A new SAS user is born

Thanksgiving turkey, Blue Jays playoff baseball, and SAS ANALYTICS all in one day!

Thanksgiving is a wonderful holiday. The air is crisp, fall colours are at their peak, family and friends abound, and a veritable feast is at hand. This year was especially good because Thanksgiving Day included three of my favourite things: roast turkey, Blue Jays playoff baseball and coding in SAS/STAT.

First the baseball. If you are a SAS User residing in the U.S. then you are thinking that baseball is over by Thanksgiving, right? That’s true, unless you live in Canada. The Toronto Blue Jays defeated the Texas Rangers on October 9 (Thanksgiving in Canada) to win the American League Division Series. Let’s not discuss the AL Championship Series.

The roast turkey, as a Thanksgiving tradition, needs no explanation. The real story here is how SAS/STAT found its way on to my Thanksgiving Day menu.

It is natural to enjoy perks from family members’ occupations: retail employee pricing, airline benefits, medical advice, etc. Are there any perks from having a statistician in the family? Yes indeed. And one who knows SAS programming, even better.

My niece, Meaghan, sent me a note before the Canadian Thanksgiving weekend to let me know that she was bringing home research data from the University of Ottawa, where she is a Masters candidate in the Department of Biology. We were gathering at her parents’ home in Annan Ontario, overlooking the Georgian Bay of Lake Huron (exquisite view!). She had some questions regarding the statistical analyses. When we sat down to review the study and the data, I noticed that she was using two software packages: MS Excel (okay, I guess), and another package that will remain nameless. I joked that I could not help her until the offending file was deleted from my view. Meaghan explained that she would like to use SAS, but that it is too expensive. Ah ha! “Not so, mon ami.”

A new SAS users is bornIt did not take long to explain that SAS University Edition is free for use to students and faculty and suggested she download it. Within a few hours, Meaghan had SAS installed on her MacBook (!). In no time she was able to execute the SAS program that I helped her code to import her data from a CSV file, transform it for a mixed model analysis, and run several candidate models using the GLIMMIX Procedure. Meaghan’s study is a classic split-split-plot design where the plots, in this case, are rabbits. Seven replicates were measured in the smallest unit, and different rabbits were “measured” at 3 times: 4, 8, and 12 weeks. But it is better if I turn this blog over to Meaghan to explain the study and how she is learning SAS.

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Analytics competition provides students real-world opportunity (and why that’s so important!)

analytics-competition-provides-students-real-world-opportunity01Hi there! I am Murali Sastry, a student pursuing my Master’s degree in Analytics at Capella University (CU). I love data and really enjoy digging into it to find valuable insights. If you’re a student in an analytics program like I am, let me give you a bit of advice. In addition to your coursework, I encourage you to look for opportunities to apply what you learn either in a real-world setting, by pursuing an internship, or using real-world data, by participating in any of a number of analytics competitions. In this blog I wanted to share with you the experience I had in my first data analytics competition. The experience has already helped me in so many ways as I look to continue my study of and career in analytics.

Analytics competition provides real-world opportunity

Fortunately for me, CU staff is well connected to industry events, especially in Twin Cities Metropolitan Area (TCMA). Recently, our department chair sent a flyer to students about an analytics competition opportunity. The competition was jointly sponsored by: Midwest Undergraduate Data Analytics Competition (MUDAC), an institution with a history of conducting analytic competitions in the academic community; Social Data Science (SDS), a non-profit society focused on applying analytic insights to the public sector; and, MinneAnalytics, a seven-thousand-member non-profit organization. The competition was part of these organizations’ commitment to advancing the analytic professional community.

For this event, MinneAnalytics provided all the funding and the judges (eighty of them!). SDS worked with the Twin Cities Metropolitan Council to provide the three datasets (those exceeded six gigabytes), and MUDAC shared its five years of experience in hosting analytics competitions among academic institutions. Per MinneAnalytics, the competition focused on “Providing an experiential learning opportunity during which students grapple with real world data and research issues, and where the outcomes they determine matter.”

The competition was a great way to sink into real-world data and what better opportunity could one have to experience real-world data than in competition, right? MinneAnalytics supported the event by bringing together students, employers, and judges to showcase student presentations and insights (i.e., all analytics professionals - budding or otherwise) in one place. It was fabulous!

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Training professors to teach SAS a primary mission for SAS’ Global Academic Program

training-professors-to-teach-sasThe combination of a looming shortage of analytical professionals in the workforce and a recent Money and Payscale.com study which named SAS the top skill to pursue in today’s job market, many students are looking to learn SAS. Many universities are stepping up to meet this need with a well-developed curriculum designed to teach SAS skills. To help professors in this endeavor, SAS’ Global Academic Program offers instructor workshops designed to teach professors how to teach SAS.

The workshops are free of charge to professors (though travel and accommodations are the responsibility of attendees), and include course materials, meals and instruction from some of SAS’ top experts. .

Workshops are offered three times throughout the year, on both the East and West coast, with different topics covered at each session.  The first round of workshops kicks off in January 2017 in San Diego, CA at National University.  For more information on the workshops, and topics offered, visit our website.

To find out what makes these workshops so enticing for professors, we asked Dr. Charlotte Baker, an Assistant Professor of Epidemiology in the College of Pharmacy and Pharmaceutical Sciences at Florida Agricultural and Mechanical University, what she likes about the workshops.

Jenna Green: What made you decide to attend a Professor Workshop?
Charlotte Baker: I love learning new things and challenging myself to spend the time to learn. I really like the types of courses you all offered and it doesn't hurt that they are free!

JG: What value did you receive from attending?
CB: Continuing education is so important. The free workshops offered by SAS really provided me the opportunity to refresh myself on tools that I haven't used in a while and learn new things that I can use in my teaching and research. I feel like the workshops gave me a great value for the amount of time spent attending.

JG: What courses are you teaching?
CB: This semester I'm teaching a biostatistics laboratory that focuses on using SAS to apply what students have learned in their biostatistics course, advanced epidemiology and biostatistical methods, and a few advanced research independent studies.

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Three skills every student seeking a career in analytics should develop

skills_every_student_seeking_a_career_in_analytics_should_developThe job market for individuals with analytical skills is hot, and it’s only getting hotter. A recent study by the McKinsey Global Institute puts the situation in perspective, citing a shortfall of nearly 200,000 professionals with strong analytical skills by the year 2018. Businesses are looking to colleges and universities to help fill that gap, asking them to provide their students with the analytical skills they need to fill many of these currently vacant analytical roles.

How students can prepare for a career in analytics, and the role universities play in preparing them, is the focus of Dursun Delen’s recent article, Mandate for STEM Educators, found in the August issue of INFORMS magazine. Given the shortage, Delen, a professor of business analytics within the Department of Management Science and Information Systems at the Spears School of Business at Oklahoma State University, says educators haven’t had a “mandate this clear since the space race of the 1960s.”

And, the demand for analytics professionals doesn’t show any signs of abating any time soon. That’s great news for students educated in analytics. With so many businesses around the world looking to hire in support of their big data initiatives, these students are stepping squarely into a seller’s job market – a rare occurrence for many new graduates.

But that only works if the student is well prepared. In his article, Delen outlines three areas that will provide a firm foundation from which a student can build his analytical career.

First, is descriptive analytics, a simple concept but critical in solving big data problems. Descriptive analytics uses a sample of a given population or data set to report on and draw conclusions without having to use all data points. With the amount of data collected in today’s digital world, students with strong skills in descriptive statistics – think mean, median, standard deviation and a number of other reporting skills – is non-negotiable.

Delen cites predictive analytics as the second area critical to any analytical professional’s knowledge base. Of course, predictive analytics builds on descriptive analytics by using advanced statistical techniques, like data or text mining, to go beyond the snapshot descriptive analytics provides. According to Delen, predictive analytics builds a model to forecast future trends, understand customers, improve business performance, drive strategic decision-making and predict behavior.

Delen says the third layer in the “analytics hierarchy” is prescriptive analytics. Prescriptive analytics goes beyond even predictive analytics by identifying the optimal decision from the universe of options. Delen says techniques in prescriptive analytics include optimization and simulation, methods that can have the greatest impact for companies using advanced analytics.

To help build a student’s expertise in analytics, a number of universities have created advanced degree or certificate programs in analytics. In addition to course work, many of these schools partner with area businesses to provide practical experience solving real-world problems.

SAS plays a critical role in many of these programs by partnering with universities to offer master of analytics, applied statistics, data analytics and a number of other degrees that use SAS software as the analytical tool of choice. Other schools offer joint certificate programs, marrying course content with SAS knowledge. (If you’re a student looking for such a program, here’s a list of the master programs with a SAS focus, as well as joint SAS/university certificate programs.)

Delen says cloud-based technology makes it even easier for companies like SAS to get software in the hands of students as well. He cites the Teradata University Network (TUN) as a great example of how software providers can provide the tools and resources professors need to ensure that their students have access to various technologies. Experience with analytical software and advanced technologies will serve students well as they launch their professional careers.

Delen talks about the importance of hands-on experience in the education of students, which is one reason why SAS, along with the INFORMS Analytics Section, sponsors the Student Analytical Scholar Competition. The purpose of the competition is to practice the process of structuring and presenting a compelling proposal for analytical work in the prescriptive analytics realm. Students read a case study that is based on a real-life project involving optimization and/or simulation and must craft a document known as a “Statement of Work” (SOW). Such documents are usually created early in a project, after some exploratory work, but may or may not fully define the problem. The challenge requires students to combine the “hard” STEM skills they’ve learned along with “soft” skills like business problem framing, communication, and presentation, just as they would have to do if they were competing to win business.

If you’re a student interested in pursuing a career in analytics, I encourage you to read Delen’s full article on the INFORMS website and learn more about the work SAS is doing in support of teaching, learning and research in education.

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Learn SAS on your own time with these simple steps

SAS Mini MOOC!!! What is MOOC? Yes, this was the big question from attendees who visited our Data Science Skills pod at SAS Forum UK 2016 – If you missed the forum, here are the resources. I also thought: why don’t write about it?

MOOC stands for Massive Open Online Course - free online learning, open for everyone, which you can use as part of a university course to improve your skills. Apparently, the term was created by Dave Cormier  (the Project Lead for Student Relations Management at the University of Prince Edward Island, Canada) in 2008 as a response to a course called Connectivism and Connective Knowledge (also known as CCK08), run by George Siemens of Athabasca University and Stephen Downes of the National Research Council. Then 2012 became “the year of the MOOC”, when top universities started to implement the model. More about its history can be found in Wikipedia.

Dave Cormier suggests five different steps to success in a MOOC:

  1. Orient: Materials, links and time.
  2. Declare: Have a place for your thoughts, maybe a blog.
  3. Network: Follow other people and make some connections.
  4. Cluster: Find yourself a cluster of people to connect with, a community for sharing.
  5. Focus: in what you want to achieve at the end of the course.

Now, we know what MOOC is, let me tell you about SAS’ Mini-MOOC. To make your journey with SAS simpler, we condensed all the SAS free resources and materials into six simple steps; the perfect route to get started with SAS and learn in your own time.

So, let’s navigate throughout the steps:

Step 1: Download the free SAS University Edition Software

mooc1

Yes, hit the button: Get free software, and start using world-class analytics software used by more than 80,000 business, government and university sites around the world. That means you'll be using the latest statistical and quantitative methods available, which allow you to build your analytical skills and prepare for gratifying careers across all industries.

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Working with the Hidden Markov Model

Editor's note: This post is part of a series of blogs written by SAS interns. To check out more posts written by our awesome interns, visit our SAS Intern Life blog series webpage.

Making a difference at SAS: My current project

the hidden markov modelI am currently pursuing my Ph.D. in the Department of Economics at North Carolina State University and earlier this year I secured a graduate student internship with SAS. For the last several months I've been working as an econometric fellow in the Econometrics Time Series group and have been fortunate enough to work on some very interesting things. One project has been particularly exciting (and it matches my dissertation) - developing the new Hidden Markov model (HMM) procedure for next year’s release.

HMM has been widely applied in engineering and the Artificial Intelligence industry, including signal processing and speech recognition (like Siri and Cortana, or the automatic subtitles in YouTube). Hidden means you can observe a sequence of signals, but do not know the sequence of states the model went through to generate the signals. Markov represents the hidden states or regimes evolve according to the transition probability under some assumptions.

Naturally, the HMM can also be applied in the financial market, since the asset prices are the signals you can observe, and the states of the economy are not known to most people. The one identifies the incoming bull or bear market and can make a fortune or avoid a loss. With the new multivariate HMM model, we can identify the most possible sequence of hidden states producing the combination of signals.

For example, by evaluating the return of the portfolio of 10 different stocks, we can find out the hidden states and how these stocks perform in each state. Then, by predicting the probability of incoming states, we can select the investing strategy accordingly. The following figure demonstrates how we used HMM to break down the stock index returns using S&P 500 data. We assume there are five states of the return process and the number of states also can be totally data driven. From the graph, we can clearly see each state cover a market condition, including positive return, negative return, the one with high volatility or peaceful period, etc.

the-hidden-markov-model

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Students in analytics: Sara Armandi

Next up in our series, Students in Analytics, we feature a recent graduate – Sara Armandi.

Sara graduated from the University of Copenhagen earlier this year and stayed close to SAS – very close. She’s part of graduate program that SAS offers to help her develop her skills as a data scientist.

Hear more about her experience with the SAS graduate program in her area.

Interested in a career using SAS? Visit the getting started with SAS webpage to find all the resources to help you – it could even lead to your own job at SAS.

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Clinton and Trump take Twitter

Clinton and Trump take Twitter

Clinton vs. Trump 2016: Analyzing and Visualizing Sentiments towards Hillary Clinton and Donald Trump’s Policies

Oklahoma State University graduate student Siddharth Grover has always been interested in politics, so the idea to analyze the Twitter buzz around the US presidential candidates came naturally to him. He did a similar analysis of the General Election in India, while getting his MBA there in 2014.

For this project, Grover analyzed trending topics, keywords and sentiment of the tweets sent from both candidates’ Twitter accounts @realdonaldtrump and @hillaryclinton plus trending hashtags like #trump2016 and #clinton2016, from April to June 2016.

He also collected information on the number of followers, retweets, and “favorited” tweets for both candidates. As we all know during campaign season, political candidates are prolific, so the volume amounted to about 200,000 tweets. In three months.

Using SAS® Enterprise Miner and SAS® Sentiment Analysis Studio, Grover analyzed the contents of every tweet, examining each candidate’s views on the top issues (immigration, taxes, gun control, etc.) and identifying patterns of sentiment and how they shifted across time and geographic regions. He presented his findings at the Analytics Experience 2016 conference. The results were fascinating.

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Students in analytics: Lucy D’Agostino

As we continue our Student in Analytics series, we chat with Lucy D’Agostino.

Lucy is a PhD student at Vanderbilt University studying biostatistics. She believes SAS is an essential tool to have in your “tool belt” -- and something that will give you an advantage in your career.

Listen to some other tips from Lucy on what SAS resources she finds most helpful.

Another tip for people interested in learning SAS is downloading SAS University Edition. This free version of SAS will allow you to get hands on with the software. If you need some formal training, there’s free e-courses and tutorials to help. Visit the getting started with SAS webpage to find all the resources.

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How interns can be entrepreneurs

#sasinternlifeWhat can interns have in common with Steve Jobs? Their spirit -- an entrepreneurial spirit.

These days, the role of an entrepreneur isn’t just reserved for those launching startups. Drive, passion, and overall entrepreneurial spirit can resonate within all employees, including interns, even at large global companies.

Interning at a leading global tech company is any student’s dream. Filled with great minds and collaborative teams, SAS is an amazing place for interns to gain free knowledge (and college students love free stuff). Since I started as a SAS intern in the summer of 2015, I’ve noticed the great similarities between interns and entrepreneurs. While entrepreneurs utilize their assets, take risks, and seek potential investors, interns make use of valuable SAS resources, tackle projects and new learning opportunities, and network with those who could potentially invest in their future careers.

Anyone can be an entrepreneur and bring an entrepreneurial spirit to the workplace, especially interns who are fresh faces to the company and can bring novel ideas to the table. Here are some ways I think interns can start being an entrepreneur:

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