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Major in Data Science

Rollout plans: 

We plan to do a “soft launch” of the program in the 2013-14 academic year. An advantage of this program is that all of the lower-division courses are needed by a variety of other majors, including Math and Computer Science. The upper-division courses will likely be offered in 2014-15 at the earliest. They will also count as electives for Math and Computer Science. This means that no specialized classes that are restricted to this major will be needed. This will allow us to grow the program at its’ own pace, rather than scrambling to get students in the initial years.  

Base Curriculum (56 units):

CS 110 (Intro to Programming I)

CS 112 (Intro to Programming II)

CS 245 (Data Structures)

CS 360 (Data Visualization)

CS 451 (Data Mining)

Math 109 (Calc I)

Math 110 (Calc II)

Math 230 (Linear Algebra)

Math 201 (Discrete Math) or Math 235 (Formal Methods)

Math 345 (Mathematical Modeling)

Math 370 (Probability with Applications)

Math 371 (Statistics  with Applications) 

Econ 111 (Microeconomics) or Econ 112 (Macroeconomics) 

One of CS 490 (Senior Project) or Math 394(Applied Math Laboratory). This will serve as a capstone experience for the students. 

In addition to the core, students must choose one of the concentrations listed below.  

Concentration in computational analytics (68 total units):

Add 3 out of the 4 from CS 212 (Software Development), CS 333 (Databases), CS 430 (Numerical Analysis) 

Concentration in economic analytics (68 total units):

Add 320 (Econometrics) instead of Math 371 (Advanced Statistics and Regression)

Add one of Econ 311, 312 (Intermediate micro/macro)

Add two from: Econ 350 (Money and Banking) , Econ 450 (Monetary Economics), Econ 425 (Economics of Financial Markets)  

Concentration in mathematical analytics (68 units):

Add Math 211 (Calculus III)

Add two of: Math 340 (Differential equations), Math 422 (Combinatorics), Math 453 (Real Analysis)  

New Courses:

CS 451: Data Mining

This course provides an overview of generative and discriminiative classifiers, including decision trees, rule learners, regression models, Bayesian models, and support vector  machines. Also clustering methods such as k-means, kNN and EM. Discussion of data cleaning and data preparation issues, including noise models, missing and unbalanced data, discrete versus continuous features, and feature selection, including PCA and mutual information. Some techniques are implemented from scratch – in other cases, real-world tools such as R, Weka, or CN5.0 are used on large-scale data sets.  

Prerequisite: CS 245. 

Math 4xx. Applied Numerical Linear Algebra (To be added in coming years)

Advanced treatment of numerical algebra.  The first 2/3 of the course will be devoted to important topics  in applied numerical linear algebra  including:  Stability of Algorithms and Conditioning of Problems ,Gaussian Elimination and LU Factorization, QR Factorization, Singular Value Decomposition, Least-Squares Solutions to Linear Systems,Numerical Matrix Eigenvalue Problems,Iterative Methods for Large and Sparse Problems.  The last 1/3 of the course will be devoted to selected applications including spectral data clustering, Pagerank and other modern iterative methods, optimization, and linear programming.  

 Prerequisite: Math 230. 

Math 371: Statistics with applications

This course introduces students to theoretical issues and data-driven applications in statistics. Topics include descriptive statistics and data analysis; confidence intervals and hypothesis tests; estimation theory, linear regression, goodness-of-fit tests, and nonparametric tests.

 Prerequisite: MATH 370 or permission of instructor.  

Sample 4-year schedule:

(this is just an illustrative example) 

 

Fall

Spring

Year 1

CS110

Math 109

Core A (speaking)

Foreign Language I

CS112

Math 110

Core A (rhet/comp)

Foreign Lang II

Year 2

CS212

Math 230

Econ 111/112

Core C1

CS245

Math 201/235

Core B2

Core C2

Year 3

CS360

Math 370

Concentration 1

Core D1

Math 371

Math 345

Core D2

elective

Year 4

CS451

BSDS 490

Concentration 2

Core F

Concentration 3

Core D3

Elective

elective

(CS110 and Math 109 count as Core B1. Econ 111/112 Count as Core E.)  

4+1 with M.S. in Analytics (MSAN)

Exceptional students can also apply for  a 4+1 program that will allow them entry into the MSAN program in their fifth year. Students may apply at the end of their sophomore year; a minimum GPA of 3.1 is required to apply. Admitted students will be allowed to skip the “boot camp” portion of the MSAN program, thereby reducing the number of credits needed for that program by three.  

Learning Outcomes

Successful graduates of the BSDS program will be able to: 

L1.  think logically and analyze information critically in a mathematical setting.

L2.  reformulate and solve problems in an abstract framework.

L3.  express mathematical results verbally, working individually and in collaborative groups.

L4. Apply mathematical techniques to specific problem domains

L5. Demonstrate competence with programming concepts, including software development techniques and data structures

L6. Apply mathematical and computational techniques to real-world problems involving large, complex data sets.

L7. Visualize, present and communicate analytical results 

Curriculum Map:

Identifies courses where learning outcomes are (I) introduced, (D) developed, or (M) mastered. 

Learning Outcomes

 

L1

L2

L3

L4

L5

L6

L7

CS110

I

I

I

 

I

I

I

CS112

 

 

I

I

I

 

 

CS245

 

 

 

 

D

 

 

CS360

 

 

 

 

D

D

D

CS451

D

 

 

D

 

M

 

CS490/Math 345

M

M

M

M

M

M

M

Math 109

I

I

I

 

I

 

I

Math 110

I

I

I

 

 

 

 

Math 230

D

D

D

 

 

 

 

Math 201/235

D

D

D

 

 

 

 

Math 370

D

D

M

 

 

D

 

Math 371

D

M

M

 

 

D

 

Math 345

M

M

M

M

 

 

D

Econ 111/112

 

 

 

I