Probability

Spring 2025
#24734
DATA C140 112S - SUP 112S offered through Data Science Undergraduate Studies

Probability for Data Science

Jessica Golden
Jan 21, 2025 - May 09, 2025
Fr
11:00 am - 11:59 am
Physics Building 2
Class #:24734
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - SUP 112S Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24732
DATA C140 109 - DIS 109 offered through Data Science Undergraduate Studies

Probability for Data Science

Nic Sebastian Ross
Jan 21, 2025 - May 09, 2025
We
03:00 pm - 03:59 pm
Evans 330
Class #:24732
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 109 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24731
DATA C140 108 - DIS 108 offered through Data Science Undergraduate Studies

Probability for Data Science

Vishnu Suresh
Jan 21, 2025 - May 09, 2025
We
02:00 pm - 02:59 pm
Evans 330
Class #:24731
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 108 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24728
DATA C140 106 - DIS 106 offered through Data Science Undergraduate Studies

Probability for Data Science

Fatima Guadalupe Gonzalez Perez
Jan 21, 2025 - May 09, 2025
We
01:00 pm - 01:59 pm
Evans 330
Class #:24728
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 106 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24727
DATA C140 105 - DIS 105 offered through Data Science Undergraduate Studies

Probability for Data Science

Tara Kulshrestha
Jan 21, 2025 - May 09, 2025
We
12:00 pm - 12:59 pm
Evans 334
Class #:24727
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 105 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24726
DATA C140 104 - DIS 104 offered through Data Science Undergraduate Studies

Probability for Data Science

Yash Satish Dave
Jan 21, 2025 - May 09, 2025
We
12:00 pm - 12:59 pm
Evans 330
Class #:24726
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 104 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24725
DATA C140 103 - DIS 103 offered through Data Science Undergraduate Studies

Probability for Data Science

Anwen Huang
Jan 21, 2025 - May 09, 2025
We
11:00 am - 11:59 am
Evans 330
Class #:24725
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 103 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24722
DATA C140 102 - DIS 102 offered through Data Science Undergraduate Studies

Probability for Data Science

Jessica Golden
Jan 21, 2025 - May 09, 2025
We
10:00 am - 10:59 am
Evans 330
Class #:24722
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 102 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24721
DATA C140 101 - DIS 101 offered through Data Science Undergraduate Studies

Probability for Data Science

Andrew Thein
Jan 21, 2025 - May 09, 2025
We
09:00 am - 09:59 am
Evans 330
Class #:24721
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - DIS 101 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
Spring 2025
#24712
DATA C140 001 - LEC 001 offered through Data Science Undergraduate Studies

Probability for Data Science

Michael Xiao
Jan 21, 2025 - May 09, 2025
Tu, Th
02:00 pm - 03:29 pm
Dwinelle 155
Class #:24712
Units:4

Instruction Mode: In-Person Instruction

No Open Seats
DATA C140 - LEC 001 Probability for Data Science more detail
An introduction to probability, emphasizing the combined use of mathematics and programming. Discrete and continuous families of distributions. Bounds and approximations. Transforms and convergence. Markov chains and Markov Chain Monte Carlo. Dependence, conditioning, Bayesian methods. Maximum likelihood, least squares prediction, the multivariate normal, and multiple regression. Random permutations, symmetry, and order statistics. Use of numerical computation, graphics, simulation, and computer algebra.
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