Comments Video by Kiran Ramsey and Amanda Caffey | The Daily OrangeWith a 6-foot-4 frame and some of the most untapped potential the Raleigh, North Carolina, basketball community had ever seen, Day’s high school head coach Chris East insisted that she had the potential to be a top player.“(The Day twins) were a little raw when they first came in as freshmen, but they just worked,” East said. “The best part is, both of them wanted to get better. I’ve never seen kids that have a motor like they do, these kids took it to another level.”After spending nearly two years lifting, shooting and drilling in the school’s gymnasium, the Day twins led Millbrook to a state championship appearance in 2011. The following season, surrounded by six sophomores, they led the Wildcats to a state title — a new height for the program, East said.In the spring of 2016, weeks after the Orange lost to Connecticut in the national championship game, and seven years after East had said something similar, SU head coach Quentin Hillsman told Day that he wanted to see her tap deeper into her potential. He said he especially wanted to see her improve her mid-range game in the offseason in order to be an asset to the 2016-2017 squad.“She needs to be able to step out a little bit and shoot the ball from 17 feet,” Hillsman said at the Orange’s preseason media day. “Her game has to evolve some for us to be successful this season.”She stayed in Syracuse for both academic sessions of the summer, preparing for her final go-around as a senior. Most of Hillsman’s players go home for the first session and return for the second.,It was a six-week period that Day spent almost solely playing basketball, and extra time in the Carmelo K. Anthony Center that helped her in almost every facet of their game. Bria Day said the twins were able to go back to North Carolina for a short visit, but otherwise, it was a full summer of basketball.“You could stay here all summer, and do nothing, and it wouldn’t really matter,” Hillsman said, explaining it was the time in the gym that mattered. “That’s just the way it is.”When the rest of the team returned for the second session of summer classes to begin preseason practices, there was a noticeable buzz around the program regarding the new and improved Day, teammates and coaches said at media day.Graduate assistant coach Maggie Morrison, a former Orange guard who’s spent the last three seasons with Day at Syracuse, said she immediately noticed Day was more aggressive, much more vocal on and off the court and performing as “a better all around player.”Morrison sees Day in more of a leadership role this year, and expects her final season at SU to be her best. She thinks that with the time Day spent in the weight room and gym this summer, she’s in the best shape she’s been in since arriving at SU, and poised to be one of the team’s strongest assets.With just one season remaining in her SU career, Day’s opportunity to take her game to the next level is limited. But as was the case in high school, she’s lived in the gym and made basketball her top priority. If she’s able to fully piece her game together, the Orange will thrive because of it.“Everything (with Day) is about toughness,” Hillsman said. “She’s really gotten herself to the level where she plays hard, and that’s what’s really important for her is staying aggressive. I’m hoping she’s able to do that all season and make us a better team.”To read the rest of the stories in Basketball Guide 2016, click here.,Banner photo by Jessica Sheldon | Photo Editor In eighth grade, when Briana Day decided to hang up her track spikes and join her middle school basketball team, the tall center said she was “awful” compared to where she’s at now. She was too big for her body, and her coordination wasn’t nearly where it is today.Day had a lot to improve on in her first years of playing, but since she didn’t start until eighth grade, the improvement would have to come much quicker than it did for her peers if she wanted to become a target for collegiate programs.“(My sister Bria and I) weren’t always good. I’m glad nobody has footage of (us playing in eighth grade),” Day said. “It was just so bad, we wore glasses, we didn’t have contacts yet we just looked so ugly, it was bad.”But Day knew that with time in the gym, she could change that.AdvertisementThis is placeholder textDay has built on what she learned as a young player, living by her credo of dedicating herself to her craft even now as one of the top centers in college basketball. The starting senior center for the No. 14 Orange, Day has learned that when something doesn’t come easy, the remedy is almost always the same. It’s how she took her Millbrook (North Carolina) High School squad to back-to-back state championship appearances, and how she plans on helping to bring SU back to the national championship game. Published on November 10, 2016 at 1:07 am Contact Matt: email@example.com,Cancel replyYou must be logged in to post a comment.
The prioritization of accelerated time to insight in contemporary business intelligence platforms and the corresponding evacuation of statistical rigor have facilitated the proliferation of spurious correlative analytics that confuse the distinction between correlation and causation. For example, recent analyses claim a correlation between an embryo’s exposure to deep ultrasounds and the onset of autism, the consumption of carrot juice and the amelioration of cancer and the ability of classical music to slow down dementia. While such studies may individually have merit with respect to the sample size upon which they operate, they evacuate deeper questions about the dataset in question, such as whether the correlation can serve as the theoretical foundation for causation on a broader scale.Machine learning technologies promise to resurrect the role played by statisticians by empowering data analysts to model relationships between a multitude of variables in contrast to business intelligence platforms that deliver correlative relationships between two variables. Moreover, machine learning platforms have the capability to model evolution in relationships over time.While the proliferation of BI platforms ushered in the death of the statistician and enabled the acceleration of the derivation of data-driven insight, it correspondingly facilitated the production of analyses that may have lacked the statistical rigor of analyses that examined correlation through the rich lens of tools at the disposal of trained statistician.Machine learning tools promise to restore some of that analytical rigor to data-driven analytics by providing additional dimensions of insight to conversations about correlations that may or may not be illustrative of causation. In conversations about health and wellness, in particular, enhanced analytical rigor about correlation vs. causation can go a long way toward ensuring that consumers of data driven analytics make well-informed choices about the best options for their health instead of falling prey to the flotsam and jetsam of findings that are produced by the emergent factory of analytical insights that has been made possible, in part, by the democratization of BI tools. One of the remarkable consequences of the contemporary proliferation of data is the corresponding profusion of data-driven analytics across a wide range of industry verticals. In particular, research in verticals such as environmental science, education, pharmacology, medicine and public health differentially explore concepts such as correlation and causality as they relate to two or more variables.For example, the contemporary profusion of data has enabled a surfeit of research that asserts a relationship between variable X and cancer, variables Y and Z and the longevity of a marriage or variable Z and a detriment or benefit to the environment. Concrete examples of such analyses include claims about the relationship between BPA and cancer, the financial investment in a wedding and the longevity of the associated marriage and vaccines and the onset of autism.Historically, statisticians have been central to efforts to understand the statistical significance of correlative analytics that illustrate a relationship between two variables. That said, one of the notable consequences of the widespread adoption of business intelligence platforms is their diminution of the importance of the statistician and corresponding elevation of the ability of business users to derive actionable insights about large-scale datasets. In addition, business intelligence platforms absolve users of the need to write custom code and subsequently accelerate the derivation of analytic insights.While the acceleration of the derivation of analytic insights, the democratization of data scientist related capabilities and enhanced data visualization functionality represent some of the key advantages of contemporary business intelligence and data analytics platforms, drawbacks include the diminution of considerations related to statistical or analytic methodology, significance and rigor. Another way of putting this would be to say that contemporary business intelligence platforms have ushered in the death of the statistician, and laid the foundation instead for data savvy, business users capable of rapidly wrangling through a massive dataset and understanding relationships between one or more variables by means of a panoply of rich and multivalent visualizations.