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STATC140

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STAT C140 - Probability for Data Science

Statistics Undergraduate CDSS - Clg of Comp Data Sci & Society

Subject

STAT

Course Number

C140

Department

Statistics

Course Level

Undergraduate

Course Title

Probability for Data Science

Formerly Known As

Statistics C140/Data Science, Undergraduate C140

Course Description

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.

Minimum Units

4

Maximum Units

4

Grading Basis

Default Letter Grade; P/NP Option

American Cultures Requirement

No

Reading and Composition Requirement

None of the Reading and Composition Requirement

Prerequisites

DATA/COMPSCI/INFO/STAT C8, or both STAT 20 and one of COMPSCI 61A or COMPSCI/DATA C88C with C- or better, or Pass; and one year of calculus at the level of MATH 1A-1B or MATH 51-52 or higher, with C- or better, or Pass. Corequisite: MATH 54, MATH 56, EECS 16B, MATH 110 or equivalent linear algebra (C- or better, or Pass, required if completed prior to enrollment in Data/Stat C140).

Repeat Rules

Course is not repeatable for credit.

Credit Restrictions

Students will receive no credit for STAT C140 after completing STAT 134, or EECS 126.

Credit Replacement Courses

-

Course Objectives

Data/Stat C140 is a probability course for Data C8 graduates who have taken more mathematics and wish to go deeper into data science. The emphasis on simulation and the bootstrap in Data C8 gives students a concrete sense of randomness and sampling variability. Data/Stat C140 capitalizes on this, abstraction and computation complementing each other throughout. Topics in statistical theory are included to allow students to proceed to modeling and statistical learning classes without taking a further semester of mathematical statistics.

Student Learning Outcomes

Work with probability concepts algebraically, numerically, and graphically Use a variety of approaches to problem solving Understand the difference between math and simulation, and appreciate the power of both

Cross-Listed Course(s)

Term

Fall and Spring

Weeks

15 weeks

Weeks

15

Lecture Hours Min

3

Lecture Hours Max

3

Lecture Mode of Instruction

In Person, Online

Discussion Hours Min

2

Discussion Hours Max

2

Discussion Mode of Instruction

In Person

Voluntary Hours Max

1

Voluntary Mode of Instruction

In Person

Outside Work Hours Min

7

Outside Work Hours Max

8

Term

Fall and Spring

Weeks

15 weeks

Weeks

15

Lecture Hours Min

3

Lecture Hours Max

3

Lecture Mode of Instruction

In Person, Online

Discussion Hours Min

1

Discussion Hours Max

1

Discussion Mode of Instruction

In Person

Voluntary Hours Max

1

Voluntary Mode of Instruction

In Person

Outside Work Hours Min

7

Outside Work Hours Max

8