Managing, Describing, and Analyzing Data

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Managing, Describing, and Analyzing Data

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

Instructor: Wendy Martin

6,220 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Build toward a degree

Learn more

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

Tools you'll learn

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

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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 videos4 readings2 assignments2 discussion prompts

7 videosTotal 48 minutes

Welcome to Managing, Describing and Analyzing Data1 minute
Types of Data and Measurement Scales7 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 RStudio11 minutes

4 readingsTotal 31 minutes

Course Updates and Accessibility Support1 minute
Earn Academic Credit for your Work!10 minutes
Course Support10 minutes
Attention Learners: R Code / File Resources10 minutes

2 assignmentsTotal 75 minutes

Week 1 Practice Assessment30 minutes
Assessment: Data and Measurement45 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 85 minutes

Create a Run Chart9 minutes
Frequency Distributions8 minutes
Frequency Polygons and Histograms7 minutes
Histogram Patterns and Density Plots8 minutes
Box and Whisker Plots7 minutes
Measures of Central Tendency Mean10 minutes
Measures of Central Tendency: Median, Mode7 minutes
Measures of Position8 minutes
Measures of Dispersion 8 minutes
Measures of Shape7 minutes
Measures of Relationship7 minutes

3 assignmentsTotal 180 minutes

Week 2 Practice Assessment30 minutes
Assessment: Describing Data Graphically75 minutes
Assessment: Describing Data Numerically75 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 70 minutes

Introduction to Probability Part 17 minutes
Introduction to Probability Part 28 minutes
Probability Distributions Part 15 minutes
Probability Distributions Part 27 minutes
The Binomial Distribution11 minutes
The Poisson Distribution9 minutes
The Normal Distribution13 minutes
The Exponential Distribution10 minutes

2 assignmentsTotal 150 minutes

Week 3 Practice Assessment30 minutes
Probability and Probability Distributions120 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 55 minutes

Sampling Error7 minutes
Random Sampling Distributions8 minutes
The Central Theorem6 minutes
Probability with RSDs8 minutes
Estimates and Estimators6 minutes
Confidence Intervals5 minutes
Confidence Intervals for the Mean and Variance11 minutes
Confidence Intervals for Proportions and Poisson Counts4 minutes

2 assignmentsTotal 120 minutes

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

What's included

13 videos2 assignments

13 videosTotal 76 minutes

Hypothesis Testing5 minutes
Significance Level and Risk2 minutes
One vs Two Tail3 minutes
Type 1 and 2 Error5 minutes
Beta and Power8 minutes
Calculating Power5 minutes
Calculating Sample Size6 minutes
Independent vs Dependent Samples3 minutes
Two Independent Sample Tests for Means10 minutes
Two Dependent Sample Tests for Means11 minutes
Two Sample Tests for Variances5 minutes
Two Sample Tests for Proportions8 minutes
Two Sample Independent Tests for Poisson Counts4 minutes

2 assignmentsTotal 120 minutes

Week 5 Practice Assessment30 minutes
Two Sample Hypothesis Testing90 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructor

Wendy Martin

University of Colorado Boulder

6 Courses12,949 learners

Offered by

University of Colorado Boulder

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