Data science 2 Year plan
Im planning to do my statistics & data science bachelors degree in 2 years at UT, but as an incoming freshman I am not sure if my course plan is viable, anyone got advice? My biggest concern is whether course planners will allow me to take the courses I want, and if the prerequisite statistics courses will be available over the summer. (I only need 60 in-residence hours. I did dual credit in high school so all general courses are met, 63 hours of my 83 hours are transferring)
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This is what I have so far:
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At least 21 hours of upper-division course work in Statistics and Data Sciences must be completed in residence at the university.
Mathematical and computational foundations (14 hours minimum depending on calculus sequence, including three upper division)
Calculus: Mathematics 408C and 408D; 408K, 408L, and 408M; or 408N, 408S, and 408M
Linear algebra: Mathematics 340L or 341
Introduction to programming: Computer Science 303E or 312, or an equivalent Computer Science course
Introduction to Databases: Computer Science 327E or an equivalent Computer Science course
Breadth Requirement: At least 12 hours, including at least six upper-division hours, in a single field of study other than Statistics and Data Sciences.
The following courses in Statistics and Data Sciences:
Core courses for the major:
Statistics and Data Sciences 313,~~ ~~Introduction to Data Science
Statistics and Data Sciences 315, Statistical Thinking
Statistics and Data Sciences 431, Probability and Statistical Inference
Statistics and Data Sciences 334, Intermediate Statistical Methods~~ ~~
Statistics and Data Sciences 336, Practical Machine Learning~~ ~~
Statistics and Data Sciences 354, Advanced Statistical Methods~~ ~~
Statistics and Data Sciences 357, Case Studies in Data Science
Six additional credit hours from an approved list of courses
Enough additional coursework to make a total of 120 semester hours
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Semester 1:
SDS 313 - Introduction to Data Science
CS 313E - Elements of Software Design
M427L -Advanced Calculus for Applications II
M 340L - Matrices and Matrix Calculations
SDS 320E - Elements of Statistics
16 hours
Semester 2:
SDS 315 - Statistical Thinking
CS 327E - Elements of Databases
CS 323 E - Elements of Scientific Computing
SDS 366 - Data Visualization
M362K - Probability 1
15 hours
Summer 1:
SDS 431 - Probability & Statistical Inference
4 hours
Summer 2:
SDS 334 - Intermediate Statistical Methods
3 hours
Semester 3:
SDS 336 - Practical Machine Learning
SDS 354 - Advanced Statistical Methods
SDS 357 - Case Studies in Data Science
CS 333 E - Elements of Data Integration
M358K - Applied Statistics
15 hours
Semester 4:
SDS 364 - Bayesian Statistics
SDS 368 - Statistical Theory
M349R - Applied Regression and Time Series
M339G - Predictive Analytics
M378P - Decision Analytics
15 hours