Advanced Probability: Distribution Models and Analytics
Master continuous random variables and probability distributions. Essential course for aspiring data scientists and analysts.
Master continuous random variables and probability distributions. Essential course for aspiring data scientists and analysts.
This comprehensive statistics course covers advanced probability concepts focusing on continuous random variables and major distribution models. Students learn about exponential, Gamma, Beta, and normal distributions, along with the Central Limit Theorem. The curriculum includes practical applications in data science, covering topics from probability models to transformation of random variables, preparing students for careers in information and data analysis.
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What you'll learn
Understand fundamental probability concepts and rules for continuous variables
Master widely used probability models and their applications
Apply Normal distribution and Central Limit Theorem in data analysis
Analyze complex probability scenarios using advanced statistical tools
Develop skills in transforming and analyzing random variables
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 6 modules in this course
The course provides an in-depth exploration of probability concepts and continuous random variables. Starting with fundamentals, it progresses through various distribution models, conditional probability, and advanced topics like moment generating functions. Students learn practical applications of probability theory in data science, with special emphasis on the Normal distribution and Central Limit Theorem. The curriculum combines theoretical foundations with real-world applications in data analysis.
Continuous Random Variables
Module 1 · 6 Hours to complete
Conditional Distributions and Expected Values
Module 2 · 6 Hours to complete
Models of Continuous Random Variables
Module 3 · 6 Hours to complete
Normal Distribution and Central Limit Theorem
Module 4 · 6 Hours to complete
Covariance and Probability Inequalities
Module 5 · 6 Hours to complete
Advanced Probability Topics
Module 6 · 6 Hours to complete
Instructor
Distinguished Data Science Pioneer Leading Innovation at Purdue University
Mark Daniel Ward serves as a Professor of Statistics at Purdue University, holding courtesy appointments in Agricultural & Biological Engineering, Computer Science, Mathematics, and Public Health. As Executive Director of The Data Mine, he has revolutionized data science education while maintaining an impressive research portfolio in probabilistic and combinatorial analysis of algorithms and data structures. His academic journey includes a B.S. from Denison University in Mathematics and Computer Science, an M.S. from the University of Wisconsin-Madison in Applied Mathematical Sciences, and a Ph.D. from Purdue University in Mathematics with Specialization in Computational Science. His excellence in both teaching and research has earned him numerous accolades, including Fellow of the American Statistical Association, the Focus Award, and membership in the International Statistical Institute. His research interests span across analytic combinatorics, applied probability, data compression, game theory, and information theory. Beyond his research, he has demonstrated exceptional leadership in education, winning multiple teaching awards including the College of Science Undergraduate Advising Award and being named a Fellow of the Purdue University Teaching Academy. His impact extends beyond traditional academics as he directs The Data Mine initiative, fostering large-scale computational research and data science education at Purdue
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