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
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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
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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 videos4 readings2 assignments2 discussion prompts
7 videos•Total 48 minutes
- Welcome to Managing, Describing and Analyzing Data•1 minute
- Types of Data and Measurement Scales•7 minutes
- Measurement Scales: Nominal and Ordinal•6 minutes
- Measurement Scales: Interval, Ratio and Absolute•6 minutes
- Measurement as a Process, The Big 5 Aspects of Data•10 minutes
- Sampling Concepts•6 minutes
- Working in RStudio•11 minutes
4 readings•Total 31 minutes
- Course Updates and Accessibility Support•1 minute
- Earn Academic Credit for your Work!•10 minutes
- Course Support•10 minutes
- Attention Learners: R Code / File Resources•10 minutes
2 assignments•Total 75 minutes
- Week 1 Practice Assessment•30 minutes
- Assessment: Data and Measurement•45 minutes
2 discussion prompts•Total 20 minutes
- Introduce Yourself!•10 minutes
- Data and Measurement•10 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 videos•Total 85 minutes
- Create a Run Chart•9 minutes
- Frequency Distributions•8 minutes
- Frequency Polygons and Histograms•7 minutes
- Histogram Patterns and Density Plots•8 minutes
- Box and Whisker Plots•7 minutes
- Measures of Central Tendency Mean•10 minutes
- Measures of Central Tendency: Median, Mode•7 minutes
- Measures of Position•8 minutes
- Measures of Dispersion •8 minutes
- Measures of Shape•7 minutes
- Measures of Relationship•7 minutes
3 assignments•Total 180 minutes
- Week 2 Practice Assessment•30 minutes
- Assessment: Describing Data Graphically•75 minutes
- Assessment: Describing Data Numerically•75 minutes
2 discussion prompts•Total 20 minutes
- Describing Data Graphically•10 minutes
- Describing Data Numerically•10 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 videos•Total 70 minutes
- Introduction to Probability Part 1•7 minutes
- Introduction to Probability Part 2•8 minutes
- Probability Distributions Part 1•5 minutes
- Probability Distributions Part 2•7 minutes
- The Binomial Distribution•11 minutes
- The Poisson Distribution•9 minutes
- The Normal Distribution•13 minutes
- The Exponential Distribution•10 minutes
2 assignments•Total 150 minutes
- Week 3 Practice Assessment•30 minutes
- Probability and Probability Distributions•120 minutes
1 discussion prompt•Total 10 minutes
- Probability and Probability Distributions•10 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 videos•Total 55 minutes
- Sampling Error•7 minutes
- Random Sampling Distributions•8 minutes
- The Central Theorem•6 minutes
- Probability with RSDs•8 minutes
- Estimates and Estimators•6 minutes
- Confidence Intervals•5 minutes
- Confidence Intervals for the Mean and Variance•11 minutes
- Confidence Intervals for Proportions and Poisson Counts•4 minutes
2 assignments•Total 120 minutes
- Week 4 Practice Assessment•30 minutes
- Sampling Distributions, Error and Estimation•90 minutes
1 discussion prompt•Total 10 minutes
- Sampling Distributions, Error and Estimation•10 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 videos•Total 76 minutes
- Hypothesis Testing•5 minutes
- Significance Level and Risk•2 minutes
- One vs Two Tail•3 minutes
- Type 1 and 2 Error•5 minutes
- Beta and Power•8 minutes
- Calculating Power•5 minutes
- Calculating Sample Size•6 minutes
- Independent vs Dependent Samples•3 minutes
- Two Independent Sample Tests for Means•10 minutes
- Two Dependent Sample Tests for Means•11 minutes
- Two Sample Tests for Variances•5 minutes
- Two Sample Tests for Proportions•8 minutes
- Two Sample Independent Tests for Poisson Counts•4 minutes
2 assignments•Total 120 minutes
- Week 5 Practice Assessment•30 minutes
- Two Sample Hypothesis Testing•90 minutes
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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
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Reviewed on Aug 18, 2024
This is the course you can actually master the content in the course
Reviewed on Apr 18, 2021
We learned some theory and practiced in R. A perfect combination!
Reviewed on Mar 12, 2021
The instructor is clear and easy to follow. The lessons are succinct. It helps to be familiar with the topics already.
Frequently asked questions
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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.
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