“The goal is to turn data into information, and information into insight.”
“Data is the new science. Big data holds the answers.”
“Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”
“You can have data without information, but you cannot have information without data.”
“Information is the oil of the 21st century, and analytics is the combustion engine.”
I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding.”
"Data scientist is just a sexed up word for statistician."
"Data scientists are statisticians because being a statistician is awesome and anyone who does cool things with data is a statistician."
"Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others."
“Think analytically, rigorously, and systematically about a business problem and come up with a solution that leverages the available data.”
"By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights."
"A data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product."
"A significant constraint on realizing value from Big Data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning, and the managers and analysts who know how to operate companies by using insights from Big Data."
"We project a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of Big Data effectively."
"The data scientist was called, only half-jokingly, 'a caped superhero.'"
"By 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge."
"Autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives."
“Statistics are ubiquitous in life, and so should be statistical reasoning.”
"My organization is slowly embracing predictive analytics outside of our environmental concerns, so this course contributes directly to strengthening my R toolkit!"
"More and more medical students are submitting systematic reviews and associated meta-analyses, to meet research exposure requirements, in the Basic Sciences Programs of their medical school education, here in the Caribbean. The course provided me with a unique and much appreciated opportunity to learn much more about the technique, its underlying principles, and how to correctly apply the tool whenever evaluating a collection of primary studies. Time and energies committed to the course have been very well spent."
"After trying to learn meta-analysis from various texts (e.g., Hunter et al., 2004; Cooper et al., 2009 and others), I found the Borenstein text and CMA software to be *extremely* user-friendly. It made the selection of fixed-effect or random-effects models much easier to understand, and the presentation of the math was very straightforward. If I could write a book, I would want to do it this way. Each chapter was short and to the point. Not a lot of extras, but a sufficient number of worked examples to make it work. The software is very easy to use, and I appreciate the continual improvement that seems to be made. You and your colleagues have done something incredible here."
"It will help me gain confidence with conducting data analysis using R. I have tried a few R MOOCs in the past which helped me start out using R but I had not found a comprehensive intro to R that I could use more broadly - this course really succeeds with accomplishing that."
"This course has given me a much better understanding of biostatistics and will allow me to gain more from my campus based studies,"
"This course supplied a solid foundation for identifying statistical outliers in demand patterns for business planning purposes. In addition, it has deepened my understanding of statistical safety stock calculations & inventory level decision making."
This course fundamentally deepened my understanding of statistics and studies. I will certainly apply the knowledge gained as often as possible. Absolutely vital course for understanding basic epidemiology, studies and statistics. THANK YOU. I would recommend that everyone get a copy of the textbook before class though. It complements the ActivEpi CD extremely well.
This course will greatly contribute to my work as environmental data scientist and division director. This course was a great introduction of how to use R to fit the models and how to interpret the R output!
Great explanations provided by the course instructor in the videos and on the discussion board. The Assistant Teacher was very diligent and professional in reaching out to me and giving me every opportunity to be successful in this course! Definitely a wonderful experience!!!
Using information gleaned from the course concerning a not-well-known distance measure (Gower dissimilarity) useful for cluster models that combine data from mixed variables, including nominal, ordinal, and numeric variables, I was able to refine a model I was working on for an academic conference paper. More broadly, the subject of identifying anomalies is a central task of academic historians, and this course allowed me the welcome luxury to reflect on their nature and methods of discovery.
I am now confident that I can use R and continue learning on my own. The course also helped me to explore other resources within the R community online. I'm looking forward to using R in data analysis for my masters thesis and beyond.