Coursera

5

In this course, you will learn how Azure Synapse Analytics enables you to perform different types of analytics through its’ components that can be used to build Modern Data Warehouses through to advanced analytical solutions....

4.82

Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today....

4.43

Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies...

4.59

Neurohacking describes how to use the R programming language (https: //cran. r-project. org/) and its associated package to perform manipulation, processing, and analysis of neuroimaging data. We focus on publicly-available structural magnetic reson...

3.91

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.

4.19

This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data...

4.39

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate...

4.5

How to use Azure Synapse Analytics to build Data Warehouses using modern architecture patterns.

4.4

Define a composite hypothesis and the level of significance for a test with a composite null hypothesis. Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form...

4.5

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied...

4.48

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets....

4.75

Case Study - Predicting Housing PricesIn 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,. . ....

4.65

Represent and manipulate networked data using the NetworkX libraryAnalyze the connectivity of a networkMeasure the importance or centrality of a node in a networkPredict the evolution of networks over time

3.33

This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical...

4.63

This course has a singular and clear aim, to empower you to do statistical tests, ready for incorporation into your dissertations, research papers, and presentations. The ability to summarize data, create plots and charts, and...

3.88

Apply foundational data science concepts and techniques to solve these real-world problems. Design, execute, assess, and communicate the results of your very own data science projects.

4.9

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...

4.32

What is data scienceHow data science, machine learning, and data-driven innovation can benefit business outcomesFoundational concepts and intuitions about machine learning techniques

4.72

Understand the role of predictive analytics for prevention, diagnosis, and effectivenessExplain key concepts in prediction modelling: appropriate study design, adequate sample size and overfittingUnderstand important issues in model development, suc...

4

This course is intended to introduce students to CyberGIS—Geospatial Information Science and Systems (GIS)—based on advanced cyberinfrastructure as well as the state of the art in high-performance computing, big data, and cloud computing in the...