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Ronald Rogers,
Katie Kormanik,
and
Sean Laraway
Statistics is an important field of math that is used to analyze, interpret, and predict outcomes from data. Descriptive statistics will teach you the basic concepts used to describe data. This is a great beginner course for those interested in Data Science, Economics, Psychology, Machine Learning, Sports analytics and just about any other field.
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possible dealbreakers
Explores core concepts for those interested in a wide range of fields (Data Science, Economics, Psychology, Machine Learning, Sports analytics, etc.)
Teaches basic concepts used to describe data, building a strong foundation for beginners
Assumes understanding of basic algebra and arithmetic, making it accessible to learners with a general math background
Prerequisites advise learners to review Udacity documentation, which may require additional time and effort
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Reviews summary
Beginner-friendly intro to statistics
Most students find this course to be a strong foundational introduction to basic concepts in data analysis and statistics for beginners who are looking to enter more advanced data science fields like economics, machine learning, and sports analytics. They appreciate the practical, real-world examples provided to help illustrate more complex concepts.
Clear and easy-to-understand explanations.
"It gives a thorough and easy to understand explanation about the key concepts in Statistics..."
Real-world examples and exercises provided.
"Lively, engaging, and practical with real-world situations."
"...reinforcing exercises."
Accessible content for absolute beginners.
"This class is perfect for the Statics beginner."
At least one exercise has a factual error.
"Also some concepts are mentioned and used early in the class but introduced only later on."
Slower pace may be considered boring.
"A lot of people here have found this course boring."
"I think it is slow paced..."
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 Intro to Descriptive Statistics with these
activities:
Review your notes from previous courses
Show steps
Refreshing your knowledge of basic algebra and arithmetic will help you understand the concepts of descriptive statistics more easily.
Browse courses on
Algebra
Show steps
-
Gather your notes from previous courses in algebra and arithmetic.
-
Review the notes and make sure you understand the key concepts.
-
Take a practice quiz to test your understanding.
Practice basic algebra and arithmetic problems
Show steps
Practicing basic algebra and arithmetic will help you sharpen your skills and make it easier to understand the concepts of descriptive statistics.
Browse courses on
Algebra
Show steps
-
Find a set of basic algebra and arithmetic problems.
-
Work through the problems step-by-step, showing your work.
-
Check your answers against the provided solutions.
-
Review the problems you got wrong and try to understand why you made mistakes.
Read 'Statistics for Beginners' by A. Agresti
Show steps
This book provides a clear and concise introduction to descriptive statistics, making it a great resource for students who want to build a strong foundation in the subject.
View
Statistical Methods for the Social Sciences,...
on Amazon
Show steps
-
Read each chapter carefully and take notes on the key concepts.
-
Work through the practice problems at the end of each chapter.
-
Summarize the main points of each chapter in your own words.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or online forum
Show steps
Joining a study group or online forum can help you connect with other students and learn from their experiences.
Browse courses on
Descriptive Statistics
Show steps
-
Find a study group or online forum that focuses on descriptive statistics.
-
Introduce yourself and get to know the other members.
-
Participate in discussions and ask questions.
-
Help other members with their questions.
Watch tutorials on YouTube or Coursera
Show steps
Watching tutorials can help you learn about descriptive statistics in a more interactive and engaging way.
Browse courses on
Descriptive Statistics
Show steps
-
Find a series of tutorials on descriptive statistics that are appropriate for your level.
-
Watch the tutorials and take notes on the key concepts.
-
Try to apply the concepts you learn in the tutorials to your own data.
Practice descriptive statistics problems
Show steps
Solving practice problems will help you reinforce your understanding of descriptive statistics concepts and improve your problem-solving skills.
Browse courses on
Descriptive Statistics
Show steps
-
Find a set of practice problems online or in a textbook.
-
Work through the problems step-by-step, showing your work.
-
Check your answers against the provided solutions.
-
Review the problems you got wrong and try to understand why you made mistakes.
Create a presentation on descriptive statistics
Show steps
Creating a presentation will help you organize your knowledge of descriptive statistics and improve your communication skills.
Browse courses on
Descriptive Statistics
Show steps
-
Choose a topic for your presentation.
-
Research your topic and gather data.
-
Create a set of slides that summarize the key concepts of your topic.
-
Practice your presentation.
Review your notes from previous courses
Show steps
Refreshing your knowledge of basic algebra and arithmetic will help you understand the concepts of descriptive statistics more easily.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Gather your notes from previous courses in algebra and arithmetic.
- Review the notes and make sure you understand the key concepts.
- Take a practice quiz to test your understanding.
Practice basic algebra and arithmetic problems
Show steps
Practicing basic algebra and arithmetic will help you sharpen your skills and make it easier to understand the concepts of descriptive statistics.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Find a set of basic algebra and arithmetic problems.
- Work through the problems step-by-step, showing your work.
- Check your answers against the provided solutions.
- Review the problems you got wrong and try to understand why you made mistakes.
Read 'Statistics for Beginners' by A. Agresti
Show steps
This book provides a clear and concise introduction to descriptive statistics, making it a great resource for students who want to build a strong foundation in the subject.
View
Statistical Methods for the Social Sciences,...
on Amazon
Show steps
Show steps
Show steps
- Read each chapter carefully and take notes on the key concepts.
- Work through the practice problems at the end of each chapter.
- Summarize the main points of each chapter in your own words.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or online forum
Show steps
Joining a study group or online forum can help you connect with other students and learn from their experiences.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Find a study group or online forum that focuses on descriptive statistics.
- Introduce yourself and get to know the other members.
- Participate in discussions and ask questions.
- Help other members with their questions.
Watch tutorials on YouTube or Coursera
Show steps
Watching tutorials can help you learn about descriptive statistics in a more interactive and engaging way.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Find a series of tutorials on descriptive statistics that are appropriate for your level.
- Watch the tutorials and take notes on the key concepts.
- Try to apply the concepts you learn in the tutorials to your own data.
Practice descriptive statistics problems
Show steps
Solving practice problems will help you reinforce your understanding of descriptive statistics concepts and improve your problem-solving skills.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Find a set of practice problems online or in a textbook.
- Work through the problems step-by-step, showing your work.
- Check your answers against the provided solutions.
- Review the problems you got wrong and try to understand why you made mistakes.
Create a presentation on descriptive statistics
Show steps
Creating a presentation will help you organize your knowledge of descriptive statistics and improve your communication skills.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Choose a topic for your presentation.
- Research your topic and gather data.
- Create a set of slides that summarize the key concepts of your topic.
- Practice your presentation.
Career center
Learners who complete Intro to Descriptive Statistics will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, data visualization, and data mining. Additionally, the course will help you build a foundation in programming, which is essential for many data analyst roles.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, probability, and statistical modeling. Additionally, the course will help you build a foundation in programming, which is essential for many statistician roles.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to help businesses understand their customers and make informed decisions. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, data visualization, and data mining. Additionally, the course will help you build a foundation in marketing, which is essential for many market researcher roles.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make recommendations to businesses and investors. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, financial modeling, and data analysis. Additionally, the course will help you build a foundation in finance, which is essential for many financial analyst roles.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, data visualization, and data mining. Additionally, the course will help you build a foundation in business, which is essential for many business analyst roles.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, machine learning, and data mining. Additionally, the course will help you build a foundation in programming, which is essential for many data scientist roles.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, machine learning, and data mining. Additionally, the course will help you build a foundation in programming, which is essential for many machine learning engineer roles.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to solve business problems. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, optimization, and data mining. Additionally, the course will help you build a foundation in programming, which is essential for many operations research analyst roles.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risk for businesses. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, risk management, and data analysis. Additionally, the course will help you build a foundation in finance, which is essential for many risk analyst roles.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to make investment decisions. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, financial modeling, and data analysis. Additionally, the course will help you build a foundation in finance, which is essential for all quantitative analyst roles.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk for insurance companies. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, risk management, and data analysis. Additionally, the course will help you build a foundation in finance, which is essential for all actuary roles.
Economist
Economist
Economists use economic data to analyze and make predictions about the economy. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, econometrics, and data analysis. Additionally, the course will help you build a foundation in economics, which is essential for all economist roles.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze biological data. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, biostatistics, and data analysis. Additionally, the course will help you build a foundation in biology, which is essential for all biostatistician roles.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, data structures, and algorithms. Additionally, the course will help you build a foundation in programming, which is essential for all software engineer roles.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and inform the public. This course can help you develop the skills you need to succeed in this role, such as understanding basic statistics, data visualization, and data mining. Additionally, the course will help you build a foundation in journalism, which is essential for all data journalist roles.
For more career information including salaries, visit:
OpenCourser.com/course/p6833a/udacity
Reading list
We've selected 26 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
Intro to Descriptive Statistics.
This classic textbook provides a comprehensive overview of statistical learning methods. It covers a wide range of topics, from linear regression to support vector machines. must-read for anyone serious about learning statistical learning.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks. This book valuable resource for anyone who wants to learn how to use deep learning for a variety of tasks.
Provides a modern and practical approach to statistical learning. It covers topics such as supervised learning, unsupervised learning, and model assessment. This book is particularly useful for those interested in data science and machine learning.
Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, from supervised learning to unsupervised learning. This book valuable resource for anyone who wants to learn how to use Python for machine learning.
This comprehensive textbook covers a wide range of statistical concepts and methods, including descriptive statistics, probability theory, and inferential statistics. It valuable resource for students who want to gain a deeper understanding of the field.
Provides a comprehensive overview of machine learning using Scikit-Learn and TensorFlow. It covers a wide range of topics, from supervised learning to unsupervised learning. This book valuable resource for anyone who wants to learn how to use Scikit-Learn and TensorFlow for machine learning.
Provides a practical guide to applying data science techniques in a business context. It covers topics such as data collection, cleaning, analysis, and visualization. This book valuable resource for those interested in using data to drive business decisions.
Provides a practical guide to using Python for data science. It covers a wide range of topics, from data cleaning to data visualization. This book valuable resource for anyone who wants to learn how to use Python for data science.
Save
Provides a practical guide to using statistical software to analyze real data. It covers a wide range of topics, from data exploration to model building. This book valuable resource for anyone who wants to learn how to use statistical software effectively.
Provides a practical guide to using Python for data analysis. It covers a wide range of topics, from data cleaning to data visualization. This book valuable resource for anyone who wants to learn how to use Python for data analysis.
This textbook provides a comprehensive overview of statistical methods used in psychology, including descriptive statistics, inferential statistics, and multivariate analysis. It covers topics such as hypothesis testing, correlation analysis, and regression analysis, and includes numerous examples and exercises.
Provides a comprehensive overview of statistical methods used in psychology. It covers a wide range of topics, including descriptive statistics, inferential statistics, and multivariate analysis. This book valuable resource for both students and researchers in psychology.
Provides a comprehensive overview of probability and statistics for computer science. It covers a wide range of topics, from probability theory to statistical inference. This book valuable resource for anyone who wants to learn how to use probability and statistics to solve problems in computer science.
Save
Provides a comprehensive overview of data visualization. It covers a wide range of topics, from the basics of data visualization to advanced techniques. This book valuable resource for anyone who wants to learn how to create effective and informative data visualizations.
Provides a comprehensive overview of mathematics for machine learning. It covers a wide range of topics, from linear algebra to calculus. This book valuable resource for anyone who wants to learn the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of causal inference, a powerful statistical technique for determining cause-and-effect relationships. It covers topics such as counterfactuals, graphical models, and structural equation modeling, and includes numerous examples and exercises.
This textbook provides a comprehensive overview of econometrics, a statistical technique for analyzing economic data. It covers topics such as regression analysis, time series analysis, and panel data analysis, and includes numerous examples and exercises.
Save
This textbook provides a comprehensive overview of forecasting, a powerful statistical technique for predicting future events. It covers topics such as time series analysis, regression analysis, and machine learning, and includes numerous examples and exercises.
Save
A comprehensive textbook that provides a solid foundation in statistical concepts. It covers a wide range of topics, including data collection, analysis, and interpretation. valuable resource for both beginners and advanced learners.
This practical guide focuses on using R to implement statistical learning methods. It provides step-by-step instructions and numerous examples, making it a valuable resource for students and practitioners who want to apply statistical learning techniques to real-world problems.
This practical guide focuses on using Python libraries such as Scikit-Learn, Keras, and TensorFlow to implement machine learning algorithms. It provides step-by-step instructions and numerous examples, making it a valuable resource for students and practitioners who want to apply machine learning techniques to real-world problems.
Provides a comprehensive overview of machine learning using R, a popular programming language for data analysis. It covers topics such as supervised and unsupervised learning, model selection, and evaluation, and includes numerous examples and exercises.
Provides a practical guide to Bayesian analysis using Python, a popular programming language for data science. It covers topics such as Bayesian inference, model selection, and hierarchical models, and includes numerous examples and exercises.
Provides a theoretical foundation for machine learning, covering topics such as supervised and unsupervised learning, model selection, and optimization. It valuable resource for students and researchers who want to gain a deeper understanding of the mathematical principles underlying machine learning.
Introduces Bayesian statistics using R and Stan, a powerful probabilistic programming language. It provides a clear and accessible explanation of Bayesian concepts and methods, and includes numerous examples and exercises.
Save
Introduces Bayesian statistics, a powerful approach to data analysis that is becoming increasingly popular in various fields. While it assumes some prior knowledge of probability and statistics, it provides a clear and accessible explanation of Bayesian concepts and methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/p6833a/udacity
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Via
Udacity
Instructors
Ronald Rogers
Katie Kormanik
Sean Laraway
Good to know
Explores core concepts for those interested in a wide range of fields (Data Science, Economics, Psychology, Machine Learning, Sports analytics, etc.)
Teaches basic concepts used to describe data, building a strong foundation for beginners
Assumes understanding of basic algebra and arithmetic, making it accessible to learners with a general math background
Prerequisites advise learners to review Udacity documentation, which may require additional time and effort
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