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This course is provided by UN System Staff College (UNSSC) for a FEE. 

Duration: 01-30 August 2015 

Course Objective

The effective use of data for public policy is of critical importance to the UN in its efforts to strengthen evidence-based programming and policy development. Generating, analysing, presenting and using data is of growing importance in our efforts to support Member States in this regard. All countries need to get "data and statistics ready" for Post-2015.

The new UN inter-agency course will be developed with a view to strengthen the skills of UN staff in selecting, creating, using  and interpreting  data and statistics. This will be done with a particular focus on public policy-making and implementation, and build on both traditional ways of data analytics as well as more recent applications related to for instance mobile technology, crowdsourcing and big data.

This is an online course provided by John Hopkins University through Coursera for FREE. 

Duration: 4 weeks, 1-31 December 2014

Course Objective

This specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. The Specialization concludes with a Capstone project that allows you to apply the skills you have learned throughout the courses. 

There 10 parts in this course: 

1. The Data Scientist's Toolbox

2. R Programming

3. Getting and Cleaning Data

4. Exploratory Data Analysis

5. Reproducible Research

6. Statistical Inference

7. Regression Models

8. Practical Machine Learning

9. Developing Data Products

10. Data Science Capstone

 with a Capstone project that allows you to apply the skills you've learned throughout the courses.

This course is offered by the UN System Staff College (UNSSC) for a FEE. 

Duration: To be announced. Expected period is FIRST QUARTER of 2015. 

Course description

The effective use of data for public policy is of critical importance to the UN in its efforts to strengthen evidence-based programming and policy development. Generating, analysing, presenting and using data is of growing importance in our efforts to support Member States in this regard. All countries need to get “data and statistics ready” for Post-2015.

The new UN inter-agency course will be developed with a view to strengthen the skills of UN staff in selecting, creating, using  and interpreting  data and statistics. This will be done with a particular focus on public policy-making and implementation, and build on both traditional ways of data analytics as well as more recent applications related to for instance mobile technology, crowdsourcing and big data.

 

This online course is provided by University of Michigan through Coursera for FREE. 

Duration: 2 February to 31 March 2015

Course Objective

This online course is called “Questionnaire Design for Social Surveys” and is based on a course created as part of the Joint Program in Survey Methodology and the Michigan Program in Survey Methodology at ISR. The original course - a core course in our MS program - is one of our most popular courses. It is offered every semester, and in every semester the course is over enrolled. Students with backgrounds in Journalism, Public Health, Criminology, Marketing, Communication, Sociology, Psychology, and Political Science are part of our regular audience.

Why does the course have such broad appeal? Because questionnaires are everywhere.  For instance, government agencies use questionnaires to measure the health of their nations, their economic wellbeing, and myriad other aspects of life to inform policy decisions. Nongovernmental organizations use questionnaires to measure their customers’ or members’ satisfaction, and pollsters use questionnaires to measure political attitudes and voting intentions. 

But designing questions that get good answers is harder than it looks. Indeed, there is now a large scientific literature dealing with how to design good questions. Thiscourse will cover the stages of questionnaire design: developmental interviewing, question writing, question evaluation, pretesting, and questionnaire ordering and formatting. It reviews the literature on questionnaire construction, the experimental literature on question effects, and the psychological literature on information processing. In addition, this course reviews the effects of essential design features on questions and questionnaires. Students will critique existing questions and questionnaires as part of the course.

The target audiences for this course are students and professionals from all fields of social science that are involved in primary data collection. These can be professionals at government agencies, such as the U.S. Census Bureau, but also professionals at market research agencies, political polling organizations and any organization interested in surveying their customers or members.

This online course is provided California Institute of Technology through eDx for FREE. 

Duration: 10 weeks, starts on 25 September 2014 

Course Objective

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:

  • What is learning?
  • Can a machine learn?
  • How to do it?
  • How to do it well?
  • Take-home lessons.

This online course is provided by University of California Berkeley through eDx for FREE. 

Duration: 5 weeks, course has ended but course archive is available at eDx

Course Description

Statistics 2 at Berkeley is an introductory class taken by about 1000 students each year. Stat2.2x is the second of three five-week courses that make up Stat2x, the online equivalent of Berkeley's Stat 2.

The focus of Stat2.2x is on probability theory: exactly what is a random sample, and how does randomness work? If you buy 10 lottery tickets instead of 1, does your chance of winning go up by a factor of 10? What is the law of averages? How can polls make accurate predictions based on data from small fractions of the population? What should you expect to happen "just by chance"? These are some of the questions we will address in the course.

We will start with exact calculations of chances when the experiments are small enough that exact calculations are feasible and interesting. Then we will step back from all the details and try to identify features of large random samples that will help us approximate probabilities that are hard to compute exactly. We will study sums and averages of large random samples, discuss the factors that affect their accuracy, and use the normal approximation for their probability distributions.

Be warned: by the end of Stat2.2x you will not want to gamble. Ever. (Unless you're really good at counting cards, in which case you could try blackjack, but perhaps after taking all these edX courses you'll find other ways of earning money.)

As Stat2.2x is part of a series, the basic prerequisites are the same as those for Stat2.1x: high school arithmetic and good comprehension of English. In addition, you are expected to know the material of Stat2.1x, with particular emphasis on histograms, averages, SDs, and the normal curve. The fundamental approach of the series was provided in the description of Stat2.1x and appears here again: There will be no mindless memorization of formulas and methods. Throughout the course, the emphasis will be on understanding the reasoning behind the calculations, the assumptions under which they are valid, and the correct interpretation of results.

This online course is provided by John Hopkins University through Coursera for FREE. 

Duration: 4 weeks, 1-29 September 2014

Course Objective

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.

 

This online course is provided by Stanford University through Coursera for FREE. 

Duration: 9 weeks, 21 September to 23 November 2014

Social networks pervade our social and economic lives.   They play a central role in the transmission of information about job opportunities and are critical to the trade of many goods and services. They are important in determining which products we buy, which languages we speak, how we vote, as well as whether or not we decide to become criminals, how much education we obtain, and our likelihood of succeeding professionally.   The countless ways in which network structures affect our well-being make it critical to understand how social network structures impact behavior, which network structures are likely to emerge in a society, and why we organize ourselves as we do.  This course provides an overview and synthesis of research on social and economic networks, drawing on studies by sociologists, economists, computer scientists, physicists, and mathematicians.

The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks.   Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids.   We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.

This online course is provided by University of Toronto through Coursera for FREE. 

Duration: 47 weeks, future sessions TBA but course archive is available at Coursera

Course Objective

We live in a world where data are increasingly available, in ever larger quantities, and are increasingly expected to form the basis for decisions by governments, businesses, and other organizations, as well as by individuals in their daily lives. To cope effectively, every informed citizen must be statistically literate.  

This course will provide an intuitive introduction to applied statistical reasoning,  introducing fundamental statistical skills and acquainting students with the full process of inquiry and evaluation used in investigations in a wide range of fields.  In particular, the course will cover methods of data collection, constructing effective graphical and numerical displays to understand the data, how to estimate and describe the error in estimates of some important quantities, and the key ideas in how statistical tests can be used to separate significant differences from those that are only a reflection of the natural variability in data.

This online course is provided by Princeton University through Coursera for FREE. 

Duration: 12 weeks, future sessions TBA but course archive is available at Coursera 

Course Objective

Statistics One is designed to be a comprehensive yet friendly introduction to fundamental concepts in statistics. Comprehensive means that this course provides a solid foundation for students planning to pursue more advanced courses in statistics. Friendly means exactly that. The course assumes very little background knowledge in statistics and introduces new concepts with several fun and easy to understand examples. 

This course is, quite literally, for everyone. If you think you can't learn statistics, this course is for you. If you had a statistics course before but feel like you need a refresher, this course is for you. Even if you are a relatively advanced researcher or analyst, this course provides a foundation and a context that helps to put one’s work into perspective.

Statistics One also provides an introduction to the R programming language. All the examples and assignments will involve writing code in R and interpreting R output. R software is free! What this means is you can download R, take this course, and start programming in R after just a few lectures. That said, this course is not a comprehensive guide to R or to programming in general. 

This online course is provided by John Hopkins University through Coursera for FREE. 

Duration: 4 weeks, 1-29 September 2014

Course Objective

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. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

This is an online course provided by University of Washington through Coursera for FREE. 

Duration: 10 weeks, 9 December to 17 February 2015

Course Objective 

Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression.

This is an online course provided by University of California Berkeley through eDx for FREE. 

Duration:  5 weeks, course has ended but the course archive is availabe at eDx

This is a past/archived course. At this time, you can only explore this course in a self-paced fashion. Certain features of this course may not be active, but many people enjoy watching the videos and working with the materials. Make sure to check for reruns of this course.

We are surrounded by information, much of it numerical, and it is important to know how to make sense of it. Stat2x is an introduction to the fundamental concepts and methods of statistics, the science of drawing conclusions from data.

The course is the online equivalent of Statistics 2, a 15-week introductory course taken in Berkeley by about 1,000 students each year. Stat2x is divided into three 5-week components. Stat2.1x is the first of the three.

The focus of Stat2.1x is on descriptive statistics. The goal of descriptive statistics is to summarize and present numerical information in a manner that is illuminating and useful. The course will cover graphical as well as numerical summaries of data, starting with a single variable and progressing to the relation between two variables. Methods will be illustrated with data from a variety of areas in the sciences and humanities.

There will be no mindless memorization of formulas and methods. Throughout Stat2.1x, the emphasis will be on understanding the reasoning behind the calculations, the assumptions under which they are valid, and the correct interpretation of results.

This is a course offered by Duke University through Coursera for FREE. 

Duration: 01 September to 10 November 2014

Course Description

The goals of this course are as follows:

  1. Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
  2. Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
  3. Have a conceptual understanding of the unified nature of statistical inference.
  4. Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions.
  5. Model and investigate relationships between two or more variables within a regression framework.
  6. Interpret results correctly, effectively, and in context without relying on statistical jargon.
  7. Critique data-based claims and evaluate data-based decisions.
  8. Complete a research project that employs simple statistical inference and modeling techniques.

 

This online course is provided by Higher School of Economics, National Research University through Coursera for FREE. 

Duration: TBA

Course Objective

This is an unconventional course in modern Data Analysis, Machine Learning and Data Mining. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. The term summarization embraces here both simple summaries like totals and means and more complex summaries: the principal components of a set of features and cluster structures in a set of entities. Similarly, correlation covers both bivariate and multivariate relations between input and target features including Bayes classifiers.

The view of the data as a subject of computational data analysis that is adhered to here has emerged quite recently. Typically, in sciences and in statistics, a problem comes first, and then the investigator turns to data that might be useful in advancing towards a solution. Yet nowadays the situation is reversed frequently, especially with the advent of Big Data. Typical questions then are: Take a look at this data set - what sense can be made out of it? – Is there any structure in the data set? Can these features help in predicting those? This is more reminiscent to a traveler’s view of the world rather than that of a scientist. The scientist sits at his desk, gets reproducible signals from the universe and tries to accommodate them into a great model of the universe. The traveler deals with what come on their way – here is the data analysis niche.  A textbook by the instructor along these lines has been published by Springer-London in 2011: “Core concepts in data analysis is clean and devoid of any fuzziness. The author presents his theses with a refreshing clarity seldom seen in a text of this sophistication. … To single out just one of the text’s many successes: I doubt readers will ever encounter again such a detailed and excellent treatment of correlation concepts. (Computing Reviews of ACM, June 2011).”

 

This online course is provided by John Hopkins University through Coursera for FREE. 

Duration: 4 weeks, future sessions TBA but course archive available at Coursera

Course Objective

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.

 

This online course is provided by John Hopkins University through Coursera for FREE. 

Duration: 4 weeks, 1-29 September 2014

Course Objective 

Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

 

This course is provided by John Hopkins University through Coursera for FREE. 

Duration: 8 weeks, future sessions TBA but course archive is available at Coursera

Course Objective

This is an unconventional course in modern Data Analysis, Machine Learning and Data Mining. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. The term summarization embraces here both simple summaries like totals and means and more complex summaries: the principal components of a set of features and cluster structures in a set of entities. Similarly, correlation covers both bivariate and multivariate relations between input and target features including Bayes classifiers.

The view of the data as a subject of computational data analysis that is adhered to here has emerged quite recently. Typically, in sciences and in statistics, a problem comes first, and then the investigator turns to data that might be useful in advancing towards a solution. Yet nowadays the situation is reversed frequently, especially with the advent of Big Data. Typical questions then are: Take a look at this data set - what sense can be made out of it? – Is there any structure in the data set? Can these features help in predicting those? This is more reminiscent to a traveler’s view of the world rather than that of a scientist. The scientist sits at his desk, gets reproducible signals from the universe and tries to accommodate them into a great model of the universe. The traveler deals with what come on their way – here is the data analysis niche.  A textbook by the instructor along these lines has been published by Springer-London in 2011: “Core concepts in data analysis is clean and devoid of any fuzziness. The author presents his theses with a refreshing clarity seldom seen in a text of this sophistication. … To single out just one of the text’s many successes: I doubt readers will ever encounter again such a detailed and excellent treatment of correlation concepts. (Computing Reviews of ACM, June 2011).”

 

This is an online course offered by Karolinska Institutet through eDx for FREE.

Duration: 5 weeks, starts on 9 September 2014

Course Objective

Do you want to learn how to harvest health science data from the internet? Do you want to understand the world through data analysis? Start by exploring statistics with R!

Skilled persons who can process and analyze data are in great demand today. In this course you will explore concepts in statistics that help you make sense out of data. You will learn the practical skills necessary to find, import, analyze and visualize data. We will take a look under the hood of statistics and equip you with broad tools for understanding statistical inference and statistical methods. You will also get to perform some really complicated calculations and visualizations, following in the footsteps of Karolinska Institutet’s researchers.

You are probably reading this course description because you know that statistics and statistical programming are essential skills in our golden age of data abundance. Let’s say it again: Health science has become a field of big data, just like so many other fields of study. New techniques make it possible and affordable to generate massive data sets in biology. Researchers and clinicians can measure the activity for each of 30000 genes of a patient. They can read the complete genome sequence of a patient. Thanks to another trend of the decade, open access publishing, the results of such large scale health science are very often published for you to read free of charge. You can even access the raw data from open databases such as the gene expression database of the NCBI, National Center for Biotechnology Information.

In this course you will learn the basics of R, a powerful open source statistical programming language. Why has R become the tool of choice in bioinformatics, the health sciences and many other fields? One reason is surely that it’s powerful and that you can download it for free right now. But more importantly, it’s supported by an active user community. In this course you will learn how to use peer reviewed packages for solving problems at the frontline of health science research. Commercial actors just can’t keep up implementing the latest algorithms and methods. When algorithms are first published, they are already implemented in R. Join us in a gold digging expedition. Explore statistics with R.

 

This online course is provided by John Hopkins University through Coursera for FREE. 

Duration: 4 weeks, 1-29 September 2014

Course Objective

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.

 

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