Data science is one of today’s fastest-growing fields. Become a Data Scientist in 2016 with Coursera.



E.g., 2016-09-30
E.g., 2016-09-30
E.g., 2016-09-30
Oct 4th 2016

This MOOC offers a flexible, collaborative introduction to learning analytics in higher education. You’ll learn by doing, using realistic data and code. Everyone involved in higher education has questions. Students want to know how they’re doing and which classes they should take.

No votes yet
Oct 3rd 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.

Average: 6.3 (9 votes)
Oct 3rd 2016

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

Average: 1 (1 vote)
Oct 3rd 2016

Smartphones are one of the most influential devices that we use in our everyday lives. Smartphones consist of the most advanced hardware and software technologies that exist in the world, all combined together into a miraculous single easy-to-use portable system.

Average: 3 (1 vote)
Oct 3rd 2016

You have most likely heard about Clouds and Big Data before, and already know how significantly important they are and will be in the future.

Average: 2 (4 votes)
Oct 3rd 2016

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: 8 (2 votes)
Oct 3rd 2016

This course covers the analysis of Functional Magnetic Resonance Imaging (fMRI) data. It is a continuation of the course “Principles of fMRI, Part 1”

Average: 9 (1 vote)
Oct 3rd 2016

Este curso ofrece una introducción al uso de R y Rstudio en el tratamiento de datos y la edición de informes estadísticos. El nivel de esta introducción es el adecuado para cubrir las necesidades de los estudiantes de primeros cursos de grado e ingeniería en esta materia, y sirve para cualquier persona que necesite realizar y redactar informes de análisis descriptivos de datos.

No votes yet
Oct 3rd 2016

In this first course of the specialization, you will discover just what data visualization is, and how we can use it to better see and understand data. Using Tableau, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer.

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Oct 3rd 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.

Average: 8.5 (4 votes)
Oct 3rd 2016

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: 2.9 (8 votes)
Oct 3rd 2016

Pragmatic randomized controlled trials reliably work out which of several healthcare interventions works best under real-world conditions.

No votes yet
Oct 3rd 2016

Learn how to apply selected mathematical modelling methods to analyse big data in this free online course. Have you ever wondered how mathematics can be used to solve big data problems? This course will show you how. Mathematics is everywhere, and with the rise of big data it becomes a useful tool when extracting information and analysing large datasets.

Average: 5.7 (10 votes)
Oct 3rd 2016

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration.

No votes yet
Oct 3rd 2016

Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions.

Average: 7.7 (7 votes)
Oct 3rd 2016

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Average: 5 (6 votes)
Oct 3rd 2016

Understand how randomized evaluations can be used to evaluate social and development programs. Learn why evaluations matter and how they can be used to rigorously measure the social impact of development programs. This practical course will provide a thorough understanding of randomized evaluations, with pragmatic step-by-step training for conducting one’s own evaluation.

No votes yet
Oct 3rd 2016

This course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. The course on operations analytics, taught by three of Wharton’s leading experts, focuses on how the data can be used to profitably match supply with demand in various business settings. In this course, you will learn how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk. The course will introduce frameworks and ideas that provide insights into a spectrum of real-world business challenges, will teach you methods and software available for tackling these challenges quantitatively as well as the issues involved in gathering the relevant data.

Average: 10 (2 votes)
Oct 3rd 2016

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

No votes yet
Oct 3rd 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.

Average: 7.2 (5 votes)

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