Coursera

4.45

This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In...

3

·Use the R programming language to work with both structured and unstructured (text) data·Prepare text data for analysis·Train and evaluate supervised learning models on text data·Train and evaluate unsupervised learning models on text dataInterpret...

4.6

Design and analyze experiments where some of the factors are randomDesign and analyze experiments where there are nested factors or hard-to-change factorsAnalyze experiments with covariatesDesign and analyze experiments with nonnormal response distri...

A​nalyzing genome sequences to find similarities and identify target subsequences using predctive models.

4.84

Create a GIS project proposalDevelop a hypothesis for a GIS-based questionComplete a data analysis in line with your project objectivesInterpret and explain the results you obtained in comparison to your original GIS question and/or hypothesis

3.33

In this course, we’ll learn about more advanced machine learning methods that are used to tackle problems in the supply chain. We’ll start with an overview of the different ML paradigms (regression/classification) and where the...

3.82

This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduc...

4.68

Identify appropriate applications of AI and machine learning within a given business situation. Formulate a machine learning approach to solve specific business problems. Select appropriate tools to solve given machine learning problems. Protect data...

4.83

D​escribe different types of data analytic questionsConduct hypothesis tests of your dataA​pply linear modeling techniques to answer multivariable questionsA​pply machine learning workflows to detect complex patterns in your data

4.71

A​pply Tidyverse functions to transform non-tidy data to tidy dataC​onduct basic exploratory data analysisC​onduct analyses of text data

4.63

D​istinguish between various types of plots and their usesU​se the ggplot2 R package to develop data visualizationsB​uild effective data summary tablesB​uild data animations for visual storytelling

3.87

Welcome to the specialization course Business Intelligence and Data Warehousing. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a...

4.36

In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity,...

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....

4.18

This capstone project course for the Recommender Systems Specialization brings together everything you've learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a...

4.59

The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require...

4.54

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure...

4.61

Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data....

4.63

Analyze ethical frameworks to determine their common principles. Compare ethical frameworks and select one to guide decisions to resolve an organizational issue. Analyze applicable regulations, standards, and international best practices. Integra...

4.4

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on...