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Wendy Martin
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/noeo38/managing
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What's inside
Syllabus
Data and Measurement
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.
Read more
Syllabus
Data and Measurement
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.
Read more
Describing Data Graphically and Numerically
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.
Probability and Probability Distributions
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.
Sampling Distributions, Error and Estimation
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.
Two Sample Hypothesis Testing
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides learners with usable and practical applications of data science and statistics for beginners
Develops foundational data science and statistical skills, including data handling, exploration, and analysis, providing a comprehensive overview of the field
Leverages R and RStudio software, industry-standard tools for data analysis and visualization, ensuring learners are equipped with in-demand skills
Includes a strong focus on probability and probability distributions, essential concepts for understanding data and decision-making
Covers sampling error and sampling distributions, crucial elements for accurate data analysis and interpretation
Taught by instructors from the University of Colorado Boulder, offering learners access to expertise and resources from a leading academic institution
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Reviews summary
Thorough data management course
Learners say this course on managing, describing, and analyzing data is thorough, engaging, and well-paced. The course combines theory with practical R applications, making it a valuable resource for those looking to improve their data skills. Students appreciate the clear explanations and helpful, responsive instructor. Overall, this course is highly recommended for those seeking to enhance their data management and analysis abilities.
Learners with no prior R experience found the course helpful.
"I had never worked with the R language before, and I learned about it satisfactorily."
Coursework is challenging but fair.
"The assessments are challenging but fair."
"It is quite challenging but I finally achieved it."
Instructor is supportive and dedicated.
"The instructor is clear and easy to follow."
"Professor Martin is fantastic - she's very active on the platform and goes out of her way to help students understand the concepts."
Blends theoretical concepts with practical exercises.
"We learned some theory and practiced in R. A perfect combination!"
"The course is very practical with good case studies and exercises..."
Students suggest combining the 3 courses into 1.
"Wishing the 3 courses to be one of the elective courses for the MEM besides the master of data science!!!"
Students request more examples similar to those on exams.
"Only providing a laundry list of statistic functions..."
"I just feel that more examples similar to those presented in the exams are needed."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Managing, Describing, and Analyzing Data with these
activities:
Review Linear Algebra
Show steps
Review the fundamentals of linear algebra to strengthen your mathematical foundation for this course.
Browse courses on
Linear Algebra
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-
Revisit matrix operations, including addition, subtraction, multiplication, and inverses.
-
Practice solving systems of linear equations using various methods.
-
Review concepts of vector spaces, subspaces, and linear transformations.
Join a Study Group
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Engage with fellow learners to discuss course concepts, work on assignments together, and provide support.
Browse courses on
Collaboration
Show steps
-
Find a study group of peers who are taking the same course.
-
Meet regularly to review materials, solve problems, and share insights.
Read "Statistical Rethinking" by Richard McElreath
Show steps
Gain insights into Bayesian statistical modeling and its applications to real-world problems.
View
Statistical Rethinking
on Amazon
Show steps
-
Read through the first few chapters to grasp the core concepts of Bayesian inference.
-
Work through the exercises to reinforce your understanding and practice implementing Bayesian models.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Probability Distributions
Show steps
Explore different probability distributions and their applications through guided tutorials.
Browse courses on
Probability Distributions
Show steps
-
Find online tutorials on binomial, normal, and Poisson distributions.
-
Complete the tutorials to learn the properties and applications of each distribution.
-
Practice using these distributions in real-world scenarios.
Solve Statistical Problems with R
Show steps
Sharpen your data analysis skills by solving statistical problems using R programming.
Browse courses on
R Programming
Show steps
-
Find online resources or textbooks with practice problems in statistics.
-
Solve the problems using R, focusing on implementing statistical methods and data visualization techniques.
-
Review your solutions to identify areas for improvement.
Develop a Data Visualization Dashboard
Show steps
Apply your newly acquired data visualization skills to create an interactive dashboard that communicates insights from a provided dataset.
Browse courses on
Data Visualization
Show steps
-
Gather data from a relevant source and explore it to identify key findings.
-
Choose an appropriate visualization tool such as Tableau or Power BI.
-
Design and develop a visually appealing and informative dashboard that clearly conveys the insights.
Participate in a Data Science Competition
Show steps
Test your skills and knowledge by participating in a data science competition that aligns with the course material.
Browse courses on
Data Science
Show steps
-
Identify and register for a relevant data science competition.
-
Work individually or in a team to develop a solution that addresses the competition challenge.
-
Submit your solution and eagerly await the results.
Contribute to an Open-Source Data Science Project
Show steps
Gain practical experience and contribute to the data science community by participating in an open-source data science project.
Browse courses on
Data Science
Show steps
-
Identify an open-source data science project that aligns with your interests and skills.
-
Join the project's community and start contributing in areas such as code development, documentation, or testing.
-
Engage with the project maintainers and other contributors to expand your knowledge network.
Review Linear Algebra
Show steps
Review the fundamentals of linear algebra to strengthen your mathematical foundation for this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit matrix operations, including addition, subtraction, multiplication, and inverses.
- Practice solving systems of linear equations using various methods.
- Review concepts of vector spaces, subspaces, and linear transformations.
Join a Study Group
Show steps
Engage with fellow learners to discuss course concepts, work on assignments together, and provide support.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find a study group of peers who are taking the same course.
- Meet regularly to review materials, solve problems, and share insights.
Read "Statistical Rethinking" by Richard McElreath
Show steps
Gain insights into Bayesian statistical modeling and its applications to real-world problems.
View
Statistical Rethinking
on Amazon
Show steps
Show steps
Show steps
- Read through the first few chapters to grasp the core concepts of Bayesian inference.
- Work through the exercises to reinforce your understanding and practice implementing Bayesian models.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Probability Distributions
Show steps
Explore different probability distributions and their applications through guided tutorials.
Browse courses on
Probability Distributions
Show steps
Show steps
Show steps
- Find online tutorials on binomial, normal, and Poisson distributions.
- Complete the tutorials to learn the properties and applications of each distribution.
- Practice using these distributions in real-world scenarios.
Solve Statistical Problems with R
Show steps
Sharpen your data analysis skills by solving statistical problems using R programming.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice problems in statistics.
- Solve the problems using R, focusing on implementing statistical methods and data visualization techniques.
- Review your solutions to identify areas for improvement.
Develop a Data Visualization Dashboard
Show steps
Apply your newly acquired data visualization skills to create an interactive dashboard that communicates insights from a provided dataset.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather data from a relevant source and explore it to identify key findings.
- Choose an appropriate visualization tool such as Tableau or Power BI.
- Design and develop a visually appealing and informative dashboard that clearly conveys the insights.
Participate in a Data Science Competition
Show steps
Test your skills and knowledge by participating in a data science competition that aligns with the course material.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify and register for a relevant data science competition.
- Work individually or in a team to develop a solution that addresses the competition challenge.
- Submit your solution and eagerly await the results.
Contribute to an Open-Source Data Science Project
Show steps
Gain practical experience and contribute to the data science community by participating in an open-source data science project.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify an open-source data science project that aligns with your interests and skills.
- Join the project's community and start contributing in areas such as code development, documentation, or testing.
- Engage with the project maintainers and other contributors to expand your knowledge network.
Career center
Learners who complete Managing, Describing, and Analyzing Data will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their skills in math, statistics, and computer science to solve business problems. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Data Analyst
Data Analyst
Data Analysts help companies make informed decisions by analyzing and interpreting large datasets. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumer behavior. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Data Engineer
Data Engineer
Data Engineers design and build the systems that store and process data. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management and data analysis. You will also learn how to use R software to work with data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management and data analysis. You will also learn how to use R software to work with data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in probability distributions and statistical inference. You will also learn how to use R software to analyze data and make inferences.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Epidemiologist
Epidemiologist
Epidemiologists use data to study the causes and spread of diseases. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
Data Journalist
Data Journalist
Data Journalists use data to tell stories. This course can help you develop the skills needed to succeed in this role by providing you with a strong foundation in data management, descriptive statistics, and probability distributions. You will also learn how to use R software to analyze data and make inferences.
For more career information including salaries, visit:
OpenCourser.com/course/noeo38/managing
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Managing, Describing, and Analyzing Data.
Save
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable reference for students who want to learn more about the theoretical foundations of deep learning.
Save
Provides a comprehensive overview of computer vision. It covers topics such as image processing, feature extraction, and object recognition. It valuable reference for students who want to learn more about the theoretical foundations of computer vision.
Provides a comprehensive overview of speech and language processing. It covers topics such as speech recognition, natural language understanding, and machine translation. It valuable reference for students who want to learn more about the theoretical foundations of speech and language processing.
Provides a comprehensive overview of data-intensive text processing with MapReduce. It covers topics such as text preprocessing, feature extraction, and text classification. It valuable reference for students who want to learn more about the theoretical foundations of data-intensive text processing with MapReduce.
Classic in the field of statistical learning. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and regularization. It valuable reference for students who want to learn more about the theoretical foundations of data analysis.
Provides a comprehensive overview of information retrieval. It covers topics such as text indexing, search algorithms, and evaluation metrics. It valuable reference for students who want to learn more about the theoretical foundations of information retrieval.
Provides a comprehensive overview of natural language processing. It covers topics such as part-of-speech tagging, named entity recognition, and machine translation. It valuable reference for students who want to learn more about the theoretical foundations of natural language processing.
More accessible introduction to statistical learning than The Elements of Statistical Learning. It covers similar topics, but with a focus on practical applications. It good choice for students who are new to data analysis.
Provides a more in-depth introduction to probability and statistics than Think Stats. It good choice for students who want to learn more about the mathematical foundations of these topics.
Save
Practical guide to data science. It covers topics such as data cleaning, data analysis, and data visualization. It good choice for students who are new to data science and want to learn how to apply it in practice.
Provides a comprehensive overview of regression and multilevel/hierarchical models, which are essential topics in data analysis. It is particularly useful for students who want to gain a deeper understanding of the statistical models and methods used in this course.
Provides a practical overview of data science for business applications. It covers topics such as data mining, machine learning, and data visualization. It good choice for students who are interested in using data analysis to solve business problems.
Free online textbook that provides an introduction to probability and statistics. It good choice for students who want to learn the basics of these topics before taking this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/noeo38/managing
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Level
Introductory
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Wendy Martin
Language
English
Transcripts
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This course belongs to a
collection
called
Data Science Methods for Quality Improvement. It's comprised of three courses:
- Stability and Capability in Quality Improvement
- Managing, Describing, and Analyzing Data (viewing)
- Measurement Systems Analysis
Good to know
Provides learners with usable and practical applications of data science and statistics for beginners
Develops foundational data science and statistical skills, including data handling, exploration, and analysis, providing a comprehensive overview of the field
Leverages R and RStudio software, industry-standard tools for data analysis and visualization, ensuring learners are equipped with in-demand skills
Includes a strong focus on probability and probability distributions, essential concepts for understanding data and decision-making
Covers sampling error and sampling distributions, crucial elements for accurate data analysis and interpretation
Taught by instructors from the University of Colorado Boulder, offering learners access to expertise and resources from a leading academic institution
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