Managing, Describing, and Analyzing Data

Managing, Describing, and Analyzing Data

This course is part of Data Science Methods for Quality Improvement Specialization

Instructor: Wendy Martin

What you'll learn

Calculate descriptive statistics and create graphical representations using R software
Solve problems and make decisions using probability distributions
Explore the basics of sampling and sampling distributions with respect to statistical inference
Classify types of data with scales of measurement

Skills you'll gain

General Statistics

Details to know

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Assessments

11 assignments

Taught in English

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This course is part of the Data Science Methods for Quality Improvement Specialization

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There are 5 modules in this course

In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Upon completion of this module, students will be able to use R and R Studio to work with data and classify types of data using measurement scales.

What's included

7 videos3 readings2 assignments2 discussion prompts

7 videosTotal 47 minutes

Welcome to Managing, Describing and Analyzing Data1 minutePreview module
Types of Data and Measurement Scales6 minutes
Measurement Scales: Nominal and Ordinal6 minutes
Measurement Scales: Interval, Ratio and Absolute6 minutes
Measurement as a Process, The Big 5 Aspects of Data10 minutes
Sampling Concepts6 minutes
Working in RStudio10 minutes

3 readingsTotal 30 minutes

Earn Academic Credit for your Work!10 minutes
Course Support10 minutes
Attention Learners: R Code / File Resources10 minutes

2 assignmentsTotal 75 minutes

Assessment: Data and Measurement45 minutes
Week 1 Practice Assessment30 minutes

2 discussion promptsTotal 20 minutes

Introduce Yourself!10 minutes
Data and Measurement10 minutes

Upon completion of this module, students will be able to use R and RStudio to create visual representations of data, and calculate descriptive statistics to describe location, spread and shape of data.

What's included

11 videos3 assignments2 discussion prompts

11 videosTotal 84 minutes

Create a Run Chart9 minutesPreview module
Frequency Distributions7 minutes
Frequency Polygons and Histograms7 minutes
Histogram Patterns and Density Plots7 minutes
Box and Whisker Plots7 minutes
Measures of Central Tendency Mean9 minutes
Measures of Central Tendency: Median, Mode7 minutes
Measures of Position8 minutes
Measures of Dispersion 7 minutes
Measures of Shape6 minutes
Measures of Relationship6 minutes

3 assignmentsTotal 180 minutes

Assessment: Describing Data Graphically75 minutes
Assessment: Describing Data Numerically75 minutes
Week 2 Practice Assessment30 minutes

2 discussion promptsTotal 20 minutes

Describing Data Graphically10 minutes
Describing Data Numerically10 minutes

Upon completion of this module, students will be able to apply the rules and conditions of probability and probability distributions to make decisions and solve problems using R and R Studio.

What's included

8 videos2 assignments1 discussion prompt

8 videosTotal 69 minutes

Introduction to Probability Part 16 minutesPreview module
Introduction to Probability Part 28 minutes
Probability Distributions Part 15 minutes
Probability Distributions Part 27 minutes
The Binomial Distribution10 minutes
The Poisson Distribution8 minutes
The Normal Distribution12 minutes
The Exponential Distribution9 minutes

2 assignmentsTotal 150 minutes

Probability and Probability Distributions120 minutes
Week 3 Practice Assessment30 minutes

1 discussion promptTotal 10 minutes

Probability and Probability Distributions10 minutes

Upon completion of this module, students will be able to use R and RStudio to characterize sampling and sampling distributions, error and estimation with respect to statistical inference.

What's included

8 videos2 assignments1 discussion prompt

8 videosTotal 54 minutes

Sampling Error7 minutesPreview module
Random Sampling Distributions8 minutes
The Central Theorem5 minutes
Probability with RSDs7 minutes
Estimates and Estimators6 minutes
Confidence Intervals4 minutes
Confidence Intervals for the Mean and Variance10 minutes
Confidence Intervals for Proportions and Poisson Counts4 minutes

2 assignmentsTotal 120 minutes

Sampling Distributions, Error and Estimation90 minutes
Week 4 Practice Assessment30 minutes

1 discussion promptTotal 10 minutes

Sampling Distributions, Error and Estimation10 minutes

Upon completion of this module, students will be able to use R and RStudio to perform statistical tests for two groups with independent and dependent data.