Coursera

4.6

Determine assumptions needed to calculate confidence intervals for their respective population parameters. Create confidence intervals in Python and interpret the results. Review how inferential procedures are applied and interpreted step by step whe...

4.58

An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data, to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences,...

4.59

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations s...

4.29

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next,...

4.78

Welcome to this course on Customising your models with TensorFlow 2! In this course you will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning models and workflows for...

4.76

In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of...

4.52

The goal of this online course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models...

4.67

Understand analytic graphics and the base plotting system in RUse advanced graphing systems such as the Lattice systemMake graphical displays of very high dimensional dataApply cluster analysis techniques to locate patterns in data

4.09

Identify characteristics of “good” estimators and be able to compare competing estimators. Construct sound estimators using the techniques of maximum likelihood and method of moments estimation. Construct and interpret confidence intervals for one an...

5

Create dimensions and measures to curate data attributes used by business usersCreate and design Explores to make data accessible to business usersUse derived tables to instantaneously create new tablesUse caching and datagroups in Looker to...

4.69

Examine the history and current challenges faced by Social Science through the digital revolution. Configure a machine to create a database that can be used for analysis. Discuss what is artificial intelligence (AI) and train...

4.46

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summ...

4.48

This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data...

4.54

Understand common data storage systemsApply data cleaning basics to make data "tidy"Use R for text and date manipulationObtain usable data from the web, APIs, and databases

4

In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss...

4.16

This MOOC – a joint initiative between EIT Digital, Université de Nice Sophia-Antipolis / Université Côte d'Azur, and INRIA - introduces the Linked Data standards and principles that provide the foundation of the Semantic web....

4.29

Power and Sample Size for Longitudinal and Multilevel Study Designs, a five-week, fully online course covers innovative, research-based power and sample size methods, and software for multilevel and longitudinal studies. The power and sample size...

4.14

Learner Outcomes: After taking this course, you will be able to: - Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr. - Process...

4.75

Learners will be able to explain what Computer Vision is and give examples of Computer Vision tasks. ​Learners will be able to describe the process behind classic algorithmic solutions to Computer Vision tasks and explain...

4.7

This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from...