Data science doesn’t have to be difficult

You could argue that it’s misguided for someone like me to say data science doesn't have to be difficult. After all, I’ve been in the industry for many years and should have a few tricks up my sleeve for dealing with data. But with the latest data visualisation technology – it even includes built-in analytics capabilities - you don’t have to be an expert to use it.

I saw this first-hand when one of our own interns used SAS Visual Analytics during his time with us earlier this year. In just a month, he delivered a full analysis of how UK universities are using data analytics for research and academic purposes.Tightrope

Each summer, we recruit eight to 10 students for a SAS summer internship that gives them a taste of being a data scientist. Over a six-week period, our interns attend a short training course and are assigned to different business departments. Each intern is tasked with solving a unique business challenge using SAS data analytics techniques.

Here’s what Kushal Shah shared with us during his internship: Read More »

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What skills do you need to be a data scientist?

To be successful as a data scientist you need technical skills like programming and mathematical skills, but you also need passion and the ability to put information into context and explain its significance, says Dr. Goutam Chakraborty of Oklahoma State University.

In the video below, Chakraborty explains that Oklahoma State University has launched a new MS in analytics program to train data scientists who will graduate with big data and data science certifications.

Hear more from Chakraborty, including how to apply your data science skills:

And if you're looking to hire a data scientist with the skills Chakraborty describes, be sure to read our e-book, Your data scientist hiring guide, which includes 20 interview questions and three data scientist profiles.

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How to future proof your data strategy

In my previous post, I discussed some of the challenges and costs organizations face when they’re stuck in Excel hell with no real data strategy. Now that we’ve discussed the problem, let’s dive into the solution.

Every organization needs a data strategy with these building blocks:strategy

Your top priority is linking your data to company strategy. Here are the steps you need to take:

  1. Identify strategic goals - i.e. long-term company objectives.
  2. Break the strategic goal down into objectives. Assign a stakeholder to each objective as the driver/owner of that objective and define initiatives that fall under each objective.
  3. Create a project work group that includes IT, end-users and information consumers. This small work group will start the project(s) that contribute to the larger initiative. With your first few projects, go for quick wins (projects that show maximum value for a minimum of effort). Early successes will help prove the value of the overall initiative and help get (and keep) the team motivated.
  4. Translate projects into solutions. The cross-functional project team will work together to address challenges from multiple angles and translate business requirements into actual solutions.

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Let consumers teach you a thing or two this holiday season

The holiday season is a festive time of the year. But it can also be a nail biter for retailers. Months of planning can be sidetracked by fickle consumers or aggressive pricing from competitors.

Paying attention to even a little bit of data can work wonders.

In October, SAS conducted online research among adult consumers from Australia, Canada, New Zealand, the United Kingdom and the United States to uncover their holiday buying plans. This survey won’t help you restock your stores or redo your promotional plans, but it points out the potential of what you can do with your own data now and for holiday seasons to come.

We talked to 3,458 consumers. As a retailer, you’ve got lots more consumer information than that at your fingertips already. So what are you doing with it?

Here are four insights from the survey and some thoughts on how you can enhance these insights for your own company. Read More »

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Best practices for using data and analytics

Know your data. Do a needs analysis. Organize for success. Empower users. These are four best practices for data and analytics that you'll want to hear more about.

In my first three posts in the Analytics in Real Life blog series, we learned how higher education customers are using SAS and why they chose SAS. These customers also explained the positive impact of using SAS and analytics for their users and institution and they also shared tips for gaining buy-in for data and analytics projects.

In this last post in the analytics in real life blog series, the following customers share best practices for using data and analytics. Read More »

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Sawdust and SAS Studio: Thoughts on a liberal arts education during an IoT workshop

I brushed aside some sawdust on the workbench and set my laptop down. It wasn’t really mine. SAS Library Services had kindly lent me a new laptop for the “Making Sense of Sensor Data” workshop at UNC’s BEaM Makerspace. I had just set the laptop down…in sawdust. Like any normal person, I immediately began to think of a good lie I could tell when I returned the laptop.

Two students learning about arduino, sensor data and SAS

Two students learning about Arduino, sensor data and SAS.

SAS: Why does this laptop smell like sawdust?

ME: I don’t know. I noticed that, too. I think it must be Windows 10.

SAS: Windows 10?

ME: Yes, I think Windows 10 just smells like that.

SAS: But we don’t have Windows 10 on that laptop.

ME: No, of course not. It kept asking me if I wanted to install Windows 10 and I kept clicking “no” and then I smelled something like burnt leaves…

SAS: Windows 10?

ME: Yup. I think I read about this online somewhere. They call it the “Winter Woodburn” scented desktop.

That’s the problem with lying. There are a small group of people who are exceptionally good at it and the rest of us are lousy. I decided I would defend myself with the truth.

“Yes, I set the laptop down in some sawdust,” I would say, “but at least I didn’t set it down beside the table saw.” That would earn me some points back, I thought.  Resolved, I pressed the power button to turn the laptop on and proceeded as planned. To my relief, the loaner laptop was apparently impervious to a little sawdust. Read More »

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The hell of Excel, or why you need to future proof your data strategy

This summer, I had several interesting sessions with customers and prospects. Much to my surprise, two of them, both multinational organizations, were doing most of their data related tasks in Excel.

This happens every now and then -- I come across organizations (like yours?) where people are manipulating and ‘analysing’ data in Excel. In many cases, they’re doing this even though other, much better, tools are available.

When I see what some of them have accomplished, I must admit it’s astonishing. They build extremely complex Excel sheets full of formulas, v-lookups and macros that do their tricks.

But when I start asking questions, it quickly becomes clear that even though Excel does the things they want it to do, the time and effort it takes far outweighs the business value they get in return.

This is what I like to call ‘the hell of Excel’ -- a lot of people are experiencing it (and some don’t even know they’re in it). Read More »

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Text mining, the movie

42-15326326I hope by now you’ve seen the movie “The Imitation Game” with Benedict Cumberbatch and Keira Knightley. It’s the true story of Alan Turing, whom many consider to be the father of the modern-day computer and the discipline of computer science.

Turing’s innovation (and the movie) takes place in early 1940’s London during the Nazi domination of Western Europe and the Atlantic. The Germans were killing three allies every 15 seconds in the early years of the war.

Like all military organizations, the Germans were communicating military instructions via coded messages. These messages were sent and received by a code machine nicknamed “The Enigma.” The Enigma codes changed every 24 hours. While the Allies were able to intercept all German messages, they couldn’t decrypt them, thus rendering the messages useless.

The inability to decipher German messages was preventing the Allies from gaining any military intelligence -- and that was costing them the war.

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3D printing: yes or no?

3D printer printing a test print

A 3D printer test print.

By now, you’ve probably seen a video of a 3D printer discharging layers of plastic to create a model of a building or a plastic figurine. You may have heard stories about 3D printed guns, 3D printed airplane parts and even 3D printed body parts parts.

While 3D printers are becoming more common, they are still a long way from being common household or break room devices.

How will 3D printers be relevant in the field of analytics? And what are their true strengths? We asked Matthew Horn, manager of SAS’ Emerging technologies UI lab for his thoughts – and here’s what we found out.

What it is

3D printing is a method for creating physical objects or models by “printing” multiple thin layers of materiel successively to form an object. 3D printers can easily print intricate shapes and interlocking pieces. Read More »

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How to get buy-in for data and analytics projects

Do you have a great idea for an analytics project but need approval to get started? Or maybe you've had some initial successes with analytics and you're ready to expand the program. We talked to four analytics leaders in the higher education industry to get their advice on how to gain buy-in for analytics projects.

In my first two posts in the Analytics in Real Life blog series, we learned how higher education customers are using SAS and why they chose SAS. Then they shared the positive impact of using SAS and analytics for their users and institution.

Today the following customers share tips use for gaining buy-in for data and analytics projects:

  • Gina Huff, Senior Applications Programmer Analyst at Western Kentucky University
  • Karl Konsdorf, Acting Director, Research, Analytics and Reporting at Sinclair Community College
  • Dan Miller, Director for Business Intelligence for the North Carolina Community College System
  • Sivakumar Jaganathan, Executive Director, Data Warehouse and Business Analytics for the University of Connecticut

Analytics in real life: gaining buy-in

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