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The following article discussing workforce diversity was originally published in October 2015. All copy and relevant statistics have been updated as of April 2021. Higher revenue, better job performance, a more trusted employer brand, and an increased customer base are just a few of the benefits of a diverse workforce. Not to mention, 67% of active and passive job seekers say a diverse workforce is important to them when evaluating companies or job offers. Recruiters today need to take time to assess their diversity recruitment strategies. If your company wants to attract the best talent and reap the benefits of a diverse workforce, you should look at what you’re currently doing and integrate these tactics.
Unless you happen to possess luck on a superhuman scale, bad data will lead to bad decisions. Alas, the situation is not symmetrical: good data may or may not lead to good decisions. Good data can be corrupted in context — by the misinterpreter, by the inattentive, by the intrusion of luck of the bleakest kind. The publishing business operates with data that no self-respecting industry would tolerate (can you imagine an executive at Exxon Mobil not knowing how many cars are on the road, how many miles they drive, and how much gasoline they consume?), and within publishing, book publishers have the worst of it, with no hard evidence about who actually purchases and uses their products, assuming they are purchased and used and not simply accessed on a pirate site somewhere or, in their print form, simply serving to dress up a furniture store.
The five things you need to know.
With the demand for data scientists and data engineers predicted to grow 39 percent by 2020, higher education institutions are stepping up to create programs that teach students the skills necessary to fill these roles.
Dakota State University’s Master of Science in Analytics is one such program, recently ranked by College Choice as one of 2017’s 50 Best Online Big Data Programs.
EdTech spoke with Dorine Bennett, the dean of DSU’s College of Business and Information Systems, and Jun Liu, Ph.D., assistant professor and coordinator for DSU’s graduate analytics program, to learn what makes the program unique, how technology is helping them reach more students and what skills data scientists need to thrive in today’s world of data overload.
The concept of “big data” has been the subject of considerable hype and speculation in recent years. So much so that the dominant technologies and technical practices that generate big data — data analytics, algorithms and machine learning — are now commonly described as “artificial intelligence” instead. As a result, Ian Bogost argues, there has been “an explosion of supposed-AI in media, industry and technology.”
In today’s digital digital transformation, big data has given organization an edge to analyze the customer behavior & hyper-personalize every interact…
Key Takeaways
What are the top learning analytics challenges among institutions in the UK?
What organizational and technical considerations need to be addressed to pave the way for a successful learning analytics initiative?
When it comes to learning analytics, are institutions ready?
via The Scout Report "Anyone looking to create appealing visualizations as a way to share data will want to check out Sizzle Analytics. This free tool is designed for creating data visualizations. To create one, users simply upload data as a CSV (Comma Separated Variable) file. Next, users select what variables to measure and what kind of graph to use. Notably, Sizzle Analytics allows users to create graphs that allows others to select variables themselves - allowing for sleek, versatile visualizations. By creating a free account, users can save their work. Those looking for inspiration can check out Featured Boards, which include visualizations of the top Spotify tracks for 2015-2016."
Via Jim Lerman, Mark E. Deschaine, PhD
At the start of this year the UK Higher Education Academy (HEA) released its report ‘from bricks to clicks‘ which included (on p.7, copied below) a set of recommendations. Some of these are pretty UK specific (there may be Australian comparisons), but others are more general.
When it was released I thought it might be interesting for us to consider how UTS was addressing the recommendations (particularly as, per recommendation 8 and 9, my department falls within a strategic investment into university data adoption). Actually looking through, the bullets, I did have immediate responses to each, but there are key recommendations I think are more interesting, and that I think would need implementing as pre-requisites to other recommendations (e.g., data literacy should be built before any use of data in accountability structures). I might come back to this.
Big Data, with its vast data volumes, is largely useless without the data analytic and presentation functionality found in visual analytic tools. Think about how difficult it is to spot anomalies or trends in endless rows and columns of spreadsheet data. Visual analytic tools solve that data overload problem.
Data is the supportive brick for any presentation or report out there. It offers bits of reality that together form an accurate image.
Without data, marketers would create their campaigns on assumptions and suppositions, instead of knowing exactly what their clients want. Not to mention engineers would construct only hazardous buildings from the beginning. Even consumers need data to know how to use their new products accordingly.
It seems everyone is interested in big data these days. From social scientists to advertisers, professionals from all walks of life are singing the praises of 21st-century data science.
As the digital world becomes more complex, leadership teams are becoming increasingly dependent on analytics to help guide actions and decisions.
“Realistically, if we want to improve, we have to look to our analytics team,” said Gareth Hershel, research director, at Gartner Symposium/ITxpo in Barcelona, Spain. “Analytics is the glue to help achieve objectives given the culture and the strategy that a business has.”
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In my role as the library dean I have already been engaged with data-driven student success initiatives. We are still working to implement use of EAB on our campus. The company offers a set of tools that can track grades and registration (OK – we already do that). It also provides functionality that tracks visits to the tutoring center, the health and counseling centers, and can analyze how successful students are when they take specific classes and specific times. If you guessed that faculty are very wary of that last bit of analysis you’d be correct.
What is datafication? And how does it affect education? These questions were put to me ahead of conference discussion panel recently. While writing a few notes, it quickly became apparent I needed some categories to sort out my thinking. In simple terms, datafication can be said to refer to ways of seeing, understanding and engaging with the world through digital data. This definition draws attention to how data makes things visible, knowable, and explainable, and thus amenable to some form of action or intervention. However, to be a bit more specific, there are at least ten ways of defining datafication.
Learning analytics cannot yet be considered a mature field in UK higher education. However, the use of data about learners and their learning to address areas such as attrition and curriculum enhancement is increasingly being investigated through projects at an institutional level. A recent survey by the UK Heads of eLearning Forum (HeLF), which elicited responses from 53 institutions, suggested that there had been rapid change in the adoption of learning analytics over the past two years. Those institution s “working towards implementation” had nearly doubled from 34% to 66%. Meanwhile, the proportion of respondents’ universities not implementing learning analytics at all had decreased from 47% to 13% (Newland & Trueman, 2017).
This quote first appeared in the October 2012 issue of the Harvard Business Review. About 4 years later, it still hold true. According to glassdoor.com, Data Science is still the best job in the USA in 2016.
Of course, as you would expect, there has been some backlash and dissent against the sensationalist title of the now famous article. One of the most popular criticisms in 2012 was that automation would make more and more data scientists redundant.
Ever had to manually edit dusty, messy, years-old information from some obsolete software?
I once worked for a company that stored paperwork offsite for 60 years. Materials were indexed in a document table. Most records had a box number, storage date, storage vendor receipt number, and a rough idea of the contents. Most, mind you.
Interpreting data from a spreadsheet can feel a bit overwhelming to a lot of students. That's why I'm excited about Google's lates
Via WebTeachers
The ubiquity of big data is such that Gartner dropped it from their Hype Cycle of Emergent Technologies back in 2015. Across sectors, businesses are scrambling to make every function “data driven,” and there’s no shortage of firms lining up to help them. The big data analytics industry, dedicated to helping big businesses leverage the petabytes of information they now generate and store, is worth $122 billion — and growing.
Analytics is about getting your team the data insights it needs to build better products and make the right decisions for your company.
But if your team can’t understand that data, then this is all for naught. Software like Amplitude can make your data easy to understand, but each member of your team still needs basic data skills to get the most value out of what they’re looking at.
The Internet of Things and big data technologies have progressed enormously in 2016 – and 2017 is set to be a year when more enterprise use case
Literature Review: Transforming Big Data into Quantitative Insights Chapter One provides an overall overview of the research stud
This abridged guide will cover the essential things to look out for in selecting and purchasing embedded analytics software. The full guide is available here.
Whether you’re producing automation software, SaaS products or cloud applications, it’s likely to assume you’re collecting a lot of data in the process.
British Library Labs Competition (2016)
The annual Competition looks for transformative project ideas which use the British Library’s digital collections and data in new and exciting ways.
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