Coursera

4.16

Estimating ML models financial impact for binary classification modelsDesign and evaluation of A/B testsReject inference

4.95

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....

3.5

Despite a large variety of different courses on analytics, the courses that offer a broad overview of the field are rare. From practice of teaching statistics, it became clear that it is difficult for learners...

4.55

The number of composite indices that are constructed and used internationally is growing very fast; but whilst the complexity of quantitative techniques has increased dramatically, the education and training in this area has been dragging...

4.73

This course aims to introduce learners to advanced visualization techniques beyond the basic charts covered in Information Visualization: Fundamentals. These techniques are organized around data types to cover advance methods for: temporal and spa...

4.76

This course provides a framework for how analysts can create and evaluate quantitative measures. Consider the many tricky concepts that are often of interest to analysts, such as health, educational attainment and trust in government....

Identify the key components of and propose a real-world data mining project. Design and develop real-world solutions across the full data mining pipeline. Summarize and present the key findings of the data mining project. Analyze...

4.75

The Capstone project will allow you to continue to apply and refine the data analytic techniques learned from the previous courses in the Specialization to address an important issue in society. You will use real...

5

Train and evaluate decision trees and random forests for regression and classification. Train and evaluate support-vector machines (SVM) for regression and classification. Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN...

3.82

Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start?...

4.75

Welcome to Data Analytics Foundations for Accountancy II! I'm excited to have you in the class and look forward to your contributions to the learning community. To begin, I recommend taking a few minutes to...

3.92

This course provides an unique opportunity for you to learn key components of text mining and analytics aided by the real world datasets and the text mining toolkit written in Java. Hands-on experience in core...

4.91

This course prepares you to deal with advanced clinical data science topics and techniques including temporal and research quality analysis.

4.14

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in...

5

This course is designed for business and data professional seeking to learn the first technical phase of the data science process known as Extract, Transform and Load or ETL. Learners will be taught how to...

4.56

This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze...

4.36

Understand the definitions of simple error measures (e. g. MSE, accuracy, precision/recall). Evaluate the performance of regressors / classifiers using the above measures. Understand the difference between training/testing performance, and generaliz...

4.4

Build an investment factor model using regression methodology Employ optimization algorithm using R standard libraryExplain the portfolio performance

5

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that...

4.61

SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services...