You will have a chance to try the first week of our course “Predictive Analytics 1 - Machine Learning
Tools.” You will be introduced to the basic concepts in predictive
analytics, also called predictive modeling, the most prevalent form of
data mining. The full course covers the two core paradigms that account for
most business applications of predictive modeling: classification and
prediction.
Try out our introductory statistics series for one week - you can learn stats!
This course covers the basic statistical principles in the design and analysis of randomized controlled trials.
This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.
This course covers sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.
After successfully completing this course, you will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation.
This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.
This course will show you how to use R to create statistical models and use them to analyze data.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
This course continues the work of Predictive Analytics 1, and introduces
you to additional techniques in predictive analytics, also called
predictive modeling, the most prevalent form of data mining.
This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
This course will show you how to use R to create statistical models and use them to analyze data.
This course will show you how to use R to create statistical models and use them to analyze data.
The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software.
To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.
This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.
In this online course, you will learn about the rapidly evolving field
of Deep Learning. At the end of this
course you will understand the basic concepts underlying the
representations and methods in deep learning and see some applications
where deep learning is most effective.
This is an introductory epidemiology course that emphasizes the underlying concepts and methods of epidemiology.