Probability

2025 Fall
#26529
DATA C140 114S - SUP 114S offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
Fr
01:00 pm - 01:59 pm
Stanley 106
Class #:26529
Units:4

Instruction Mode: In-Person Instruction

Open Seats

51 Unreserved Seats

DATA C140 - SUP 114S 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.
2025 Fall
#26326
DATA C140 113S - SUP 113S offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
Fr
12:00 pm - 12:59 pm
Stanley 106
Class #:26326
Units:4

Instruction Mode: In-Person Instruction

Open Seats

43 Unreserved Seats

DATA C140 - SUP 113S 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.
2025 Fall
#26325
DATA C140 112S - SUP 112S offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
Fr
11:00 am - 11:59 am
Valley Life Sciences 2040
Class #:26325
Units:4

Instruction Mode: In-Person Instruction

Open Seats

32 Unreserved 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.
2025 Fall
#24919
DATA C140 109 - DIS 109 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
03:00 pm - 03:59 pm
Evans 330
Class #:24919
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.
2025 Fall
#24918
DATA C140 108 - DIS 108 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
02:00 pm - 02:59 pm
Evans 330
Class #:24918
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.
2025 Fall
#24916
DATA C140 106 - DIS 106 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
01:00 pm - 01:59 pm
Evans 330
Class #:24916
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.
2025 Fall
#24915
DATA C140 105 - DIS 105 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
12:00 pm - 12:59 pm
Evans 332
Class #:24915
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.
2025 Fall
#24914
DATA C140 104 - DIS 104 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
12:00 pm - 12:59 pm
Evans 330
Class #:24914
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.
2025 Fall
#24913
DATA C140 103 - DIS 103 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
11:00 am - 11:59 am
Evans 330
Class #:24913
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.
2025 Fall
#24912
DATA C140 102 - DIS 102 offered through Data Science Undergraduate Studies

Probability for Data Science

Aug 27, 2025 - Dec 12, 2025
We
10:00 am - 10:59 am
Evans 330
Class #:24912
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.
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