Terminology in Data Analytics As data continue to grow at a faster rate than either population or economic activity, so do organizations’ efforts to deal with the data deluge, and use it to capture value. And so do the methods used to analyze data, which creates an expanding set of terms (including some buzzwords) usedContinue reading “Data Analytics”
Monthly Archives: October 2019
Data Analytics Courses
Data analytics and data science are popular terms, and skills in these areas are in great demand. But what do these terms mean? Below is an overview and a listing of related courses. For information about our certificate programs in data science and analytics, click here. →Test Yourself Take a 10-question quiz on analytics Data PrepContinue reading “Data Analytics Courses”
Statistical Thinking
Gambler’s Fallacy I – forgetting that the “coin has no memory” Gamblers often believe that after a long streak of one outcome, the probability of a different outcome has increased. Sports commentators often say that a batter in a slump is “due” for a hit. Psychologically, they think that an outcome opposite to the streakContinue reading “Statistical Thinking”
Latin hypercube
In Monte Carlo sampling for simulation problems, random values are generated from a probability distribution deemed appropriate for a given scenario (uniform, poisson, exponential, etc.). In simple random sampling, each potential random value within the probability distribution has an equal value of being selected. Just due to the vagaries of random chance, clusters of similarContinue reading “Latin hypercube”
Oct 14: Statistics in Practice
This week we look at several ways to fool yourself, statistically – variants of the “Gambler’s Fallacy.” Gambling is all about accurately assessing risk, so, naturally, our featured course is: Nov 15 – Dec 13: Risk Simulation and Queuing See you in class! – Peter Bruce, Chief Academic Officer, Author, Instructor, and Founder The Institute forContinue reading “Oct 14: Statistics in Practice”
Workforce Management
Anyone who has worked in retail knows the anxiety that attends workforce scheduling for both manager and employee. The manager wonders “Will my employees show up at the right times?” The employee wonders “Will I be scheduled for inconvenient times? Enough hours? Too many hours?” The ability of Uber and Lyft to attract drivers, despiteContinue reading “Workforce Management”
Regularize
The art of statistics and data science lies, in part, in taking a real-world problem and converting it into a well-defined quantitative problem amenable to useful solution. At the technical end of things lies regularization. In data science this involves various methods of simplifying models, to minimize overfitting and better reveal underlying phenomena. Some examplesContinue reading “Regularize”
Machine Learning and Human Bias
Does better AI offer the hope of prejudice-free decision-making? Ironically, the reverse might be true, especially with the advent of deep learning. Bias in hiring is one area where private companies move with great care, since there are thickets of laws and regulations in most countries governing bias in employment. The total cost of recruiting,Continue reading “Machine Learning and Human Bias”
Oct 7: Statistics in Practice
This week we take a look at how AI encodes human bias, despite our best efforts. Our spotlight this week is on: Nov 8 – Dec 6: Deep Learning See you in class! – Peter Bruce, Chief Academic Officer, Author, Instructor, and Founder The Institute for Statistics Education at Statistics.com Machine Learning and Human Bias DoesContinue reading “Oct 7: Statistics in Practice”