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Nicholas Crouch,
Dr. Brendan Harding,
Dr. Melissa Humphries,
Dr. David Butler,
and
Dr. Danny Stevenson
This course is part five of the MathTrackX XSeries Program which has been designed to provide you with a solid foundation in mathematical fundamentals and how they can be applied in the real world.
Read more
This course is part five of the MathTrackX XSeries Program which has been designed to provide you with a solid foundation in mathematical fundamentals and how they can be applied in the real world.
Read more
This course is part five of the MathTrackX XSeries Program which has been designed to provide you with a solid foundation in mathematical fundamentals and how they can be applied in the real world.
This course introduces probability and how it manifests in the world around us. Beginning with discrete random variables, together with their uses in modelling random processes involving chance and variation, you will start to uncover the framework for statistical inference.
Guided by experts from the School of Mathematics and the Maths Learning Centre at the University of Adelaide, this course will introduce discrete and continuous random variables and their applications in a variety of contexts.
Join us as we provide opportunities to develop your skills and confidence in applying mathematics to solve real world problems.
Register for this course and see more details by visiting:
OpenCourser.com/course/vdmlsk/mathtrackx
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What's inside
Learning objectives
- How to understand and interpret probabilities depending on the context
- The difference between a discrete random variable and a continuous random variable
- How to calculate probabilities for a range of everyday scenarios
- How to calculate the expected value, variance and standard deviation of random variables
- The effects of linear changes of scale and origin on the mean and the standard deviation
- How to calculate quantiles of normal distribution.
- How to understand and interpret probabilities depending on the context
- The difference between a discrete random variable and a continuous random variable
- How to calculate probabilities for a range of everyday scenarios
- How to calculate the expected value, variance and standard deviation of random variables
- The effects of linear changes of scale and origin on the mean and the standard deviation
- How to calculate quantiles of normal distribution.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces probability and its applications, fostering a foundation for statistical inference
Offered by instructors from the University of Adelaide, known for their expertise in mathematics
Covers discrete and continuous random variables, providing a comprehensive exploration of probability
Explores the calculation of probabilities, expected values, variance, and standard deviation
Suitable for learners seeking to strengthen their foundation in probability and its applications
Save this course
Save MathTrackX: Probability to your list so you can find it easily later:
Save
Save
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 MathTrackX: Probability with these
activities:
Practice Probability Concepts
Show steps
Review foundational probability concepts to strengthen your understanding of the course material.
Browse courses on
Probability
Show steps
-
Revisit key terms and definitions related to probability, such as sample space, events, and probability distributions.
-
Solve practice problems involving discrete random variables, such as binomial and geometric distributions.
-
Calculate probabilities using conditional probability and Bayes' theorem.
Calculate Probabilities and Distributions
Show steps
Engage in repetitive exercises to reinforce your understanding of probability calculations and distributions.
Browse courses on
Probability Distributions
Show steps
-
Solve numerous practice problems involving discrete and continuous random variables.
-
Calculate expected values, variances, and standard deviations of random variables.
-
Analyze data sets and determine the appropriate probability distribution to model the data.
Learn Probability Software
Show steps
Enhance your understanding of probability by utilizing specialized software that facilitates calculations and visualizations.
Browse courses on
Statistical Software
Show steps
-
Identify and research probability software options.
-
Follow tutorials and documentation to learn the software's features.
-
Apply the software to solve probability-related problems.
Three other activities
Expand to see all activities and additional details
Show all six activities
Discuss Probability Concepts with Peers
Show steps
Engage with classmates to discuss probability concepts and deepen your understanding through diverse perspectives.
Show steps
-
Join or create a study group with peers.
-
Prepare questions and topics related to probability concepts.
-
Engage in active discussions, sharing insights and perspectives.
Explore Applications of Probability
Show steps
Delve into real-world scenarios where probability plays a role to enhance your comprehension.
Show steps
-
Find online tutorials or articles that demonstrate the use of probability in fields such as finance, medicine, or engineering.
-
Identify the different types of probability distributions used in these applications.
-
Apply probability concepts to solve practical problems.
Develop a Probability Model
Show steps
Apply your probability skills to create a mathematical model that represents a real-world phenomenon.
Browse courses on
Data Analysis
Show steps
-
Identify a problem or scenario where probability can be applied.
-
Research and gather data related to the problem.
-
Develop a probability model that fits the data and makes predictions.
-
Evaluate the accuracy and validity of the model.
Practice Probability Concepts
Show steps
Review foundational probability concepts to strengthen your understanding of the course material.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Revisit key terms and definitions related to probability, such as sample space, events, and probability distributions.
- Solve practice problems involving discrete random variables, such as binomial and geometric distributions.
- Calculate probabilities using conditional probability and Bayes' theorem.
Calculate Probabilities and Distributions
Show steps
Engage in repetitive exercises to reinforce your understanding of probability calculations and distributions.
Browse courses on
Probability Distributions
Show steps
Show steps
Show steps
- Solve numerous practice problems involving discrete and continuous random variables.
- Calculate expected values, variances, and standard deviations of random variables.
- Analyze data sets and determine the appropriate probability distribution to model the data.
Learn Probability Software
Show steps
Enhance your understanding of probability by utilizing specialized software that facilitates calculations and visualizations.
Browse courses on
Statistical Software
Show steps
Show steps
Show steps
- Identify and research probability software options.
- Follow tutorials and documentation to learn the software's features.
- Apply the software to solve probability-related problems.
Three other activities
Expand to see all activities and additional details
Show all six activities
Discuss Probability Concepts with Peers
Show steps
Engage with classmates to discuss probability concepts and deepen your understanding through diverse perspectives.
Show steps
Show steps
Show steps
- Join or create a study group with peers.
- Prepare questions and topics related to probability concepts.
- Engage in active discussions, sharing insights and perspectives.
Explore Applications of Probability
Show steps
Delve into real-world scenarios where probability plays a role to enhance your comprehension.
Show steps
Show steps
Show steps
- Find online tutorials or articles that demonstrate the use of probability in fields such as finance, medicine, or engineering.
- Identify the different types of probability distributions used in these applications.
- Apply probability concepts to solve practical problems.
Develop a Probability Model
Show steps
Apply your probability skills to create a mathematical model that represents a real-world phenomenon.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a problem or scenario where probability can be applied.
- Research and gather data related to the problem.
- Develop a probability model that fits the data and makes predictions.
- Evaluate the accuracy and validity of the model.
Career center
Learners who complete MathTrackX: Probability will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians use probability and statistics to collect, analyze, and interpret data. They may also develop statistical models or design experiments to help solve problems. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Data Analyst
Data Analyst
Data analysts use probability and statistics to analyze data and identify trends. They may also forecast future events or make recommendations based on their analysis. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Medical Statistician
Medical Statistician
Medical statisticians use probability and statistics to design and analyze clinical trials. They may also develop statistical models or advise researchers on how to interpret their data.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use probability and statistics to develop and implement trading strategies. They may also be called upon to design risk management systems or to advise clients on investment decisions. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Risk Manager
Risk Manager
Risk managers use probability and statistics to identify, assess, and manage risks. They may also develop risk management plans or advise clients on how to reduce their risks. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Biostatistician
Biostatistician
Biostatisticians use probability and statistics to design and analyze studies in the medical field. They may also develop statistical models or advise researchers on how to interpret their data. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use probability and statistics to analyze and improve the efficiency of business processes. They may also develop mathematical models or simulations to help solve business problems. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Actuary
Actuary
Actuaries use probability and statistics to help insurance companies evaluate the riskiness of different types of policies. They may also be called upon to design new policies or to advise clients on how to manage their risks. This course would help build a foundation in probability for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Epidemiologist
Epidemiologist
Epidemiologists use probability and statistics to study the distribution and causes of disease. They may also develop public health interventions or advise policymakers on how to prevent disease.
Underwriter
Underwriter
Underwriters use probability and statistics to assess the riskiness of insurance policies. They may also be called upon to design new policies or to advise clients on how to manage their risks. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Computational Biologist
Computational Biologist
Computational biologists use probability and statistics to analyze biological data. They may also develop computational models or simulations to help solve biological problems. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Financial Analyst
Financial Analyst
Financial analysts use probability and statistics to analyze financial data and make investment recommendations. They may also develop financial models or advise clients on how to manage their investments. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Market Researcher
Market Researcher
Market researchers use probability and statistics to design and conduct surveys and experiments. They may also analyze data to identify trends or make predictions about consumer behavior. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Psychologist
Psychologist
Psychologists use probability and statistics to design and analyze experiments in the field of psychology. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
Teacher
Teacher
Teachers use probability and statistics to teach students about mathematics. They may also develop lesson plans or create teaching materials. This course would help build a foundation in probability and statistics for this role. It will introduce discrete and continuous random variables and their applications in a variety of contexts.
For more career information including salaries, visit:
OpenCourser.com/course/vdmlsk/mathtrackx
Reading list
We've selected 14 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
MathTrackX: Probability.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in engineering and science. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, estimation, and hypothesis testing.
Provides a comprehensive introduction to mathematical statistics, with a focus on applications in a variety of fields. It covers a wide range of topics, including probability, random variables, estimation, and hypothesis testing.
Provides a clear and concise introduction to probability, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayes' theorem.
Provides a unique and thought-provoking introduction to probability, with a focus on the foundations of the subject. It covers a wide range of topics, including the history of probability, the axioms of probability, and Bayesian inference.
Save
Provides a rigorous and in-depth introduction to the theory of probability. It covers a wide range of topics, including the foundations of probability, the axioms of probability, and Bayesian inference.
Provides a clear and concise introduction to probability and statistics, with a focus on applications in computer science. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
Provides a comprehensive introduction to probability and statistical inference, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and hypothesis testing.
Save
Provides a clear and concise introduction to probability and applied statistics, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and hypothesis testing.
Provides a comprehensive introduction to probability and risk analysis, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
Provides a comprehensive introduction to probability and social science, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
Provides a comprehensive introduction to probability and finance, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
Provides a comprehensive introduction to probability and machine learning, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
Provides a comprehensive introduction to probability and natural language processing, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
Save
Provides a comprehensive introduction to probability and image processing, with a focus on applications in a variety of fields. It covers a wide range of topics, including discrete and continuous random variables, probability distributions, and Bayesian inference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vdmlsk/mathtrackx
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Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
University of Adelaide
Instructors
Nicholas Crouch
Dr. Brendan Harding
Dr. Melissa Humphries
Dr. David Butler
Dr. Danny Stevenson
Language
English
Transcript
English
This course belongs to a
collection
called
MathTrackX. It's comprised of six courses:
Good to know
Introduces probability and its applications, fostering a foundation for statistical inference
Offered by instructors from the University of Adelaide, known for their expertise in mathematics
Covers discrete and continuous random variables, providing a comprehensive exploration of probability
Explores the calculation of probabilities, expected values, variance, and standard deviation
Suitable for learners seeking to strengthen their foundation in probability and its applications
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