data scientist

5月 112016
 

As the demand for analytical skills continues to grow and the data scientist has been catalogued as the sexiest job of the 21st century, more and more students are showing interest in the analytics and big data world. We asked one of our graduates to share her experiences working as […]

The post How one data scientist turns ideas into reality appeared first on Generation SAS.

4月 202016
 

Just last weekend, I was considering buying a new camera lens. I already had a few brands in mind, so I looked online at their websites to learn more about their product information. I was able to conduct a comparison on different brands and lenses to narrow down to a specific 50mm lens provided by a major brand. I added the lens to my cart online, but wanted to get a closer look of it, so I chatted online with a representative to see if there were any lenses available at stores near me. This digital channel was my first point of interaction with the brand, but what impact did that have on my buying experience? Would responsive design come479424735 into play? Would the brand proactively contact me about similar products? Or would they simply react to inquiries that I had as a consumer? But today’s consumers expect immediate, individualized messages – would this brand deliver?

The fact of the matter is that a lot of brands don’t have the capabilities to modify messages, offers and interactions across channels, devices and points in time so that they are more relevant to the end consumer.

 Enter SAS

SAS Customer Intelligence 360, launching this month to the marketplace, offers an all-encompassing view of customers no matter how they choose to engage with you across digital properties.

A complete customer view

SAS Customer Intelligence 360 can give you detailed insights from digital channels customers interact with to create the most effective and relevant actions. The solution rapidly transforms digital data into a complete 360-degree view of the customer, meeting each customer’s needs at the right time, place and in proper context. Multiple decision-making methods, such as predictive models and multivariate tests, help ensure that customers gets the most relevant and personalized offers.

Data integration

Data is also easy to integrate with many offline customer channels though SAS Customer Intelligence 360 and its customer decision hub. Customer interactions are based on previous engagements on all other platforms. The data hub is able to convert all of this into customer-focused actions. With this data integration, the Customer_decision_hubbrand is able to gather my interactions and information from all available sources; not just the website, but the call center, mobile apps, social media and point of sale.

Offline customer data can be appended to digital data to further augment the view of me as a customer. These data sources, typically demographic or transactional in nature, gives marketers valuable insight into a customer’s true needs in order to create more relevant offers, better targeted activities and more efficient use of marketing resources. This capability allows the brand to see me more than just page clicks. They’ll see me as a father with young children, interested in photography and seeking to buy a 50mm lens to capture fleeting family moments.

Insights into future actions

You don’t need to be a data scientist to harness the power of predictive marketing; SAS Customer Intelligence 360 includes guided analytics to provide marketers a forward-looking view of customer journeys. This enables them to better understand business drivers and incorporate them into segmentation, optimization and other analytic techniques. Marketers can better forecast how customers will perform in the future. The solution acts as the data scientist – enabling marketers to become more efficient and effective in the analytical techniques they embed into marketing initiatives.

Web data collection

Each web page is embedded with a single line of HTML that automatically collects page information without expensive tagging. With this feature, the webpage configuration might change simultaneously with what I click on, the order and timing of my clicks, each keystroke, etc. Dynamic data collection offers me more relevant content as I navigate through the brand’s site. Any customer activities are recorded privately and securely over time so that once a customer is identified, the information is connected automatically.

Simply put, SAS Customer Intelligence 360 offers marketers the confidence to manage their digital customer journeys in a more personalized and profitable way. Marketers gain a complete view of their customers and transform this data using analytical insight into customer-centric knowledge and future actions. With this solution, brands can interact with customers on a personalized level and customers will be more satisfied with their entire relationship with a brand, not just a single transaction. Customer loyalty goes up and attrition goes down.

And as for me, I got the lens I was looking for, and was satisfied with the customer experience. Of course I have ideas on how to improve it on behalf of this brand, and SAS Customer Intelligence 360 fits into that picture.

tags: customer decision hub, customer journey, data hub, data scientist, Digital Intelligence, Predictive Marketing, Predictive Personalization, SAS Customer Intelligence 360

SAS Customer Intelligence 360: Digital discovery and engagement brought into focus was published on Customer Intelligence.

4月 112016
 

In a few short years, the need for people with analytics skills could significantly outpace supply. In fact, recent research from MGI and McKinsey's Business Technology Office says: By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million […]

The post Three tips for developing an analytics program that prepares students for big data careers appeared first on Generation SAS.

3月 162016
 

Numerous studies and statistics point to the fact that in just a few short years the need for people with analytics skills could significantly outpace supply. With so much talk around the analytics skills gap and the growing market for analytic talent, we wanted to highlight a variety of avenues […]

The post Preparing for Big Data Careers: Interview with Robert McGrath, University of New Hampshire appeared first on Generation SAS.

2月 042016
 

Numerous studies and statistics point to the fact that in just a few short years the need for people with analytics skills could significantly outpace supply. With so much talk around the analytics skills gap and the growing market for analytic talent, we wanted to highlight a variety of avenues […]

The post Preparing for Big Data Careers: Interview with Jennifer Priestley, Kennesaw State University appeared first on Generation SAS.

12月 042015
 

On the first day of Big Data Analytics my colleagues sent to me: A data scientist discussing a decision tree On the second day of Big Data Analytics my colleagues sent to me: Two business analysts and A data scientist discussing a decision tree On the third day of Big Data Analytics my […]

The twelve days of big data analytics was published on SAS Voices.

11月 232015
 

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 […]

What skills do you need to be a data scientist? was published on SAS Voices.

10月 012015
 

If I were to believe the feedback I get, statisticians are among the most difficult people to work with. What’s more, they’re the only group that should be allowed to work in data analytics. It sounds harsh, but this may explain why big data projects continually fail. Businesses need statisticians who are both […]

Three tips for building a data scientist team was published on SAS Voices.

9月 122015
 
Data Science

Lyn Fenex, Experis

The data science profession has been called the sexiest job of the 21st century. It’s also landed on the list of the 25 highest-paying jobs with the most openings right now.

There’s a wealth of knowledge on the web describing “what is” a data scientist, but there are far fewer resources to help people learn the steps it takes to practice in this elusive discipline.

“I’m an applied statistician and I’m really trying to figure out what a data scientist is,” said Christine Wells, UCLA. “I’ve heard several people call themselves data scientists, but they don’t have any overlap in their skillsets.” Wells got some of her questions answered during a presentation at the Western Users of SAS Software (WUSS) conference in San Diego.

Presenter Lyn Fenex of Experis explained what you need to know to jumpstart your understanding of the theory and tools of data science.

8 data skills to get you hired

Fenex highlighted these eight skills that will help get your hired as a data scientist:

 

  • Basic Tools
  • Basic Statistics
  • Machine Learning
  • Multivariable Calculus and Linear Algebra
  • Data Munging
  • Data Visualization & Communication
  • Software Engineering

“You don’t have to have all of these skills,” said Fenex. “There are certain things you already know that will qualify you for some of these positions.” Her advice is to read data science job descriptions closely.  This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.

Most in-demand data skills

Fenex stressed that data scientists need to be able to program, preferably in different programming languages such as Python, R, Java, Ruby, Clojure, Matlab, Pig or SQL.  Data scientists should also have an understanding of Hadoop, Hive and/or MapReduce.

WUSS attendee and data scientist, Joseph Lei, offered his opinion about what it takes to be a data scientist. “Being a data scientist involves combining many different disciplines in a way that makes sense,” said Lei. “But I don’t think it’s possible to be a data scientist without being able to code.”

Resources for getting started

SAS offers free resources to help you get started down the path of a data scientist career.

tags: data science, data scientist, US Regional Conferences

Jumpstart your data science career was published on SAS Users.

9月 072015
 

Along with the data scientist hype, analytics and the people who make them work have found themselves in the spotlight. The trend has also put an emphasis on the "science" aspects of analysis, such as a data focus, statistical rigor, controlled experiments and the like. Now, I’m not at all against adding more […]

Enter the data composer was published on SAS Voices.