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Managing, Describing, and Analyzing Data
  • Level Foundation
  • المدة 18 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

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.

الوحدات

Introduction to Managing, Describing and Analyzing Data

    • 1 Videos
    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself!

1 Videos

  • Welcome to Managing, Describing and Analyzing Data

2 Readings

  • Earn Academic Credit for your Work!
  • Course Support

Data and Measurement

    • 4 Videos
    • 1 Readings
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4 Videos

  • Types of Data and Measurement Scales
  • Measurement Scales: Nominal and Ordinal
  • Measurement Scales: Interval, Ratio and Absolute
  • Measurement as a Process, The Big 5 Aspects of Data

1 Readings

  • Attention Learners: R Code / File Resources

Sampling Concepts

    • 1 Videos
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1 Videos

  • Sampling Concepts

Using R and RStudio

    • 1 Videos
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1 Videos

  • Working in RStudio

Discussion: Data and Measurement

    • 1 Discussion
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1 Discussions

  • Data and Measurement

Assessment: Data and Measurement

    • 2 Quiz
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2 Quiz

  • Week 1 Practice Assessment
  • Assessment: Data and Measurement

Displaying Data Through Time

    • 1 Videos
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1 Videos

  • Create a Run Chart

Frequency Visualizations

    • 3 Videos
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3 Videos

  • Frequency Distributions
  • Frequency Polygons and Histograms
  • Histogram Patterns and Density Plots

Box and Whisker Plots

    • 1 Videos
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1 Videos

  • Box and Whisker Plots

Measures of Central Tendency and Position

    • 3 Videos
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3 Videos

  • Measures of Central Tendency Mean
  • Measures of Central Tendency: Median, Mode
  • Measures of Position

Measures of Dispersion and Shape

    • 2 Videos
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2 Videos

  • Measures of Dispersion
  • Measures of Shape

Measures of Relationship

    • 1 Videos
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1 Videos

  • Measures of Relationship

Discussion: Describing Data Graphically and Numerically

    • 2 Discussion
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2 Discussions

  • Describing Data Graphically
  • Describing Data Numerically

Assessment: Describing Data Graphically and Numerically

    • 3 Quiz
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3 Quiz

  • Week 2 Practice Assessment
  • Assessment: Describing Data Graphically
  • Assessment: Describing Data Numerically

Probability and Probability Distributions

    • 4 Videos
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4 Videos

  • Introduction to Probability Part 1
  • Introduction to Probability Part 2
  • Probability Distributions Part 1
  • Probability Distributions Part 2

Discrete Probability Distributions

    • 2 Videos
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2 Videos

  • The Binomial Distribution
  • The Poisson Distribution

Continuous Probability Distributions

    • 2 Videos
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2 Videos

  • The Normal Distribution
  • The Exponential Distribution

Discussion: Probability and Probability Distributions

    • 1 Discussion
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1 Discussions

  • Probability and Probability Distributions

Assessment: Probability and Probability Distributions

    • 2 Quiz
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2 Quiz

  • Week 3 Practice Assessment
  • Probability and Probability Distributions

Sampling Error

    • 1 Videos
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1 Videos

  • Sampling Error

Random Sampling Distributions and the Central Limit Theorem

    • 3 Videos
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3 Videos

  • Random Sampling Distributions
  • The Central Theorem
  • Probability with RSDs

Estimates and Estimators

    • 1 Videos
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1 Videos

  • Estimates and Estimators

Confidence Intervals

    • 3 Videos
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3 Videos

  • Confidence Intervals
  • Confidence Intervals for the Mean and Variance
  • Confidence Intervals for Proportions and Poisson Counts

Discussion: Sampling Distributions, Error and Estimation

    • 1 Discussion
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1 Discussions

  • Sampling Distributions, Error and Estimation

Assessment: Sampling Distributions, Error and Estimation

    • 2 Quiz
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2 Quiz

  • Week 4 Practice Assessment
  • Sampling Distributions, Error and Estimation

Hypothesis Testing

    • 4 Videos
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4 Videos

  • Hypothesis Testing
  • Significance Level and Risk
  • One vs Two Tail
  • Type 1 and 2 Error

Beta, Power and Sample Size

    • 3 Videos
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3 Videos

  • Beta and Power
  • Calculating Power
  • Calculating Sample Size

Testing for Differences / Changes in Means

    • 3 Videos
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3 Videos

  • Independent vs Dependent Samples
  • Two Independent Sample Tests for Means
  • Two Dependent Sample Tests for Means

Testing for Differences / Changes in Variances

    • 1 Videos
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1 Videos

  • Two Sample Tests for Variances

Testing for Differences / Changes in Proportions and Counts

    • 2 Videos
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2 Videos

  • Two Sample Tests for Proportions
  • Two Sample Independent Tests for Poisson Counts

Quiz: Two Sample Hypothesis Testing

    • 2 Quiz
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2 Quiz

  • Week 5 Practice Assessment
  • Two Sample Hypothesis Testing

Auto Summary

Unlock the power of data with the "Managing, Describing, and Analyzing Data" course, designed to provide a solid foundation in data science within the Maths & Statistics domain. This course guides learners through the essentials of understanding and classifying data, crucial for making informed decisions. You will master the art of describing data both graphically and numerically through descriptive statistics and the use of R software. Explore four common probability distributions and learn how to apply them to analyze data sets effectively. Additionally, gain insights into sampling error, sampling distributions, and the nuances of decision-making errors. This comprehensive course is part of CU Boulder’s Master of Science in Data Science (MS-DS) degree available on Coursera. The MS-DS is an interdisciplinary program that integrates expertise from various departments including Applied Mathematics, Computer Science, and Information Science. With performance-based admissions and no lengthy application process, it caters to individuals from diverse academic and professional backgrounds in computer science, mathematics, and statistics. Suitable for foundational learners, the course spans 1080 hours and offers flexible subscription options, including Starter and Professional plans. Whether you're aiming to earn academic credit towards an MS-DS degree or simply elevate your data science skills, this course provides the tools and knowledge to succeed. Embark on your data science journey today and transform your ability to manage, describe, and analyze data effectively.

Wendy Martin
Instructor

Wendy Martin