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

STARTS

Sep 19th 2016

Taught by:

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.

We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively.

What you'll learn:

- Intuition behind probability and statistical analysis

- How to summarize and describe data

- A basic understanding of various methods of evaluating social programs

- How to present results in a compelling and truthful way

-Skills and tools for using R for data analysis

Oct 3rd 2016

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

Nov 4th 2016

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Oct 24th 2016

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data!

Oct 31st 2016

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.

Oct 31st 2016

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

Oct 31st 2016

This course builds on the theory and foundations of marketing analytics and focuses on practical application by demystifying the use of data in marketing and helping you realize the power of visualizing data with artful use of numbers found in the digital space.

Oct 31st 2016

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Self Paced

In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this free R online course today!

Oct 18th 2016

Use R to learn the fundamental statistical topic of basic inferential statistics. In the second part of a two part course, we’ll learn how to take data and use it to make reasonable and useful conclusions. You’ll learn the basics of statistical thinking – starting with an interesting question and some data.

Oct 10th 2016

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

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Self-Paced MOOCs

MOOC List Coupon Discount

Providers and Categories

University / Entity

Instructor

Country

Language

Type of Certificate

Tag

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- MOOC stands for a Massive Open Online Course.
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- They are similar to university courses, but do not tend to offer academic credit.
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