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Data Science Pathway

This pathway offers a blend of Statistics and Computer science. Students will build skills in statistical analysis and software development, data exploration, visualization, modeling, and model evaluation and interpretation to solve real-world problems. Students will be exposed to contemporary programming languages and cloud-based technologies that enhance data science and machine learning capabilities.

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Plan Your Degree

Disclaimer: This program map is intended ONLY as a guide for students to plan their course of study. It does NOT replace any information in the Undergraduate Catalog, which is the official guide for completing degree requirements.

Term 1: Fall

Course Name Credit Hours
C1: ENGL 1101

English Composition I

3
M: MATH 1113

Precalculus

4
T3: CS 1300

Intro to Computer Science

4
I1: Written and Oral Communications 3

Milestones:

  • Complete ENGL 1101 with a C or better
  • Complete MATH 1113 with a C or better
  • A credit from MATH 1113 and CS 1300 each count as Major Elective Credits

Term 2: Spring

Course Name Credit Hours
C2: ENGL 1102

English Composition II

3
F: MATH 1634

Calculus I

3
I2: XIDS 2002

(Recommended) First-Year Seminar

2
T1: Science + Lab 4
S2: Social Science 3

Milestones:

  • Complete ENGL 1102 with a C or better
  • Complete MATH 1634 with a C or better
  • Complete lab science

14 Fall Credit Hours + 16 Spring Credit Hours = 30 Credit Hours

Term 1: Fall

Course Name Credit Hours
P1: HIST 2111 OR 2112

US History

3
P2: POLS 1101

American Government

3
F: CS 1301

Computer Science I

4
F: MATH 2644

Calculus II

4
A: Humanities 3

Milestone:

  • Complete CS 1301 with a B or better

Term 2: Spring

Course Name Credit Hours
S1: HIST 1111 OR 1112

World History

3
MATH 4203

Mathematical Probability

3
F: MATH 2853

Elementary Linear Algebra

3
CS 1302

Computer Science II

4
A: Humanities 3

Milestones:

  • Complete CS 1302 with a B or better
  • Complete BIS Degree Plan and submit to Registrar.

17 Fall Credit Hours + 16 Spring Credit Hours = 33 Credit Hours

Term 1: Fall

Course Name Credit Hours
MATH 3003

Transition to Advanced Math

3
CS 3280

Systems Programming

3
Elective

3000/4000 level elective course

3
F: XIDS 2000 OR Elective

Intro to Interdisciplinary Studies (See note below)

3
T2: Non-lab Science 3

Milestone:

  • Finish core courses.

Note:

  • XIDS 2000 is offered in the Spring during even years and in the Fall during odd years.

 

Term 2: Spring

Course Name Credit Hours
MATH 4483

Graph Theory

3
CS 3151

Data Structures and Discrete Math I

3
CS 3270

Intelligent Systems

3
XIDS 3000

Interdisciplinary Methods

3
F: XIDS 2000 OR Elective

Intro to Interdisciplinary Studies (See note below)

3

Milestones:

  • Meet with Disciplinary Mentors about Degree Plan and Capstone Project
  • Submit plan for capstone project

Note:

  • XIDS 2000 is offered in the Spring during even years and in the Fall during odd years.

15 Fall Credit Hours + 15 Spring Credit Hours = 30 Credit Hours

Term 1: Fall

Course Name Credit Hours
MATH 4213

Mathematical Statistics

3
CS 4725

Foundations of Machine Learning

3
MATH 4803

Analysis of Variance

3
Elective

3000/4000 level elective course

3
Elective 3

Milestone:

  • Finish Capstone proposal/plan in XIDS 3000

Term 2: Spring

Course Name Credit Hours
XIDS 4000

Interdisciplinary capstone

3
MATH 4813

Regression Analysis

3
Elective 3
Elective 3

Milestone:

  • Complete Capstone Project, submit in XIDS 4000

15 Fall Credit Hours + 12 Spring Credit Hours = 27 Credit Hours

Crush Your Course

First Year:

  • Make sure to take XIDS 2000: Introduction to Interdisciplinary Studies to start your intellectual, boundary-crossing journey!
  • Discover your interests in your core classes. These can help you establish your disciplines.

Middle Years:

  • Work with your IDS professors in XIDS 3000 to establish your degree plan, including identifying your complex problem and exploring how and what disciplines can help inform your inquiry.

Last Year:

  • This is the time for your XIDS capstone! Make sure you have 9 hours of 3000-4000-level coursework for each of your two disciplines!

Find Your Place

First Year:

Middle Years:

  • Attend UWG Scholars’ Day.
  • Check out what university associations and community organizations relate to your disciplines.

Last Year:

  • Hone your leadership skills by mentoring new IDS majors!
  • Consider running for an officer position in a student organization.

Broaden Your Perspectives

First Year:

Middle Years:

Last Year:

Connect Off-Campus

First Year:

  • Visit Wolves Vote to learn about the voting process and registration.
  • Consider volunteering for a campaign or organization in your community.

Middle Years:

  • Complete an internship in your field.
  • Consider a summer or part-time job.
  • Ask your department about networking opportunities with alumni.

Last Year:

  • Ask for advice from professionals in your field of interest.
  • Explore career shadowing opportunities.

Take Care of Yourself

First Year:

Middle Years:

Last Year:

 

Pave Your Path

First Year:

Middle Years:

  • Draft your resume and attend a resume blitz.
  • Learn about how to network on social media and update your Handshake profile.
  • Draft your personal statement.
  • Visit the graduate school to find out about graduate programs and admission requirements.

Last Year:

  • Request references from professors and supervisors.
  • Draft your resume cover letter and personal statement and revise it with career services.
  • Attend business fairs and career fairs at UWG and across the state.
  • Attend an interview workshop.
  • Apply for graduate programs.

Requirements

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Degree Requirements

IDS MAJORS

All IDS majors complete an XIDS course sequence through which they learn interdisciplinary concepts and method, culminating with a capstone project that reflects their intellectual and career interests:

  • XIDS 2000 - Introduction to Interdisciplinary Studies
  • XIDS 3000 - Interdisciplinary Methods
  • XIDS 4000 - Interdisciplinary Capstone

Pathway requirements

Pathway Requirements

Courses in red are required for the Data Science Certificate

Discipline 1 - Mathematics

Foundational 1000/2000-level course (counted in area F):

  • Math 2853 (3 credits)
  • Math 2644 (4 credits)

Major Foundation Courses (6 credits):

  • Math 3003 Transition to Advanced Math
  • Math 4203 Mathematical Probability (prereq: Math 2644)

Major Required Courses (12 credits):

  • Math 4213 Mathematical Statistics (prereq: Math 4203)
  • Math 4803 Analysis of Variance (prereq: Math 4203)
  • Math 4813 Regression Analysis (prereq: Math 4203)
  • Math 4483 Graph Theory (prereq: Math 3003)

Discipline 2 - Computer Science

Foundational 1000/2000-level course (counted in area F):

  • CS 1301 Computer Science I (4 credits) [prereq: Math 1113 (>=C) OR Math 1112 (>= C)]
  • CS 1300 Intro to CS in Python (4 credits) [no prereqs]

Major Foundation Courses (4 credits):

  • CS 1302 Computer Science II (4 credits) [prereq: CS 1301, >= B]

Major Required Courses (13 credits):

  • CS 3270 Intelligent Systems [prereq: CS 1302 (>= B)]
  • CS 3280 Systems Programming [prereq: CS 1302 (>= B)]
  • CS 3151 Data Structures and Discrete Math I [prereq: CS 1302 (>= B)]
  • CS 4725 Foundations of Machine Learning [prereq: CS 3270 & 
    pre/co-requisites MATH 4203]

Suggested Courses

19 credits from other courses (including minors and electives, etc.), but must have at least 9 credits from 3000/4000 levels. Here are some suggestions.

Electives:

  • Math 4013 Numerical Analysis
  • Math 4823 Applied Experimental Design
  • Math 4833 Applied Nonparametric Statistics
  • Math 4843 Introduction to Sampling
  • CS 3152 Data Structures and Discrete Math II 
  • CS 3211 Software Engineering I
  • CS 3230 Information Management
  • CS 4225 Distributed and Cloud Computing

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