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Here's some more detail regarding UCLA's Data Theory major.

firmament2xfirmament2x 669 replies6 threads Member
This was from Miles Chen a professor in the math department from around the middle part of 2019:

The major is jointly administered by the Math Department and Statistics Department. The goal is to provide a strong foundation for students who plan on pursuing graduate studies in fields related to Data Science.

Students will have to take statistics courses that cover data analysis (Stats 101A, 101C, 102A, 102B) as well as courses that cover more advanced proof-based math like Math 115A and Math 131A. Students will learn programming in C++ (Pic 10A), R (Stats 20), and Python (Stats 21 - new course). It is a capstone major, so students will take a capstone course with a data project in their final year. A few new courses are also being developed for the major. The full requirements for completing the major is expected to be published and made public by Fall 2019.

There's a few reasons for the naming "Data Theory" rather than Data Science. Data Science rests on a foundation of math, statistics, and computer science (in addition to domain knowledge of the source of data). One could imagine another major with a stronger focus on software/computer engineering that is developed as a joint venture between Stats/CS or Math/CS departments. "Data Theory" highlights the more theoretical components of the major. That said, students in the data theory major will still get a lot of exposure to computer programming - but will probably learn less of the theoretical aspects of computer science (similar to the difference between PIC courses and CS courses.)

edited May 11
6 replies
Post edited by 10s4life on
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Replies to: Here's some more detail regarding UCLA's Data Theory major.

  • firmament2xfirmament2x 669 replies6 threads Member
    And the language by the professor is standard at UCLA (as I copy and paste):
    The major is jointly administered by the Math Department and Statistics Department. The goal is to provide a strong foundation for students who plan on pursuing graduate studies in fields related to Data Science.

    The University wants to convey that the major will set someone up for graduate studies if she/he desires, not that the main or only focus is to go for graduate studies.
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  • firmament2xfirmament2x 669 replies6 threads Member
    I just wanted to add that there's also a Python class in the Program In Computing ("PIC") courses 16, which has been there for awhile longer than the one Dr. Chen mentioned in his blurb, as well as Java courses in the three-course PIC 20 series.

    Here's a list of the PIC courses,

    https://www.math.ucla.edu/ugrad/pic-course-descriptions

    which can be attached to one's degree as a Specialization in Computing, which just about any major can include, with exceptions being the Computer Science major, Statistics, and Data Theory, because, of course, most of their computer courses are self-contained.

    There are computationally based majors for various disciplines: Computational and Systems Biology leading to an MS (Life Sciences Department), Linguistics and Computer Science (Linguistics Department), and Mathematics of Computation (Math Dept.).

    And there are minors in Digital Humanities, available to a large cross-section of majors; Bioinformatics, for life-science majors, and GIS&T through Geography.
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  • firmament2xfirmament2x 669 replies6 threads Member
    Here's the missing link (one that is extant) from my post above from which I took the information:

    https://catalog.registrar.ucla.edu/ucla-catalog19-20-1360.html

    At some point, hopefully soon, I'll parse the list for course 22 in my post labeled above as "List 2" and try to remove the courses that don't belong -- it appears that there are a few the University doesn't offer anymore within the Stats department.

    There are also a handful of courses that are not offered now because the first cohort for Data Theory ("DT") having entered in 2019 as freshmen hasn't reached the level to be able to take them. As far as transfers are concerned, I didn't see any who were listed as DT majors for the cohort entering in 2019 -- unless they were listed under Statistics majors, so it'll be interesting to see (at least for me) what the numbers are for 2020. Perhaps the Math department is holding off until 2021 for transfers so those who entered as frosh in 2019 catch up with those who transfer in.
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  • firmament2xfirmament2x 669 replies6 threads Member
    I redid the following schedule because the prior one was more jumbled and confusing.

    UCLA Data Theory ("DT") Major – Minimum Total of Units needed to complete DT major:
    ……GE Requirements…………....…..48
    ……Prep for Major……………........38-39
    ……Major Requirements……...51-60
    ……Total……………………...................137-147, with 33-43 min. additional units needed to be filled towards the 180 units needed to graduate. This can be accomplished by delving more into the DT major or by adding another major or minor. Unit caps are stated as 208-216, which might be enforced for Letters and Sciences, but not in engineering. I don’t know if there is a program for DT as in engineering (with a 3.5 min. gpa), whereby one can obtain a masters in DT by taking an additional year of courses.

    GE Requirements, 48 units min., 10 Courses. Here are two Links:

    Courses Needed : http://catalog.registrar.ucla.edu/ucla-catalog19-20-149.html

    Master List: https://sa.ucla.edu/ro/Public/SOC/Search/GECoursesMasterList

    There are 25 courses needed to complete a degree in DT

    Here’s a link to the DT major with requirements:

    https://catalog.registrar.ucla.edu/ucla-catalog19-20-1360.html

    Here are the links related to these courses (lower division classes are numbered < 100, and upper is ≥ 100):

    -- Mathematics and Program In Computing Undergraduate Course Landing, the prerequisite courses and the links to each class which gives a sample syllabus, a very detailed course description, and when course is offered:

    https://www.math.ucla.edu/ugrad/courses

    -- Here are the Math Courses, Descriptions and Prerequisites (without having to go to an individual link provided for each class, with less detail in description):

    https://www.registrar.ucla.edu/Academics/Course-Descriptions/Course-Details?SA=MATH&funsel=3

    -- Here are the Statistics Course Descriptions and Prerequisites:

    https://www.registrar.ucla.edu/Academics/Course-Descriptions/Course-Details?SA=STATS&funsel=3

    Here are the 25 requisite courses for the DT major:

    1.) Math 31A - Differential & Integral Calculus, 4.0

    2.) Math 31B - Integration & Infinite Series, 4.0

    3.) Math 32A - Calculus of Several Variables, 4.0

    4.) Math 32B - Calculus of Several Variables, 4.0

    5.) Math 33A - Linear Algebra & Apps., 4.0

    …....Math 33B - Differential Equations, 4.0, Not Required

    6.) Math 115A - Linear Algebra, 5.0 (Req. Math 33A)

    7.) PIC 10A - Intro. Programming, 5.0 (If no programming experience, take PIC 1, not sure if they still offer this course)

    8.) One from following:
    ……Stats 10 - Intro. Stats Reasoning, 5.0
    ……Stats 12 - Intro. Stats Methods for Geog. & Environ. Studies, 5.0
    ……Stats 13 - Intro. Stats Methods for Life Sciences, 5.0
    ……Stats 15 - Intro. Data Science, 5.0
    ……Stats 20 - Intro. Stats Programming with R, 4.0
    ……Stats 21 - Python & Other Technologies for Data Science, 4.0

    9.) Math 42 - Intro. Data-driven Math Modeling: Life, Universe, Everything, 4.0 (Req. .......Math 31A-33A; Stats 10, 12, or 13; PIC 10)

    Major Requirements (See Course Description for additional prereqs), 51-60 Units:

    10.) Math 118 – Math Methods of Data Theory, 4.0

    11.) Math 131A – Analysis, 4.0

    12.) Math 156 – Machine Learning, 4.0

    13.) Stats 101A – Intro. Data Analysis & Regression, 4.0

    14.) Stats 102A – Intro. Computational Statistics with R, 4.0

    15.) Stats 102B – Intro. Comp. and Optim. for Statistics, 4.0 (Req. Stats 102A)

    16.) Stats 101C – Intro. Stats Models & Data Mining, 4.0 (Req. Stats 101B, conflict?)

    17.) Stats 147 – Data Technologies for Data Scientists, 2.0 (Req. Stats 101B, Math .................................170S, 101A, 101C or 156)

    18.) Stats 184 – Societal Impacts of Data, 2.0 (Req. Stats 101B, Math 170S, 101A, ...................................101C or 156)

    19.) Math 170E – Intro. to Probability & Statistics 1, 4.0
    ……..or
    19.) Math 100A – Intro. to Probability, 4.0

    20.) Math 170S – Intro. to Probability & Statistics 2, 4.0
    ……....or
    20.) Math 100B – Intro. to Math Statistics, 4.0

    21.) Select 1:
    ……..List 1
    ……..Math 151A - Applied Numerical Methods, 4.0
    ……..Math 151B - Applied Numerical Methods, 4.0
    ……..Math 164 - Optimization, 4.0 (Req. Math 131A)
    ……..Math 168 - Introduction to Networks, 4.0 (Req. Math 170E)
    ……..Math 171 - Stochastic Processes, 4.0 (Req. Math 170E or 170A)
    ……..Math 174E - Mathematics of Finance for Mathematics/Economics ....................................Students, 4.0
    ……..Math 178A - Foundations of Actuarial Mathematics: Life Insurance and ....................................Annuities, 4.0
    ……..Math 178B - Foundations of Actuarial Mathematics: Additional Topics in .....................................Long-Term Actuarial Mathematics, 4.0
    ……..Math 178C - Foundations of Actuarial Mathematics: Loss Models, 4.0
    ……..Math 179 - Advanced Topic in Financial Mathematics, 4.0
    ……..Math 182 - Algorithms, 4.0

    22.) Select one:
    ……..List 2:
    ……..Stats 100C - Linear Models, 4.0
    ……..Stats 101B - Introduction to Design and Analysis of Experiment, 4.0
    ……..Stats 102C - Introduction to Monte Carlo Methods, 4.0
    ……..Stats C151 - Experimental Design, 4.0
    ……..Stats M154 - Measurement and Its Applications, 4.0
    ……..Stats C155 - Applied Sampling, 4.0
    ……..Stats 157 - Probability and Statistics Data Modeling and Analysis using .................................Statistics Online Computational Resource, 4.0
    ……..Stats C161 - Introduction to Pattern Recognition and Machine Learning, ...................................4.0
    ……..Stats 170 - Introduction to Time-Series Analysis, 4.0
    ……..Stats M171 - Introduction to Spatial Statistics, 4.0
    ……..Stats C173 - Applied Geostatistics, 4.0
    ……..Stats 175 - Statistics for Spatial Data, 4.0
    ………Stats C180 - Introduction to Bayesian Statistics, 4.0
    ………Stats C183 - Statistical Models in Finance, 4.0
    ………Stats 184 - Societal Impacts of Data, 2.0
    ………Stats 188SA - Individual Studies for USIE Facilitators, 1.0
    ………Stats 188SB - Individual Studies for USIE Facilitators, 1.0
    ………Stats 188SC - Individual Studies for USIE Facilitators, 2.0
    ………Stats 195 - Community or Corporate Internships in Statistics, 4.0
    ………Stats 199 - Directed Research in Statistics, 1.0 to 4.0

    23.) One Course from List 1 or 2

    24.) One Course from List 1 or 2

    25.) Capstone Course: Math M148 – Experience of Data Science, 4.0
    ……..or
    25.) Capstone Course: Stats M148 – Experience of Data Science, 4.0
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  • firmament2xfirmament2x 669 replies6 threads Member
    Some additional notes:

    I didn’t include the following classes from Math which don’t appear to be offered anymore:

    …..Math 172B: Actuarial Models I

    …..Math 172C: Actuarial Models II

    …..Math 173A: Casualty Models I

    …..Math 173B: Casualty Models II

    …..Math 175: Introduction to Financial Math (**Possibly still offered, because they offer a more advanced financial math class)

    …..Math 176: Speech Communications for Actuarial Students

    I think this followed from a few years back when math deemphasized the Actuarial major, even though it still offers the Financial Actuarial Mathematics major. I didn’t see these in the class schedule for 2019-2020.

    - UCLA used to offer Stats from within the Math Department, but it broke off Stats into its own category. You’ll be taking Data Theory core classes from Math or Stats unless they break off Data Theory from these two.

    - Let’s see what Dr. Chen had to say again:
    Students will have to take statistics courses that cover data analysis (Stats 101A, 101C, 102A, 102B) as well as courses that cover more advanced proof-based math like Math 115A and Math 131A. Students will learn programming in C++ (Pic 10A), R (Stats 20), and Python (Stats 21 - new course). It is a capstone major, so students will take a capstone course with a data project in their final year. A few new courses are also being developed for the major. The full requirements for completing the major is expected to be published and made public by Fall 2019.

    There's a few reasons for the naming "Data Theory" rather than Data Science. Data Science rests on a foundation of math, statistics, and computer science (in addition to domain knowledge of the source of data). One could imagine another major with a stronger focus on software/computer engineering that is developed as a joint venture between Stats/CS or Math/CS departments. "Data Theory" highlights the more theoretical components of the major. That said, students in the data theory major will still get a lot of exposure to computer programming - but will probably learn less of the theoretical aspects of computer science (similar to the difference between PIC courses and CS courses.)

    So you have Data Analysis Courses:

    Stats 101A: Introduction to Data Analysis and Regression

    Stats 101C: Introduction to Statistical Models and Data Mining

    Stats 102A: Introduction to Computational Statistics with R

    Stats 102B: Introduction to Computation and Optimization for Statistics

    Then you have advanced-proof based math:

    Math 115A: Linear Algebra – Techniques of proof, abstract vector spaces, linear transformations, and matrices; determinants; inner product spaces; eigenvector theory. P/NP or letter grading. (A lot of majors at UCLA require this course, even among computational and Economics-related majors.)

    Math 131A: Analysis - Rigorous introduction to foundations of real analysis; real numbers, point set topology in Euclidean space, functions, continuity. P/NP or letter grading.

    These courses might ensure that you wouldn’t have to take courses with undergrads if you entered an MS in Data Science program. I believe this is what Dr. Chen meant when he said the following:
    The goal is to provide a strong foundation for students who plan on pursuing graduate studies in fields related to Data Science.

    Again, not that UCLA wouldn't prepare a DT major for the workplace.

    And you have the Programming Courses:

    PIC 10A: Introduction to Programming, C++

    Stats 20: Introduction to Statistical Programming with R

    Stats 21: Python and Other Technologies for Data Science

    Unless there’s a restriction on DT majors, you can probably take the Specialization in Computing coursework. Here’s the link to this program:

    https://www.math.ucla.edu/ugrad/majors/speccomp

    There’s a three-course PIC series involving Java, 20A, 20B, 20C. And as seen above, there are Math and Stats courses involving programming.

    Major Cutoffs: 3.30 gpa in premajor gets one in automatically, but a 2.70 is the minimum. Those with between a 2.70 and 3.30 will be admitted “if space is available.” For transfers, it essentially looks like a 3.30 is needed.

    There is a capstone within the DT major, a senior project, which more majors at UCLA are requiring.

    Here’s what Math M148 states:

    Students solve real data science problems for community- or campus-based clients. Students work in small groups with faculty member and client to frame client's question in data science terms, create mathematical models, analyze data, and report results. Students may elect to undertake research on foundations of data science, studying advanced topics and writing senior thesis with discussion of findings or survey of literature on chosen foundational topic. Development of collaborative skills, communication principles, and discussion of ethical issues. Letter grading.

    I hope this gives you at least a basis of what to expect from the DT major at UCLA. In any case your counselor/advisor or maybe your talks with your math professors would be best with whom to consult. Sounds like a great major.
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  • firmament2xfirmament2x 669 replies6 threads Member
    Sorry I sloughed over the non-DT courses as I wanted to detail mainly the DT major. But for Letters and Science, there are also: the Writing, the Foreign Language, the Diversity, and the Quantitative Reasoning Requirements within L&S.

    Writing Requirements, Letters and Science

    If one doesn’t qualify for bypassing Writing I, the student will have to take English Composition 3, 5.0 units. Writing II encompasses a list of courses to take which are below. One can satisfy this requirement within the GEs by taking the Freshmen Clusters, but there are also some interesting courses for WII outside of the Clusters and GEs.

    Here’s a link to an algorithm to see if one can bypass WI and the reqs:

    https://www.registrar.ucla.edu/Academics/Writing-II-Requirement

    Here’s a link for the WII courses, along with whether it would satisfy a GE:

    https://sa.ucla.edu/ro/Public/SOC/Search/WritingtwoMasterList

    If you wanted to step up your game by taking say a scientific or business writing class here’s a list of Composition classes, with upper division having some really specialized classes:

    https://www.registrar.ucla.edu/Academics/Course-Descriptions/Course-Details?SA=ENGCOMP&funsel=3

    Diversity Requirement

    There’s a Stats class, 112, 5.0 units – Windows to Underserved Diversity, that satisfies the requirement. Here’s the master list:

    https://sa.ucla.edu/ro/Public/SOC/Search/DiversityCoursesMasterList

    Language Requirements

    Here is the master list of languages that fulfill UCLA’s L&S requirements in case not fulfilled in high school or by examination:

    https://www.registrar.ucla.edu/Academics/Foreign-Language-Requirement

    Quantitative Reasoning Requirement

    Satisfied by majoring in DT.

    Freshman Clusters

    As stated above the Clusters will satisfy many of the GEs, possibly the Diversity requirement, and possibly the writing reqs.
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