MathTrackX: Probability
Master probability concepts and their real-world applications in this 4-week course from AdelaideX, covering discrete and continuous random variables.
Master probability concepts and their real-world applications in this 4-week course from AdelaideX, covering discrete and continuous random variables.
This comprehensive course, part of the MathTrackX XSeries Program, provides a solid foundation in probability fundamentals. Students explore discrete and continuous random variables, their applications in modeling random processes, and the framework for statistical inference. Led by experts from the University of Adelaide's School of Mathematics and Maths Learning Centre, participants develop practical skills in calculating probabilities, expected values, and standard deviations. The course emphasizes real-world applications and problem-solving, making complex probability concepts accessible and applicable.
Instructors:
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What you'll learn
Calculate and interpret probabilities in various real-world contexts
Differentiate between discrete and continuous random variables
Compute expected values variance and standard deviation of random variables
Analyze the effects of linear changes on statistical measures
Calculate quantiles of normal distribution
Apply probability concepts to solve everyday scenarios
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This comprehensive probability course covers fundamental concepts including discrete and continuous random variables, probability calculations, expected values, variance, and standard deviation. Students learn to interpret probabilities in various contexts, understand the differences between variable types, and apply mathematical concepts to real-world scenarios. The course emphasizes practical applications and problem-solving skills, featuring instruction from University of Adelaide experts.
Basic probability
Module 1
Discrete random variables
Module 2
Continuous random variables
Module 3
Assessment
Module 4
Fee Structure
Instructors
7 Courses
Distinguished Mathematics Educator and Learning Innovation Expert
David Butler serves as Lecturer and Coordinator of the Maths Learning Centre and Writing Centre at the University of Adelaide, where he specializes in helping students develop effective mathematical learning strategies across all disciplines. His academic background combines pure mathematics, with a PhD in Finite Geometry, and educational expertise through a Graduate Diploma in Education. As a former high school mathematics teacher, he brings practical pedagogical experience to his university role. His innovative approach emphasizes the integration of play and artistic elements in mathematical education, making complex concepts more accessible to students. At the Maths Learning Centre, he focuses on developing students' mathematical thinking skills and confidence, working across diverse academic disciplines to support students in mastering course-specific mathematical concepts. His passion for mathematics education extends beyond the classroom through his active engagement in mathematical education communities and his development of creative learning resources.
5 Courses
A Pioneering Statistician Bridging Applied Mathematics and Forensic Science
Melissa Humphries serves as Senior Lecturer in Statistics at the University of Adelaide, where she combines her unique background as a former chef with expertise in statistical analysis and cognitive modeling. After completing her PhD in Statistics and Mathematical Psychology from the University of Tasmania in 2017, she has built an impressive career spanning teaching and research. Her work focuses on applied statistics across diverse fields including forensic science, defense, psychology, and wastewater analysis, with particular emphasis on developing tools that support expert decision-making. As a lecturer, she coordinates multiple courses including second-year undergraduate statistics and has helped develop innovative online learning units in Bayesian reasoning. Her teaching portfolio includes Data Literacy, Statistical Analysis and Modeling, and Research Methods and Statistics. Recently named a Superstar of STEM for 2023-24, she advocates for making academia more accessible while maintaining an active research program in Bayesian inference, stochastic processes, and large dataset management. Her expertise extends to analyzing spatially and temporally autocorrelated data, contributing to fields ranging from astrophysics to forensic anthropology, while her background in psychology enhances her ability to communicate complex technical concepts to diverse audiences.
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