# Search Results for “Probability and Statistics”

1. ## Practicing Statistics Interview Questions in R

Description: In this course, you’ll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Learning Outcome: Probability Distributions, Exploratory Data Analysis, Statistical Tests, Regression Models
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

2. ## Introduction to Statistics in R

Description: In this course, you’ll use sales data to discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You’ll also tackle probability, the backbone of statistical reasoning, and learn how to conduct a well-designed study to draw your own conclusions from data.
Learning Outcome: Summary Statistics, Random Numbers and Probability, More Distributions and the Central Limit Theorem, Correlation and Experimental Design
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

3. ## Survey and Measurement Development in R

Description: In this course, you’ll learn how to design and analyze a marketing survey to describe and even predict customers’ behavior based on how they rate items on “a scale of 1 to 5.” You’ll wrangle survey data, conduct exploratory & confirmatory factor analyses, and conduct various survey diagnostics such as checking for reliability and validity.
Learning Outcome: Preparing to analyze survey data, Exploratory factor analysis & survey development, Confirmatory factor analysis & construct validation, Criterion validity & replication
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

4. ## Designing and Analyzing Clinical Trials in R

Description: This course would be valuable for data analysts, medical students, clinicians, medical researchers and others interested in learning about the design and analysis of clinical trials.
Learning Outcome: Principles, Trial Designs, Sample Size and Power, Statistical Analysis
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

5. ## Probability Puzzles in R

Description: This course will help get you there, using problem-based learning with probability puzzles as the framework. As you are guided through their solutions, you will gain coding tools and general strategies for solving probability problems that you might encounter in many other situations. Organized by theme, the course begins with classic problems like the Birthday Problem and Monty Hall, and ends with puzzles that involve poker like Texas Hold’em and the World Series of Poker!
Learning Outcome: Introduction and Classic Puzzles, Games with Dice, Inspired from the Web, Poker
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

6. ## Forecasting Product Demand in R

Description: This course unlocks the process of predicting product demand through the use of R. You will learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example. By the end of the course you will be able to predict demand for multiple products across a region of a state in the US. Then you will roll up these predictions across many different regions of the same state to form a complete hierarchical forecasting system.
Learning Outcome: Forecasting demand with time series, Components of demand, Blending regression with time series, Hierarchical forecasting
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

7. ## Hierarchical and Mixed Effects Models in R

Description: This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You’ll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals.
Learning Outcome: Overview and introduction to hierarchical and mixed models, Linear mixed-effect models, Generalized linear mixed-effect models, Repeated Measures
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

8. ## Data Privacy and Anonymization in R

Description: In this course, you will learn to code basic data privacy methods and a differentially private algorithm based on various differentially private properties. With these tools in hand, you will learn how to generate a basic synthetic (fake) data set with the differential privacy guarantee for public data release.
Learning Outcome: Introduction to Data Privacy, Introduction to Differential Privacy, Differentially Private Properties, Differentially Private Data Synthesis
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

9. ## Multivariate Probability Distributions in R

Description: In this course, you’ll learn ways to analyze these datasets. You will also learn about common multivariate probability distributions, including the multivariate normal, the multivariate-t, and some multivariate skew distributions. You will then be introduced to techniques for representing high dimensional data in fewer dimensions, including principal component analysis (PCA) and multidimensional scaling (MDS).
Learning Outcome: Reading and plotting multivariate data, Multivariate Normal Distribution, Other Multivariate Distributions, Principal Component Analysis and Multidimensional Scaling
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid

10. ## Spatial Analysis with sf and raster in R

Description: In this course you will learn why the sf package is rapidly taking over spatial analysis in R. You will read in spatial data, manipulate vectors using the dplyr package and learn how to work with coordinate reference systems. You’ll also learn how to perform geoprocessing of vectors including buffering, spatial joins, computing intersections, simplifying and measuring distance. With rasters you will aggregate, reclassify, crop, mask and extract. The last chapter of the course is devoted to showing you how to make maps in R with the ggplot2 and tmap packages and performing a fun mini-analysis that brings together all your new skills.
Learning Outcome: Vector and Raster Spatial Data in R, Preparing layers for spatial analysis, Conducting spatial analysis with the sf and raster packages, Combine your new skills into a mini-analysis
Platform: Datacamp
Course Level: Newbie
Duration: 4 hours
Domain: Probability & Statistics
Paid/Free: Paid