This is an example of using math and computation to prevent fraud!
Click on the link below to learn how it works!
This is an example of using math and computation to prevent fraud!
Click on the link below to learn how it works!
DON’T GIVE UP ON SIGNING UP FOR CLASSES!
We know the Junior core courses are currently listed as full for Fall 2015. We are working on options to accommodate everyone. If you want to take these courses in Fall 2015, please indicate that by wait-listing both math 320 and math 344 (in AIM, click WL under A**)
We love finding new articles that bring math into real world situations!
Here is one on the NBA! Since the starting of the NCAA basketball tournament, WIRED has this feature about how Kirk Goldsberry’s obsession with basketball with statistics is changing pro hoops.
Phys.org posted this interesting article, we think you will enjoy.
The most common program language for data scientists is R, but is being displaced by Python. To find out why, click on the link below.
Click on the link below to find out the variety of companies that hire data scientists.
Another day, another reason to get better at math.
It’s no secret that quantitative skills are in high demand on the job market—one analytics recruiter recently told The Journal that workers who can’t crunch numbers may ultimately face a “permanent pink slip.”
Now, a new ranking from the job-search website CareerCast.com names mathematician as the best occupation of 2014. “Math skills unlock a world of career opportunities,” publisher Tony Lee said. (Cue the Square One theme, and tune inMathnet.)
Data whizzes of all stripes fared well in the annual list: Statisticians (No. 3), actuaries (No. 4) and computer systems analysts (No. 8) all landed near the top.
Mathematicians pull in a midlevel income of $101,360, according to CareerCast.com, and the field is expected to grow 23% in the next eight years. Other high earners include actuaries and software engineers, who can expect to earn about a midlevel income of $93,000 per year.
Speaking of math, the list is tallied by scoring 200 types of jobs according to four categories: environment, which rates things like competitiveness; income at low, middle, and high career positions; the outlook for income and employment growth; and stress factors such as travel and deadlines.
Jockeying for last place on the list are–again–ink-stained wretches and wielders of axes, as journalists and lumberjacks get squeezed out by new technologies, the report notes. Lumberjacks fare worse in dollar terms, registering just $24,340 in midlevel income.
The outlook for some jobs are so bleak that they weren’t included on this year’s list. Among the occupations consigned to the dustbin are bricklayer, typist/word processor, and stationary engineer automobile assembler.
Best Jobs of 2014 / Midlevel Income
1. Mathematician / $101,360
2. Tenured University Professor / $68,970
3. Statistician /$75,560
4. Actuary / $93,680
5. Audiologist / $69,720
6. Dental Hygienist / $70,210
7. Software Engineer / $93,350
8. Computer Systems Analyst / $79,680
9. Occupational Therapist /$75,400
10.Speech Pathologist / $69,870
Worst Jobs of 2014 / Midlevel Income
200. Lumberjack/ $24,340
199. Newspaper Reporter / $37,090
198. Enlisted Military Personnel / $28,840
197. Taxi Driver / $22,820
196. Broadcaster / $55,380
195. Head Cook / $42,480
194. Flight Attendant $37,240
193. Garbage Collector / $22,970
192. Firefighter / $45,250
191. Corrections Officer / $38,970
BY ALAN BOYLE
This article is from http://www.nbcnews.com/storyline/missing-jet/how-math-solved-mystery-missing-malaysian-jets-path-n62026
Analysts at the Inmarsat satellite venture executed a computational tour de force to determine where a missing Malaysia Airlines jet probably went, but mathematician John Zweck says the feat is simple enough to explain with a basketball, a hoop … and an ant.
“The math involved is really just a little bit more than the trigonometry you’d have in high school,” Zweck, a professor at the University of Texas at Dallas, told NBC News.
Don’t worry: Zweck isn’t popping a trig quiz on us. Instead, he’s retracing the steps that the investigators at Inmarsat, Britain’s Air Accidents Investigations Branch and the Boeing Co. took to narrow down the search area for the Malaysia Airlines Flight 370 jet, which disappeared from radar screens on March 8.
All the investigators had to go on were signals from a transmitter on the Boeing 777 jet that sent hourly pings to an Inmarsat telecom satellite, hovering high over the Indian Ocean. They were able to glean two key sets of clues from those signals: the angular distance of the jet when each of the pings was sent, and the frequency of each ping.
The angular distance allowed Inmarsat to draw a series of arcs, to the north and to the south, indicating the range of possible locations when each ping was sent. “We know the times at which the airplane crossed those arcs,” Zweck said. But the arcs stretch so far it’d be impossible to search the entire swath of ocean.
Image: Jet’s course JOHN ZWECK / UT-DALLAS
An analysis of satellite pings from Malaysia Airlines Flight 370 allowed Inmarsat to sketch out broad arcs that the jet crossed hour by hour. Further analysis produced projected flight paths for the missing jet. The black dashed lines indicate the paths plotted by Inmarsat, and the yellow line indicates mathematician John Zweck’s computed flight path. The black dot with a white dot in the center indicates the precise point over which the satellite was flying.
To narrow down the search area, Zweck took the plane’s last known position, as of 2:11 a.m. local time, and then looked for straight-line routes that traced a great circle around the globe. That would be consistent with a scenario in which the crew — and perhaps the passengers as well — were incapacitated, leaving the plane to fly on autopilot until the fuel ran out.
North-south lines of longitude are examples of great circles. “However, in our case, instead of the great circles being lines of longitude through the North Pole, they are going through where the plane was thought to be at 2:11 a.m.,” Zweck said.
Put the ball in the hoop
If you’re looking for a straight-line, great-circle route, it’s possible to figure out the jet’s path using only the satellite data. This is where the basketball comes in handy.
“You could take a basketball, and draw the arcs with a red pen on the basketball,” Zweck said. “Then put the basketball in a hoop that fits snugly around the ball. Pin that basketball onto the rim at the place where the plane was at 2:11 a.m., and put another pin on the exact opposite point on the ball.”
The rim of the hoop can now define any of the great circles that pass through the starting point.
“Let’s say the rim has a ruler marked on it,” Zweck said. “Rotate the rim around the sphere and look to see where it crosses the arcs. Measure the distance between the first arc and the second arc, the second arc and the third arc, the third and the fourth. You want all those measurements to be the same.”
That would produce a route that intersected all the pings at the right time to reflect a constant speed for the plane.
How Investigators Narrowed Down the Missing Plane’s LocationNIGHTLY NEWS
Inmarsat’s analysts had an advantage over Zweck, in that they could estimate the speed of the plane by analyzing Doppler shifts in the frequency of the pings. “They knew how to calibrate their ruler,” Zweck said. “I didn’t, but I was able to infer it.”
Zweck ended up with a speed estimate that was the same as Inmarsat’s: 450 knots, or 518 mph.
“What I was able to do was reproduce Inmarsat’s results,” Zweck said. “But I didn’t use a basketball and a rim. I used a computer and trigonometry.”
North vs. south
There was one other problem to solve: The arcs could point to a northward flight just as easily as a southward flight. So which path did Flight 370 take? The key clue came from the pings’ Doppler shift.
You can hear a Doppler shift in the whistle of a passing train: The pitch rises when the train is approaching you, and falls when it’s chugging away from you. Similarly, the frequency of the jet’s ping would be slightly higher if the plane is moving toward the satellite, and slightly lower if it’s moving away.
To explain how this relates to the north vs. south question, Zweck metaphorically passes you the basketball once more. Imagine you’re holding the ball in front of you at arm’s length. The point in the very center of your vision is analogous to the point on Earth directly below the satellite.
“It makes all of us math majors around the world swell with pride.”
“We know the plane started north of the satellite,” Zweck said. “If you imagine that the plane was an ant on the basketball, the ant starts a little bit above the level of your eye. Now imagine that the ant is walking south, across the surface of the basketball. When the ant walks south along that straight line, it’s getting just a little closer at first.”
But when it crosses the plane of your vision, the ant starts receding. The jet would go through a similar, ever-so-slight advance and retreat as it crossed the equator and headed south.
That rate of movement, forward and back, was reflected in the pings’ Doppler shift: first a slight rise in frequency, and then a fall. “They were able to tell by looking at the frequency of the signal that it was getting closer to the satellite, but then after a certain point, it’s moving away,” Zweck said.
That suggested that the plane took the southern route. If it had gone north, the Doppler data would have shown that the plane was consistently farther away from the satellite.
The forward-and-back Doppler shift was so subtle that Inmarsat’s analysts had to double-check it by comparing data from other 777 jets that traveled similar routes. They also checked the data that the Malaysian jet sent back before it disappeared. “It all agreed,” Zweck said.
For more of the technical nitty-gritty, check out this statement from Malaysia’s acting transport minister, or Zweck’s UT-Dallas website.
Producing the proof
“It’s an amazing bit of analysis, because the beginning of the path is so darn close to the equator,” observed James Oberg, NBC News’ space analyst. “It makes all of us math majors around the world swell with pride.”
However, the only way to verify the analysis is to identify wreckage in the search area, an Alaska-sized stretch of the Indian Ocean about 1,550 miles (2,500 kilometers) southwest of Perth, Australia.
There have been some intriguing sightings of debris, gleaned from satellite imagery and aerial observations. Ships and aircraft from six countries — Australia, China, Japan, New Zealand, South Korea and the United States — are combing the area. NASA says it’s taking pictures of the southern search zone with its Terra and EO-1 satellites. But so far, Inmarsat’s masterful theorem has yet to be proven.
Breaking Down Bracket Math
this article is from http://www.bloomberg.com/video/march-math-ness-breaking-down-the-brackets-3YExBDv1RG~KCvn81KDfWg.html
article from http://blogs.hbr.org/2014/02/recruit-better-data-analysts/
In the big data talent wars, most companies feel they’re losing. Marketing leaders are finding it difficult to acquire the right analytical talent. In the latest CMO Survey, only 3.4% senior marketers believe they have the right talent. Business-to-business companies have a bigger gap than business-to-consumer companies, as do companies with a lower percentage of their sales coming from the internet. And yet analytic skill is a must for effective marketing.
Results indicate that companies with above-average marketing analytics talent experienced significantly greater rates of marketing return on investment (MROI) than companies with below average analytics talent (+4.18% vs. +2.51%). When it comes to profits, the same pattern emerged—companies that are above average on analytics talent experienced profitability increases of +4.69% compared to companies below average on analytics talent +2.71%. In short, while using any analytical skill truly is better than none, strong analytical skills are measurably better.
So how do you find those people? Given how tight the market for analytical talent is – and how critical it is to a business growth – companies have to adopt different strategies for hiring and keeping people. Some large companies have taken to acquiring start-ups or developing “research labs” jointly with academic institutions or organizations. But there are a range of tactics companies of any size can use to improve their analyst recruiting.
The first is simply using more specific language. At one top retailer, the analytics team was looking to fill a direct marketing measurement position but was not satisfied with the direct marketing experience in the CVs the recruiting team was sharing with them. So the analytics and recruiting teams came together to redefine the characteristics of the ideal candidate. This collaboration led to searching CVs for a more targeted set of keywords (not generic “measurement” skills but advanced “segmentation” and “predictive analytics” capabilities). The new approach led to the discovery of dozens of qualified candidates. Similarly, at General Mills, recruiters looking for senior marketing analytics managers found that using more precise and discerning language cut search times in half.
A second strategy is to use an “always on” approach to recruiting. As John Walthour, Director, Growth Insights & Analytics at General Mills, noted, “We know these positions will continue to be in demand at General Mills and so we no longer wait for a specific position to arise.” Still other employers search constantly in stealth mode for the best talent. For example, Beth Axelrod, SVP of Human Resources for eBay, works with companies such as Gild, which identifies prospective employees on the hard-science side of marketing analytics by examining the quality of their open code.
A third component is beefing up management’s analytical skill. We find that senior executives often don’t have a clear sense of what’s needed from the analysis and, therefore, don’t ask questions that lead to helpful answers. Senior managers need to be educated to understand the basics and be able to ask good questions, such as probing the quality of the statistics being used or asking about how to incorporate new types of data types.
Finally, in order to hire the best analysts, hiring managers may need to recognize that some softer business skills won’t come in the same person. Instead of holding out for the perfect total package, one banking company solved this issue by creating a mixed team of hard-core statisticians and marketers who together mined the data, analyzed the results, and developed marketing campaigns based on those results. After three months, the team was delivering better analytical insights, and both customer activity and revenues were nearly 10 times higher.
Whatever the strategy, however, acquiring the right array of marketing analytics talent is critical to turning big data into a powerful capability for companies.