Statistics

 

 


 

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E.g., 2017-09-25
E.g., 2017-09-25
E.g., 2017-09-25
Oct 9th 2017

Discover practical data mining and learn to mine your own data using the popular Weka workbench. Today’s world generates more data than ever before! Being able to turn it into useful information is a key skill. This course introduces you to practical data mining using the Weka workbench.

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Oct 2nd 2017

This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm.

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Oct 2nd 2017

This course is a hands-on introduction to statistical data analysis that emphasises fundamental concepts and practical skills.

Average: 5.7 (3 votes)

Oct 2nd 2017

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

Average: 7.1 (16 votes)
Oct 2nd 2017

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.

Average: 6.9 (13 votes)
Oct 2nd 2017

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Average: 7.1 (7 votes)

Oct 2nd 2017

Financial Engineering is a multidisciplinary field involving finance and economics, mathematics, statistics, engineering and computational methods. The emphasis of FE & RM Part II will be on the use of simple stochastic models to (i) solve portfolio optimization problems (ii) price derivative securities in various asset classes including equities and credit and (iii) consider some advanced applications of financial engineering including algorithmic trading and the pricing of real options. We will also consider the role that financial engineering played during the financial crisis.

Average: 8.9 (9 votes)
Oct 2nd 2017

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Average: 7.7 (7 votes)
Oct 2nd 2017

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

Average: 4 (14 votes)
Self Paced

Gain a solid understanding of statistics and basic probability, using Excel, and build on your data analysis and data science foundation. If you’re considering a career as a data analyst, you need to know about histograms, Pareto charts, Boxplots, Bayes’ theorem, and much more. In this applied statistics course, the second in our Microsoft Excel Data Analyst XSeries, use the powerful tools built into Excel, and explore the core principles of statistics and basic probability—from both the conceptual and applied perspectives.

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Oct 1st 2017

Learn how to statistically analyse process data to determine the root cause for process problems and propose solutions and to implement quality management tools, such as FMEA, 8D and the 5 Whys. Learn how to analyse data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.

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Sep 26th 2017

Learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest. This statistics and data analysis course will introduce you to the essential notions of probability and statistics.

Average: 2 (3 votes)