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Descriptive Statistics
Sebastian Thrun
and
Josh Bernhard
Sebastian Thrun
and
Josh Bernhard
Learn how to describe data in terms of data types, measures of center, measures of spread, shape, and outliers. These essential skills in descriptive statistics provide the foundation for more advanced statistical techniques that are used for data science,...
Read more
Learn how to describe data in terms of data types, measures of center, measures of spread, shape, and outliers. These essential skills in descriptive statistics provide the foundation for more advanced statistical techniques that are used for data science,...
Read more
Learn how to describe data in terms of data types, measures of center, measures of spread, shape, and outliers. These essential skills in descriptive statistics provide the foundation for more advanced statistical techniques that are used for data science, data analysis, and machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/2sxuct/descriptive
What's inside
Syllabus
In this lesson, we kick off the course with an introduction to data and descriptive statistics.
In this lesson, we establish key distinctions between different types of data, including quantitative, categorical, ordinal, nominal, continuous, and discrete data types.
Read more
Syllabus
In this lesson, we kick off the course with an introduction to data and descriptive statistics.
In this lesson, we establish key distinctions between different types of data, including quantitative, categorical, ordinal, nominal, continuous, and discrete data types.
Read more
In this lesson, we get into the calculations and use cases for three popular measures of center: mean, median, and mode.
In this lesson, we demystify the mathematical notation used for random variables, observed values, and aggregations.
In this lesson, we get into the calculations and use cases for several popular measures of spread, including five-number summaries, standard deviation, and variance.
In this lesson, we cover two additional forms of descriptive statistics for data distributions: shape and outliers.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces foundational concepts in descriptive statistics essential for further exploration in data science
Taught by Sebastian Thrun, a renowned figure in the field of AI and autonomous vehicles
Provides a comprehensive overview of descriptive statistics techniques, including measures of center, spread, shape, and outliers
Suitable for beginners seeking to establish a solid foundation in descriptive statistics
Offers a practical approach to understanding data and extracting meaningful insights
May require prior knowledge of basic mathematical concepts
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Activities
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Descriptive Statistics.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Descriptive Statistics will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. They work in various industries, including healthcare, finance, and research. This Descriptive Statistics course provides a strong foundation for aspiring Statisticians by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Statisticians.
Data Analyst
Data Analyst
Data Analysts help businesses make decisions based on extensive data analysis. They analyze data using various statistical techniques, including those taught in this Descriptive Statistics course. This course covers measures of center, spread, shape, and outliers, providing a solid foundation for data analysis. The course also covers different data types and how to calculate various statistics, which are essential skills for Data Analysts.
Data Scientist
Data Scientist
Data Scientists use statistical methods and machine learning algorithms to extract insights from data. They work in various industries, including technology, healthcare, and finance. This Descriptive Statistics course provides a solid foundation for aspiring Data Scientists by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Data Scientists.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior and market trends. This Descriptive Statistics course provides a strong foundation for aspiring Market Researchers by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Market Researchers.
Risk Analyst
Risk Analyst
Risk Analysts use statistical methods to assess and manage risk. This Descriptive Statistics course provides a strong foundation for aspiring Risk Analysts by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Risk Analysts.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze financial data and make investment recommendations. This Descriptive Statistics course provides a strong foundation for aspiring Quantitative Analysts by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Quantitative Analysts.
Actuary
Actuary
Actuaries use statistical methods to assess risk and uncertainty in insurance and finance. This Descriptive Statistics course provides a strong foundation for aspiring Actuaries by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Actuaries.
Epidemiologist
Epidemiologist
Epidemiologists use statistical methods to investigate the causes and spread of diseases. This Descriptive Statistics course provides a strong foundation for aspiring Epidemiologists by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Epidemiologists.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze studies in the medical field. This Descriptive Statistics course provides a strong foundation for aspiring Biostatisticians by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Biostatisticians.
Survey Researcher
Survey Researcher
Survey Researchers use statistical methods to design and analyze surveys. This Descriptive Statistics course provides a strong foundation for aspiring Survey Researchers by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Survey Researchers.
Business Analyst
Business Analyst
Business Analysts use statistical methods to analyze business data and make recommendations for improvement. This Descriptive Statistics course provides a strong foundation for aspiring Business Analysts by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Business Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical methods to analyze and improve business operations. This Descriptive Statistics course provides a strong foundation for aspiring Operations Research Analysts by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Operations Research Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use statistical methods to analyze financial data and make investment recommendations. This Descriptive Statistics course provides a solid foundation for aspiring Financial Analysts by covering key concepts such as measures of center, spread, shape, and outliers. The course also explores different data types and how to calculate various statistics, which are essential skills for Financial Analysts.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines and databases. This Descriptive Statistics course may be useful for aspiring Data Engineers as it covers key concepts such as data types and how to calculate various statistics. These skills can be helpful for Data Engineers who need to understand and process data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This Descriptive Statistics course may be useful for aspiring Software Engineers as it covers key concepts such as data types and how to calculate various statistics. These skills can be helpful for Software Engineers who need to understand and process data.
For more career information including salaries, visit:
OpenCourser.com/course/2sxuct/descriptive
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
Descriptive Statistics.
Provides a solid foundation in statistical concepts and methods, covering topics such as data types, measures of center and spread, and hypothesis testing. It valuable resource for students and practitioners in various fields who want to enhance their understanding of statistics.
Offers a comprehensive and accessible introduction to the field of statistics, covering a wide range of topics from descriptive statistics to Bayesian inference. It valuable resource for students and practitioners who want to gain a broad understanding of statistical concepts.
Provides a rigorous and theoretical foundation in statistical inference, covering topics such as point estimation, hypothesis testing, and Bayesian inference. It valuable resource for students and researchers interested in the mathematical underpinnings of statistics.
Comprehensive introduction to statistical learning, providing an in-depth exploration of supervised and unsupervised learning methods. It covers topics such as linear and logistic regression, decision trees, support vector machines, and clustering algorithms.
Provides a comprehensive introduction to probability and statistics for computer science students and practitioners. It covers topics such as probability theory, random variables, and statistical inference.
Focuses specifically on statistical methods used in psychology, providing a clear and concise explanation of concepts and techniques. It covers topics such as hypothesis testing, correlation, regression, and analysis of variance.
Focuses on multivariate statistical techniques, providing a comprehensive overview of methods such as principal component analysis, cluster analysis, and discriminant analysis. It valuable resource for students and practitioners in various fields.
Provides a comprehensive introduction to nonparametric statistical methods, covering topics such as hypothesis testing, rank-based procedures, and resampling techniques. It valuable resource for students and researchers who work with data that do not follow a normal distribution.
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Provides a comprehensive introduction to probability and statistics, covering topics such as probability distributions, random variables, and statistical inference. It valuable resource for students and practitioners in engineering and science.
Provides a comprehensive introduction to statistical power analysis, covering topics such as effect size estimation, sample size calculation, and hypothesis testing. It valuable resource for researchers and practitioners who want to ensure that their studies have sufficient statistical power.
Is tailored towards business and economics students, providing a practical approach to statistical concepts and applications. It covers topics such as descriptive statistics, probability, hypothesis testing, and regression analysis.
While specialized in astronomy, this book provides a valuable introduction to statistical methods and concepts commonly used in various scientific disciplines. It covers topics such as data visualization, probability distributions, and statistical inference.
Provides a practical introduction to statistical methods commonly used in agricultural, biological, and environmental sciences. It covers topics such as experimental design, data analysis, and statistical modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2sxuct/descriptive
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Time
300 hours
Level
Introductory
Via
Udacity
Instructors
Sebastian Thrun
Josh Bernhard
Language
English
Good to know
Introduces foundational concepts in descriptive statistics essential for further exploration in data science
Taught by Sebastian Thrun, a renowned figure in the field of AI and autonomous vehicles
Provides a comprehensive overview of descriptive statistics techniques, including measures of center, spread, shape, and outliers
Suitable for beginners seeking to establish a solid foundation in descriptive statistics
Offers a practical approach to understanding data and extracting meaningful insights
May require prior knowledge of basic mathematical concepts
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