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

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Wendy Martin

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Part of the Master of Science in Data Science degree
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Data Science Methods for Quality Improvement SpecializationUniversity of Colorado Boulder

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 1 of 3 in the

Data Science Methods for Quality Improvement Specialization

Beginner Level

Beginner level; no previous experience necessary.

Approx. 17 hours to complete

English

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What you will 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 will gain

analyzing data
describing data
using R
graphing data

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 3 in the

Data Science Methods for Quality Improvement Specialization

Beginner Level

Beginner level; no previous experience necessary.

Approx. 17 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.2/5 (10 Ratings)

Wendy Martin

W. Edwards Deming Professor of Management

Engineering Management Program

3,888 Learners

4 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Data and Measurement

3 hours to complete

7 videos (Total 48 min), 2 readings, 2 quizzes

7 videos

Welcome to Managing, Describing and Analyzing Data1mTypes of Data and Measurement Scales6mMeasurement Scales: Nominal and Ordinal6mMeasurement Scales: Interval, Ratio and Absolute6mMeasurement as a Process, The Big 5 Aspects of Data10mSampling Concepts6mWorking in RStudio10m

2 readings

Welcome and Where to Find Help 10mAttention Learners: R Code / File Resources10m

2 practice exercises

Week 1 Practice Assessment30mAssessment: Data and Measurement45m

Week2

Week 2

5 hours to complete

Describing Data Graphically and Numerically

5 hours to complete

11 videos (Total 85 min)

11 videos

Create a Run Chart9mFrequency Distributions7mFrequency Polygons and Histograms7mHistogram Patterns and Density Plots7mBox and Whisker Plots7mMeasures of Central Tendency Mean9mMeasures of Central Tendency: Median, Mode7mMeasures of Position8mMeasures of Dispersion 7mMeasures of Shape6mMeasures of Relationship6m

3 practice exercises

Week 2 Practice Assessment30mAssessment: Describing Data Graphically1h 15mAssessment: Describing Data Numerically1h 15m

Week3

Week 3

4 hours to complete

Probability and Probability Distributions

4 hours to complete

8 videos (Total 70 min)

Week4

Week 4

3 hours to complete

Sampling Distributions, Error and Estimation

3 hours to complete

8 videos (Total 55 min)

Reviews

4.5

8 reviews

5 stars
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4 stars
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3 stars
4.34%
1 star
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TOP REVIEWS FROM MANAGING, DESCRIBING, AND ANALYZING DATA

by MWMar 12, 2021

The instructor is clear and easy to follow. The lessons are succinct. It helps to be familiar with the topics already.

by BTApr 18, 2021

We learned some theory and practiced in R. A perfect combination!

View all reviews

About the Data Science Methods for Quality Improvement Specialization

Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability.

By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization 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.

Data Science Methods for Quality Improvement

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Managing, Describing, and Analyzing Data

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What you will learn

Skills you will gain