes that are shaping our social and economic worlds. Indeed, IoT represents a new infrastructural layer that has the power to impact decision-making processes, resources distribution plans, information access, and much more. Understanding what IoT is, how “things” get networked, as well as how IoT devices and tools are constructed and deployed, are important and emerging facets of information literacy. Research libraries are uniquely positioned to help students, researchers, and other information professionals unpack IoT and understand its place within our knowledge infrastructures and digital cultures. By developing and modeling the use of IoT devices for space and program assessment, by teaching patrons how to work with IoT hardware and software, and by developing methods and infrastructures to collect IoT devices and data, we can help our patrons unlock the potential of IoT and harness the power of networked knowledge.
Lauren Di Monte is a Libraries Fellow at NC State. In this role she develops programs that facilitate critical and creative engagements with technologies and develops projects to bring physical and traditional computing into scholarship across the disciplines. Her current research explores the histories and futures of STEM knowledge practices.
I’m not sure if the IoT will hit academic with the wave force of the Web in the 1990s, or become a minor tangent. What do schools have to do with Twittering refrigerators?
Here are a few possible intersections.
Changing up the campus technology space. IT departments will face supporting more technology strata in a more complex ecosystem. Help desks and CIOs alike will have to consider supporting sensors, embedded chips, and new devices. Standards, storage, privacy, and other policy issues will ramify.
Mutating the campus. We’ve already adjusted campus spaces by adding wireless coverage, enabling users and visitors to connect from nearly everywhere. What happens when benches are chipped, skateboards sport sensors, books carry RFID, and all sorts of new, mobile devices dot the quad? One British school offers an early example.
New forms of teaching and learning. Some of these take preexisting forms and amplify them, like tagging animals in the wild or collecting data about urban centers. The IoT lets us gather more information more easily and perform more work upon it. Then we could also see really new ways of learning, like having students explore an environment (built or natural) by using embedded sensors, QR codes, and live datastreams from items and locations. Instructors can build treasure hunts through campuses, nature preserves, museums, or cities. Or even more creative enterprises.
New forms of research. As with #3, but at a higher level. Researchers can gather and process data using networked swarms of devices. Plus academics studying and developing the IoT in computer science and other disciplines.
An environmental transformation. People will increasingly come to campus with experiences of a truly interactive, data-rich world. They will expect a growing proportion of objects to be at least addressable, if not communicative. This population will become students, instructors, and support staff. They will have a different sense of the boundaries between physical and digital than we now have in 2014. Will this transformed community alter a school’s educational mission or operations?
Weiler, A. (2005). Information-Seeking Behavior in Generation Y Students: Motivation, Critical Thinking, and Learning Theory. Journal Of Academic Librarianship, 31(1), 46-53.
The research indicates that only a very small percentage of the general population prefer to learn by reading.
members of “Generation Y,” the generation born between 1980 and 1994.
The first model for study of information-seeking behavior in the general population was developed by James Krikelas in 1983. This model suggested that the steps of information seeking were as follows: (1) perceiving a need, (2) the search itself, (3) finding the information, and (4) using the information, which results in either satisfaction or dissatisfaction.
A second model developed by Carol C. Kuhlthau of Rutgers University stresses a process approach with an emphasis placed on cognitive skills; as they increase, so does information-seeking effectiveness. This model is one of the few that was developed based on actual research and not simply on practical experience.
Eisenberg and Berkowitz proposed a model based on the “Big Six Skills”—task definition, information seeking, implementation, use, synthesis, and evaluation. Their model is flexible and nonlinear in the same way that hypertext is, allowing for different areas and avenues to be explored out of sequence. In addition, seekers can go back to refine and reidentify the information need, implementing new strategies.
Critical thinking is a process that is widely acknowledged in the literature to be crucial to the learning process, to cognitive development, and to effective information seeking.
A more effective lesson on Internet information then, rather than specifically dwelling on “good” and “bad” Web sites, would be to present actual examples and to raise questions rather than giving answers, opening the student up to the next level intellectual development, “multiplicity.” Multiplicity is the ability to acknowledge that the world contains knowledge that the student cannot yet classify as right or wrong, knowledge which requires further study and thought (the so-called “gray area”).
Behavior Theory, first developed by B. F. Skinner in the 1950s, uses the concepts of “positive” and “negative” reinforcement to control behavior. This theory explains learning behavior very simply: Reward students who perform well, and punish students who do not.
The “Control Theory” of behavior was developed by William Glasser. The theory states that, rather than being a response to outside stimulus, behavior is determined by what a person wants or needs at any given time, and any given behavior is an attempt to address basic human needs such as love, freedom, power, etc.
The Myers–Briggs Personality Analysis test, developed by Isabel Myers and Katherine Briggs, was developed using Jung’s theory of personality types in an effort to determine what type any given individual is. The personality type then determines the learning style of a given individual.
Multiple Intelligences
Gardner’s theory relates more directly to intelligence rather than to personality. Gardner states that intelligence is comprised of a group of different abilities, which originate in the stages of development each person passes through as they grow to adulthood. He identifies seven such intelligences—verbal–linguistic, logical–mathematical, visual–spatial, body–kinesthetic, musical–rhythmic, interpersonal, and intrapersonal—but he suggests that there are probably more.
Information seeking is a highly subjective process, one which students approach with prior knowledge, strongly held opinions, and differing levels of cognitive development. From the research it is apparent that, aside from personal preconceptions, issues of time and levels of difficulty in obtaining information are usually of more concern to students than issues of accuracy. It is still unclear, however, whether this is because they are not concerned about the accuracy unless their instructor is, or because they are assuming most information is by nature accurate.
disconnects into three categories—technology, policy, and unexploited opportunities—and discuss ways academic libraries can create next-generation landscapes to address these gaps.
Most library information systems and discovery tools are not easy to customize and remain substantially limited by an enduring library obsession with individual privacy and copyright.
Technology Disconnects
Some of the key technology disconnects between libraries and current online communities include:
Libraries lack tools to support the creation of new-model digital scholarship and to enable the use of Web services frameworks to support information reformatting (for example, RSS) and point-of-need Web-based assistance (multimedia tutorials or instant messaging assistance).
Dogmatic library protection of privacy inhibits library support for file-sharing, work-sharing, and online trust-based transactions that are increasingly common in online environments, thus limiting seamless integration of Web-based services.
Ubiquitous handheld access is more prominent thanks to digital lifestyle devices such as smart phones and iPods, yet libraries still focus on digital content for typical desktop PCs.
Policy Disconnects
Drawing a clear line between technology and policy can be difficult. For example, how many of the characteristics of current libraries (identified by the list below) are driven purely by technology or by policy? These traits include:
Mainly electronic text-based collections with multimedia content noticeably absent
Constructed for individual use but requires users to learn from experts how to access and use information and services
Library presence usually “outside” the main online place for student activity (MySpace, iTunes, Facebook, the campus portal, or learning management system)
Similarly, a policy solution might be required to address the following types of disconnects between libraries and online users:
Deliberately pushing library search tools into other environments such as learning management systemsor social network infrastructure and, conversely, integrating popular external search tools into library frameworks (such as Google Scholar and MS Academic Live Search or LibX.org)
Libraries linking and pointing to larger sets of open-access data that add context to their local collections
Restructuring access to reflect use instead of library organizational structure
Opportunity Disconnects
What is your library doing to:
Support the user’s affinity for self-paced, independent, trial-and-error methods of learning?
Create opportunities to make library information look and behave like information that exists in online entertainment venues?
Explore alternative options for delivering information literacy skills to users in online environments and alternate spaces?
Apply the typical user’s desire for instant gratification to the ways that libraries could be using technology for streamlined services?
Redefine administrative, security, and policy restrictions to permit online users an online library experience that rivals that of a library site visit?
Preserve born-digital information?
The promise of seamlessness that stems from ubiquitous computing access and instantly available networked information is, unfortunately, stifled significantly within the libraries of today.
Society for Information Technology and Teacher Education site.aace.org
March 5 – 9, 2017 Austin, Texas, USA
Proposals Due: October 21, 2016
SITE 2017 is the 28th annual conference of the Society for Information Technology and Teacher Education. This society represents individual teacher educators and affiliated organizations of teacher educators in all disciplines, who are interested in the creation and dissemination of knowledge about the use of information technology in teacher education and faculty/staff development.
SITE is unique as the only organization which has as its sole focus the integration of instructional technologies into teacher education programs. SITE promotes the development and dissemination of theoretical knowledge, conceptual research, and professional practice knowledge through conferences, books, projects, and the Journal of Technology and Teacher Education (JTATE).
You are invited to attend and participate in this annual international forum which offer numerous opportunities to share your ideas, explore the research, development, and applications, and to network with the leaders in this important field of teacher education and technology.
The Conference Review Policy requires that each proposal will be peer- reviewed by three reviewers for inclusion in the conference program, and conference proceedings.
Enquiries: conf@aace.org
Hosted By: AACE.org – The Association for the Advancement of Computing in Education
Sponsored by: LearnTechLib.org – The Learning and Technology Library
Faculty perceive undergraduates to be less proficient with digital literacy skills. One-third think
their students do not find or organize digital information very well. The majority (52%) think
they lack skill in validating digital information. My note: for the SCSU librarians, digital literacy is fancy word for information literacy. Digital literacy, as used in this report is much greater area, which encompasses much broader set of skills
Faculty do not prefer to teach online (57%) or in a hybrid format (where some sessions occur
online, 32%). One-third of faculty reported no experience with these least popular course types
my note: pay attention to the questions asked; questions I am asking Mike Penrod to let me work with faculty for years. Questions, which are snubbed by CETL and a dominance of D2L and MnSCU mandated tools is established.
Table 5. Do you use these in-class technologies for teaching undergraduates? Which are the Top 3 in-class technologies you would like to learn or use more? (n = 442)
usingonline resourcesto find high quality curricular materials
37%
48%
31%
3%
18%
iClickers
24%
23%
16%
9%
52%
otherpresentation tool (Prezi, Google presentation, Slide Carnival, etc.)
23%
14%
21%
15%
51%
whiteboard / blackboard
20%
58%
23%
6%
14%
Powerpoint or Keynote
20%
74%
16%
4%
5%
document camera / overhead projector
15%
28%
20%
14%
38%
Table 6. Do you have undergraduates use these assignment technology tools? Which are your Top 3 assignment technology tools to learn about or use more? (n = 432)
D2Lasa portal to other learning tools (homework websites, videos, simulations, Nota Bene/NB, Voice Thread, etc.)
21%
28%
18%
11%
42%
videos/animationsproducedelsewhere
19%
40%
36%
2%
22%
In both large and small classes, the most common responses faculty make to digital distraction are to discuss why it is a problem and to limit or ban phones in class. my note: which completely defies the BYOD and turns into empty talk / lip service.
Quite a number of other faculty (n = 18) reported putting the onus on themselves to plan engaging and busy class sessions to preclude distraction, for example:
“If my students are more interested in their laptops than my course material, I need to make my curriculum more interesting.”
I have not found this to be a problem. When the teaching and learning are both engaged/engaging, device problems tend to disappear.”
The most common complaint related to students and technology was their lack of common technological skills, including D2L and Google, and needing to take time to teach these skills in class (n = 14). Two commented that digital skills in today’s students were lower than in their students 10 years ago.
Table 9. Which of the following are the most effective types of learning opportunities about teaching, for you? Chose your Top 2-3. (n = 473)
Count Percentage
meeting 1:1 with anexpert
296
63%
hour-longworkshop
240
51%
contact an expert on-call (phone, email, etc)
155
33%
faculty learning community (meeting across asemester,
e.g. ASSETT’s Hybrid/Online Course Design Seminar)
116
25%
expert hands-on support for course redesign (e.g. OIT’s Academic Design Team)
114
24%
opportunityto apply for grant funding with expert support, for a project I design (e.g. ASSETT’s Development Awards)
97
21%
half-dayorday-longworkshop
98
21%
other
40
8%
multi-day retreats/ institutes
30
6%
Faculty indicated that the best times for them to attend teaching professional developments across the year are before and early semester, and summer. They were split among all options for meeting across one week, but preferred afternoon sessions to mornings. Only 8% of respondents (n = 40) indicated they would not likely attend any professional development session (Table 10).
Table T1: Faculty beliefs about using digital technologies in teaching
Count
Column N%
Technology is a significant barrier to teaching and learning.
1
0.2%
Technology can have a place in teaching, but often detracts from teaching and learning.
76
18.3%
Technology has a place in teaching, and usually enhances the teaching learning process.
233
56.0%
Technology greatly enhances the teaching learning process.
106
25.5%
Table T2: Faculty beliefs about the impact of technology on courses
Count
Column N%
Makes a more effective course
302
72.6%
Makes no difference in the effectiveness of a course
42
10.1%
Makes a less effective course
7
1.7%
Has an unknown impact
65
15.6%
Table T3: Faculty use of common technologies (most frequently selected categories shaded)
Once a month or less
A few hours a month
A few hours a week
An hour a day
Several hours a day
Count
%
Count
%
Count
%
Count
%
Count
%
Computer
19
4.8%
15
3.8%
46
11.5%
37
9.3%
282
70.7%
Smart Phone
220
60.6%
42
11.6%
32
8.8%
45
12.4%
24
6.6%
Office Software
31
7.8%
19
4.8%
41
10.3%
82
20.6%
226
56.6%
Email
1
0.2%
19
4.6%
53
12.8%
98
23.7%
243
58.7%
Social Networking
243
68.8%
40
11.3%
40
11.3%
23
6.5%
7
2.0%
Video/Sound Media
105
27.6%
96
25.2%
95
24.9%
53
13.9%
32
8.4%
Table T9: One sample t-test for influence of technology on approaches to grading and assessment
Test Value = 50
t
df
Sig. (2-tailed)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
In class tests and quizzes
-4.369
78
.000
-9.74684
-14.1886
-5.3051
Online tests and quizzes
5.624
69
.000
14.77143
9.5313
20.0115
Ungraded assessments
1.176
66
.244
2.17910
-1.5208
5.8790
Formative assessment
5.534
70
.000
9.56338
6.1169
13.0099
Short essays, papers, lab reports, etc.
2.876
70
.005
5.45070
1.6702
9.2312
Extended essays and major projects or performances
1.931
69
.058
3.67143
-.1219
7.4648
Collaborative learning projects
.000
73
1.000
.00000
-4.9819
4.9819
Table T10: Rate the degree to which your role as a faculty member and teacher has changed as a result of increased as a result of increased use of technology
Strongly Disagree
Disagree
Somewhat Disagree
Somewhat Agree
Agree
Strongly Agree
Count
%
Count
%
Count
%
Count
%
Count
%
Count
%
shifting from the role of content expert to one of learning facilitator
12
9.2%
22
16.9%
14
10.8%
37
28.5%
29
22.3%
16
12.3%
your primary role is to provide content for students
14
10.9%
13
10.1%
28
21.7%
29
22.5%
25
19.4%
20
15.5%
your identification with your University is increased
23
18.3%
26
20.6%
42
33.3%
20
15.9%
12
9.5%
3
2.4%
you have less ownership of your course content
26
20.2%
39
30.2%
24
18.6%
21
16.3%
14
10.9%
5
3.9%
your role as a teacher is strengthened
13
10.1%
12
9.3%
26
20.2%
37
28.7%
29
22.5%
12
9.3%
your overall control over your course(s) is diminished
23
17.7%
44
33.8%
30
23.1%
20
15.4%
7
5.4%
6
4.6%
Table T14: One sample t-test for influence of technology on faculty time spent on specific teaching activities
Test Value = 50
t
df
Sig. (2-tailed)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
Lecturing
-7.381
88
.000
-12.04494
-15.2879
-8.8020
Preparing course materials
9.246
96
.000
16.85567
13.2370
20.4744
Identifying course materials
8.111
85
.000
13.80233
10.4191
17.1856
Grading / assessing
5.221
87
.000
10.48864
6.4959
14.4813
Course design
12.962
94
.000
21.55789
18.2558
24.8600
Increasing access to materials for all types of learners
AAEEBL (The Association for Authentic, Experiential and Evidence-Based learning) starts the Baston Blog
Blockchain Credentialing: What Impact Will it Have?
Posted By Trent Batson Ph. D.
blockchain credentialing, big news since the MIT Media Lab offered an open source means of credentialing using blockchain technology (the technology behind bitcoin).
Blockchain credentialing makes verification of credentials much simpler and less time consuming, according to the articles I’ve collected below. Even IBM has entered the arena.
As with badges, we in the eportfolio world need to be aware of the trend toward blockchain credentialing. I’ve sorted through the links below so I could select those I thought would be most useful for you.
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.
Across generations, concerns for privacy may dissipate with time as specific technologies take hold or as people become aware of a technology’s benefits and value those over their value for privacy.
Library Privacy Guidelines for Students in K-12 Schools
Digital badges unify the learning that happens in these diverse contexts—often at a relatively granular level—with a common and portable representation of achievement.
Digital badges:
include a consistent set of metadata or information about the nature of the assessment, experience, or criteria that led to the skills or competency-based outcomes represented;
incorporate authentic evidence of the outcome being certified;
can be shared, displayed, or pulled into different kinds of platforms and environments in both human-readable and machine-readable formats;
can be distributed in a simple, consistent format, fostering relationship building, networking, and just-in-time career development opportunities;
are searchable and discoverable in a range of settings; and
offer data and insights about how and where they are used, valued, and consumed.
As a marker of achievement, a digital badge looks both backward and forward at the same time: backward to the experience or assessment that was completed to qualify for it, and forward to the benefits, rewards, or new opportunities available to those who have earned it.
Some of the possibilities you might consider include:
Serving as an alternate qualification for lifelong learning. Degrees and licenses certify summative achievements often following formal education programs or courses of study; do your digital badges provide official certification recognizing learning that is more granular, formative, or incremental?
Surfacing, verifying, or sharing evidence of achievement. How can we surface discrete evidence that certifies a skill or accomplishment, and by doing so arm learners with official recognition they can use toward new opportunities? Does validating and making a specific success or outcome more visible, portable, and sharable help a learner move successfully from one learning experience to the next?
Democratizing the process of issuing credit. How can we empower anyone who can observe or assess meaningful achievements to issue digital recognition of those accomplishments, even if that means that credential issuing becomes less centralized?
Exposing pathways and providing scaffolding. How can we better suggest or illuminate a path forward for learners while also enabling that pathway and progress to be shared with an external audience of peers or potential employers?
Supporting ongoing engagement. How can digital badges support learners incrementally as they progress through a learning experience? Can we enhance motivation before and after the experience?
The process for developing an effective badge system can be broken into steps:
Create a badge constellation. A constellation is a master plan or blueprint that shows all of the badges you intend to offer and how they relate to core themes or to each other.
Map meaning to each badge and to the overall badge system. Ensure that each part of your constellation has a value to the earner, to your organization, and to those who would reward or offer opportunities to bearers of each badge.
Identify or develop an assessment strategy. How will you know when an earner is ready to receive a badge? Are existing assessments, observation opportunities, or measures already in place, or does your system require new ways to determine when an individual has qualified for a digital badge or credential? What activities or work will be assessed, and what evidence can accompany each issued badge?
Determine relationships within the system and how learners progress. Is your plan one that shows progress, where components build on one another? How does one badge relate to another or stack to support ongoing personal or professional development?
Incorporate benefits, opportunities, and rewards into the system. Work backwards from the benefits that will be available to those who earn badges in your system. Does each badge serve a greater purpose than itself? What doors does it unlock for earners? How will you communicate and promote the value of your badges to all constituents?
Address technology considerations. How will you create and issue badges? Where and how will the badges be displayed or consumed by other systems and platforms in which they realize their potential value?
Develop an appropriate graphic design. While the visual design is but one element of a badge rich with data, how an achievement is visually represented communicates a great deal of additional information. Digital badges offer a unique and powerful opportunity to market the skills and capabilities of those who complete your programs, and badges promote your initiatives as well as your organization and what it values.
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.
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.
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.
