Coursera

4.39

Breast Imaging; AI Processing Paradigm; Performance Assessment Metrics

4.72

This online course is an introduction into formal concept analysis (FCA), a mathematical theory oriented at applications in knowledge representation, knowledge acquisition, data analysis and visualization. It provides tools for understanding the dat...

Create charts to describe and compare the composition of data setsIllustrate the distribution of data through visualizationsGenerate visualizations for specialized data (e. g. geographical, three dimensional, etc)

4.58

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing...

4.37

Calculate descriptive statistics and create graphical representations using R softwareSolve problems and make decisions using probability distributionsExplore the basics of sampling and sampling distributions with respect to statistical inferenceClas...

4.28

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making - we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the...

4.42

Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data...

4.5

In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the...

4.76

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

4.64

The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to...

3.42

Recognize and distinguish the difference in complexity and sophistication of text mining, text processing, and natural language processing. Write basic regular expressions to identify common clinical text. Assess and select note sections that can be...

4.86

Ask better questions in empirical researchDesign more informative studiesEvaluate the scientific literature taking bias into accountReflect on current norms, and how you can improve your research practices

4.26

You'll be able to build a basic recommender system. You'll be able to choose the family of recommender systems that best suits the kind of input data, goals and needs. You'll learn how to identify...

4.58

This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables. We’ll begin by exploring the components of a bivariate...

3.56

Project structure of interactive Python data applicationsPython web server frameworks: (e. g. ) Flask, Django, DashBest practices around deploying ML models and monitoring performanceDeployment scripts, serializing models, APIs

4.16

Describe a numerical data set using statistical functions in RImport and manipulate data sets using TidyverseDetermine if populations are different using statistical testsUse statistical tests to describe or explain the relationship between data sets

4.59

Knows (the value of) all aspects of data management and acknowledge the importance of initial data analysis. Knows the pros and cons of statistical methods and can choose the appropriate data analysis approach in common...

Understand how to use, select, and interpret process control charts to identify special causes of variationCreate and interpret control charts for normal and non-normal distributionsCreate and interpret control charts for discrete dataAnalyze the cap...

4.81

Participate in the deployment of machine learningIdentify potential machine learning deployments that will generate value for your organizationReport on the predictive performance of machine learning and the profit it generatesUnderstand the potentia...

4.86

Apply ML: Identify opportunities where machine learning can improve marketing, sales, financial credit scoring, insurance, fraud detection, and morePlan ML: Determine the way machine learning will be operationally integrated and deployed, and the s...