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Mat Leonard,
Parnian Barekatain,
Eddy Shyu,
Brok Bucholtz,
Elizabeth Otto Hamel,
Cindy Lin,
Cezanne Camacho,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/hi2rx2/statistic
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What's inside
Syllabus
In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
Read more
Syllabus
In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Suitable for persons hoping to strengthen prior learning in the topic
Ideal for entrants expecting an extensive review of primary statistical theory
Particularly suitable for persons interested in acquiring the foundational expertise in statistics
Appropriate for persons interested in starting or advancing a career in data science
Appropriate for political science practitioners needing statistical techniques for political data analysis
Appropriate for entrants curious about applied statistics and its use in various fields
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Reviews summary
Foundational statistics for data & decisions
According to learners, this 'Statistics' course provides a solid foundation for understanding key statistical concepts. Many found the explanations exceptionally clear and intuitive, making complex topics like hypothesis testing and Bayes' Rule accessible. The integration of Python for practical application and the A/B testing case study are frequently highlighted as highly beneficial, preparing students for real-world data analysis. While lauded as excellent for beginners, some advanced learners might find the pacing too slow or the depth insufficient for their needs, suggesting it excels as an introduction rather than an advanced deep-dive. Overall, it's a well-structured and practical introduction to statistics.
The course follows a logical progression, making learning systematic.
"The way the lessons flow from data types to hypothesis testing is very logical and easy to follow."
"Each module built upon the last, which made understanding complex ideas much simpler."
"I appreciated the clear progression of topics, which helped in grasping the overall statistical framework."
Ideal for those new to statistics, providing a comprehensive and accessible start.
"As a complete beginner, this course was perfect. It built my understanding from the ground up."
"This is definitely a great starting point for anyone who needs to learn statistics from scratch."
"I came in with no prior stats knowledge, and now I feel confident in the basics and beyond."
Strong emphasis on applying statistics, particularly with Python and A/B testing.
"The Python exercises were invaluable for seeing how statistical concepts work in practice."
"I found the A/B testing case study incredibly relevant; it gave me real insights into business applications."
"This course truly prepares you for using statistics in actual data analysis projects."
The course excels at breaking down complex statistical concepts effectively.
"The instructor made even the most complex statistical ideas easy to grasp."
"I really appreciated how clear and intuitive the explanations were for everything, especially probability."
"This course helped me understand statistics after struggling with it for years, thanks to the straightforward teaching."
Some learners found the pace too slow or the depth insufficient for advanced needs.
"While good for beginners, I found the initial modules a bit slow given my prior exposure to some concepts."
"I was hoping for more advanced topics; this course focuses heavily on the fundamentals."
"For someone with a background in data science, some sections felt a bit too basic and drawn out."
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 Statistics with these
activities:
Statistics Refresher Quiz
Show steps
Assess knowledge of basic statistics concepts to identify areas needing reinforcement.
Browse courses on
Measures of Central Tendency
Show steps
-
Take a 10-question quiz on measures of central tendency.
-
Take a 10-question quiz on measures of dispersion.
Linear Algebra Review
Show steps
Strengthen foundational understanding of linear algebra concepts to support advanced topics like regression and classification.
Browse courses on
Linear Algebra
Show steps
-
Review notes from previous linear algebra course.
-
Solve practice problems on matrix operations and vector spaces.
Crash Course in Python for Data Science
Show steps
Reinforce programming concepts by working through interactive tutorials, strengthening the foundation for data analysis.
Show steps
-
Complete the 'Introduction to Python for Data Science' module on DataCamp.
-
Watch the 'Python Tutorial for Beginners' video series on YouTube.
-
Build a simple data visualization using Python libraries.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Thinking and Deciding'
Show steps
Gain insights on decision-making and cognitive biases to enhance understanding of statistical concepts.
View
Lawyers, Judges & Semi-Rational Beasts:...
on Amazon
Show steps
-
Read Chapter 5: 'The Availability Heuristic'
-
Read Chapter 10: 'Overconfidence'
Probability Distrubution Practice
Show steps
Practice different formulas for probability distributions to solidify understanding of the concepts.
Browse courses on
Probability
Show steps
-
Review the formulas for Binomial, Normal, and Poisson distributions.
-
Solve 10 practice problems for each distribution type.
Hypothesis Testing Discussion Group
Show steps
Engage in peer discussions to clarify concepts, share insights, and reinforce understanding of hypothesis testing.
Browse courses on
Hypothesis Testing
Show steps
-
Join a study group focused on hypothesis testing.
-
Participate in weekly online or in-person meetings.
-
Lead a discussion on a specific hypothesis testing topic.
Own Clothing Brand Business Plan
Show steps
Apply statistical techniques to create a business plan for an e-commerce clothing brand, enhancing problem-solving and analytical skills.
Browse courses on
Bayes Rule
Show steps
-
Conduct market research to determine target audience.
-
Use Bayes' theorem to calculate the probability of success.
-
Create a sampling distribution to estimate revenue projections.
Statistical Literacy Poster
Show steps
Develop a visually engaging poster that explains statistical concepts in a clear and concise manner, enhancing communication and understanding.
Browse courses on
Data Visualization
Show steps
-
Choose a statistical concept to focus on.
-
Research and gather supporting data and examples.
-
Design the poster using visually appealing elements.
-
Present the poster to the class or a wider audience.
Statistics Refresher Quiz
Show steps
Assess knowledge of basic statistics concepts to identify areas needing reinforcement.
Browse courses on
Measures of Central Tendency
Show steps
Show steps
Show steps
- Take a 10-question quiz on measures of central tendency.
- Take a 10-question quiz on measures of dispersion.
Linear Algebra Review
Show steps
Strengthen foundational understanding of linear algebra concepts to support advanced topics like regression and classification.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes from previous linear algebra course.
- Solve practice problems on matrix operations and vector spaces.
Crash Course in Python for Data Science
Show steps
Reinforce programming concepts by working through interactive tutorials, strengthening the foundation for data analysis.
Show steps
Show steps
Show steps
- Complete the 'Introduction to Python for Data Science' module on DataCamp.
- Watch the 'Python Tutorial for Beginners' video series on YouTube.
- Build a simple data visualization using Python libraries.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Thinking and Deciding'
Show steps
Gain insights on decision-making and cognitive biases to enhance understanding of statistical concepts.
View
Lawyers, Judges & Semi-Rational Beasts:...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 5: 'The Availability Heuristic'
- Read Chapter 10: 'Overconfidence'
Probability Distrubution Practice
Show steps
Practice different formulas for probability distributions to solidify understanding of the concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review the formulas for Binomial, Normal, and Poisson distributions.
- Solve 10 practice problems for each distribution type.
Hypothesis Testing Discussion Group
Show steps
Engage in peer discussions to clarify concepts, share insights, and reinforce understanding of hypothesis testing.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Join a study group focused on hypothesis testing.
- Participate in weekly online or in-person meetings.
- Lead a discussion on a specific hypothesis testing topic.
Own Clothing Brand Business Plan
Show steps
Apply statistical techniques to create a business plan for an e-commerce clothing brand, enhancing problem-solving and analytical skills.
Browse courses on
Bayes Rule
Show steps
Show steps
Show steps
- Conduct market research to determine target audience.
- Use Bayes' theorem to calculate the probability of success.
- Create a sampling distribution to estimate revenue projections.
Statistical Literacy Poster
Show steps
Develop a visually engaging poster that explains statistical concepts in a clear and concise manner, enhancing communication and understanding.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a statistical concept to focus on.
- Research and gather supporting data and examples.
- Design the poster using visually appealing elements.
- Present the poster to the class or a wider audience.
Career center
Learners who complete Statistics will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst takes on a range of job duties related to collecting, cleaning, analyzing, and interpreting data. While data analyst jobs can drastically differ in focus area and responsibility based on industry, virtually all will involve statistical analysis to support decision making. The field of statistics is considered a specific subset of the larger field of data science, and as such, will give a data analyst a strong foundation for performing statistical analyses. This course, in particular, covers topics such as measures of center, spread, shape, and outliers, along with critical probability theory, inference, and hypothesis testing. Each of these skills is essential for data analysts to build, interpret, and present data-driven conclusions to stakeholders.
Statistician
Statistician
Statisticians apply statistical theory and methods to collect, analyze, interpret, and present data, whether it's to solve research questions or make predictions. As a career, statistics can be split into multiple subfields, such as applied statistics, mathematical statistics, and theoretical statistics. Regardless of subfield, a statistician will use a range of techniques to find meaningful patterns in data. This course dives into the critical foundations of statistics, providing a firm base from which to branch out in the statistics field. Concepts including data types, measures of center, spread, and shape, along with probability theory, inference, and hypothesis testing, are all necessary skills for a statistician to gather and interpret real-world data.
Data Scientist
Data Scientist
Data scientists combine knowledge of mathematics, statistics, computer science, and domain knowledge to extract meaningful insights from data. While data science is a broader field than data analytics and statistics, it often relies on statistical tools and methodologies, particularly when it comes to data cleaning, preparation, exploration, and analysis. This course provides the foundational statistical knowledge necessary for data scientists to tackle a range of data-driven problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions in the financial sector. Building models using advanced statistical techniques is a large part of quantitative analyst duties, including developing quantitative financial and risk models. This course provides a strong foundation of statistical theory and methods, which can be applied in quantitative analyst roles, such as in the development of risk management models.
Market Researcher
Market Researcher
Market researchers conduct research to gather, analyze, and interpret market data, translating it into actionable insights for a range of clients, including businesses, nonprofits, and government agencies. Market researchers use statistical methods to understand market conditions, trends, and consumer behavior. This course can provide a strong understanding of the foundational statistical theory used in market research.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of health and disease in populations. They often use statistical methods and data analysis to identify risk factors, study outbreaks, and evaluate the effectiveness of public health interventions. This course can provide epidemiologists with a solid foundation in statistical concepts and methods essential for their work.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology, medicine, and public health. They work with scientists and researchers to design studies, analyze data, and interpret results. This course provides a solid foundation in statistics for biostatisticians, covering topics such as measures of center, spread, shape, and outliers, along with probability theory, inference, and hypothesis testing.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty in a range of fields, including insurance, finance, and healthcare. The course provides a solid foundation of probability and statistics, which are essential for actuaries to assess risk.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. They often use statistical methods to analyze economic data. The course lays a foundation in probability and statistics for economists, covering data analysis, hypothesis testing, and regression analysis.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. With a solid foundation in statistics, business analysts can use their knowledge to analyze trends, identify risks, and make sound decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help software engineers build foundational knowledge in probability and statistics, which can be applied to tasks such as data analysis, quality control, and performance optimization.
Research Analyst
Research Analyst
Research Analysts collect, analyze, and interpret data for a wide range of clients and industries. This course provides a foundation in statistics that can be useful for research analysts working in fields such as market research, financial analysis, and public policy.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve problems in a range of industries, including manufacturing, logistics, and healthcare. This course provides a solid foundation in statistics for operations research analysts.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. This course can help provide data engineers with foundational knowledge in probability and statistics.
Web Analyst
Web Analyst
Web Analysts use data to improve the performance of websites and online marketing campaigns. This course can provide web analysts with a foundation in statistics, which can be useful for analyzing website traffic, user behavior, and conversion rates.
For more career information including salaries, visit:
OpenCourser.com/course/hi2rx2/statistic
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
Statistics.
Save
Provides a comprehensive introduction to deep learning, a powerful approach to machine learning that has revolutionized many fields. It valuable resource for students and researchers interested in the latest advances in deep learning.
This classic textbook covers advanced statistical methods, including machine learning techniques. It valuable resource for readers interested in exploring more complex statistical concepts.
Provides a comprehensive introduction to statistical learning methods, including linear models, regression, and classification. It valuable resource for students and practitioners interested in applying statistical learning techniques.
Provides a comprehensive introduction to probabilistic graphical models, a powerful tool for representing and reasoning about complex systems. It valuable resource for students and researchers interested in the latest advances in machine learning and AI.
Provides an introduction to Bayesian statistics, a powerful approach to statistical inference. It uses R and Stan software for practical examples, making it suitable for readers with some programming experience.
Provides a rigorous introduction to causal inference, focusing on the graphical and counterfactual frameworks. It valuable resource for researchers and students interested in understanding the principles of causality.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It covers a wide range of topics, from probability theory to advanced machine learning algorithms. It valuable resource for students and researchers interested in a deeper understanding of Bayesian methods.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for students and practitioners who want to apply machine learning techniques to real-world problems.
Provides a practical introduction to Bayesian data analysis, focusing on the application of Bayesian methods to real-world problems. It valuable resource for students and researchers who want to use Bayesian methods for statistical inference.
Save
This concise textbook provides a comprehensive overview of statistical inference, covering a wide range of topics from probability to hypothesis testing. It great resource for students and professionals who need a quick and thorough review of statistics.
This comprehensive textbook provides a thorough overview of statistical principles and their applications across different fields. It valuable resource for students seeking a deeper understanding of statistics.
This online textbook introduces statistical concepts through real-world examples and Python code. It provides an interactive and practical approach to learning statistics.
Save
Explores the fundamental concepts of causality and provides a framework for causal inference. It thought-provoking read for anyone interested in understanding how cause and effect relationships work.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hi2rx2/statistic
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Similar courses
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Time
1310 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Parnian Barekatain
Eddy Shyu
Brok Bucholtz
Elizabeth Otto Hamel
Cindy Lin
Cezanne Camacho
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
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