数据分析
Prepare to take on big data challenges!
数据分析 is growing rapidly across a wide range of fields including healthcare, 金融, 市场营销, 计算机科学, 生物医学研究等. You'll quickly become valued for your ability to process large amounts of data and make insights that drive 战略决策.
所有数据分析专业的学生必须学习:
- BUSI 106当代商业
- CSCI 156计算机科学1
- CSCI 157计算机科学2
- 数据库系统
- DATA 105分析视角
- 数据201分析II
- 数据可视化 & 分析
- DATA 203 Current Topics in 数据分析
- 数据401分析顶点项目
- DATA 402分析研讨会
- 经济学310应用计量经济学 & 预测分析
- 工程师1XX工程实验室
- MATH 231 Introduction to Data Science
You must take an additional 16 hours of Analytics electives.
您还需要以下艺术 & 科学课程:
- BUSI 113描述分析 and Statistics
- BUSI 213 研究 Methods for Business
- 数学151微积分I
- 英语101写作1
- 英语102写作II
- MIS 101分析学I
- 大学101共同基础
社会科学(最少1门)
人文学科(最少1门)
- Writing requirements are specified in the Catalog under General Education 需求 for Liberal 艺术 and Sciences, 基本的能力, 书面交流.
The Bachelor of Science degree is composed of analytics core courses plus analytics electives from a broad array of courses across many disciplines. You will take required and elective courses in the liberal arts to reach a minimum of 60 credits of liberal arts courses (as required for all Bachelor of Science degrees). With the flexibility built into the 数据分析 degree, you can choose to add a minor (or two) or even double-major. 你亦须:
- 保持至少2.总体平均绩点为0
- Complete one Personal 健身 course (credits not included in the 120 credits required for graduation) and one 健康 Course.
- Satisfy the University's Global Perspectives requirement
在你的第二年完成之后, we urge all 数据分析 students to gain the valuable, real-world experience only an internship can offer.
- Most internships occur during the summer months between semesters
- Each credit hour you'll earn equals 45 hours of work; you'll figure out the actual schedule with your site supervisor
- 实习 may or may not be paid, depending on your host organization
- Grades are assessed based on complete, timely submission of all required internship documentation, with the final learning report weighted most heavily
数据分析 as a double major is available to any AU student. To earn a 数据分析 double major, 参加以下课程, totalling 31 to 36 credits depending on Statistics and 研究 Methods courses selected.
- CSCI 156计算机科学1
- 数据库系统
- 数据201分析II
- 数据可视化 & 分析
- 数据203当前主题分析
- 数据401分析顶点项目 (Field Experience)
- DATA 402分析研讨会
- MIS 101分析学I
一级统计-从以下选项中选择1:
- BIOL 226, BUSI 113, ENGR 305, ENVS 205, POLS/SOCI 230, PSYC 2203
二级统计-从以下选项中选择1:
- ECON 310, MATH 231, MATH 381, PSYC 4113
研究方法-选择1从:
- BIOL 226, BUSI 213, ENVS 205, POLS/SOCI 431, PSYC 220, PSYC 4113
The minor in 数据分析 includes 28 credits (29 if you substitute a 4-credit statistics course):
- BUSI 113描述分析 & Statistics (or a 4-credit statistics course from the College of Liberal 艺术 & 科学)
- 数学151微积分I
- CSCI 156计算机科学1
- 数据201分析II
- 数据可视化 & 分析
- MIS 101分析学I
加上以下8个学分:
- ART 232视频、声音入门 & 新媒体
- ART 285 -数字媒体
- ART 332 -高级视频艺术
- ART 335 -互动媒体工作室
- ART 336 -生成 & 交互式动画
- ART 340 -网页设计 & 移动设备
- 生物地理学
- 生物信息学
- CSCI 200+ - 计算机科学 Course 200-level or higher
- 经济学310 -应用计量经济学 & 预测分析
- ENVS 212 - Introduction to Remote Sensing
- ENVS 220 -地理信息系统简介
- ENVS 301 - Contemporary Topics in Geospatial Technology
- ENVS 302 -移动 & 基于Web的GIS
- 先进的地理信息系统应用
- ENVS/GEOL 206 - Fieldcraft - Outdoor Proficiency
- ENVS/GEOL 450 -独立学习
- MATH 231 - Introduction to Data Science
You can greatly enhance your career opportunities by earning a 工商管理硕士 (MBA) degree in one year after completing your undergraduate degree.
This streamlined and competitive opportunity allows our undergraduate students to earn both their undergraduate degree and 工商管理硕士 加速格式(4+1).
Nearly 98% of our students receive some form of financial aid, mostly in the form of scholarships and grants that don't have to be paid back. We urge you to apply for financial aid, even if you're unsure if you qualify. We're committed to making your AU education affordable.
成功率
98%的毕业生 are employed full-time or entered graduate school after six months of graduation. You'll be uniquely prepared for the workforce because of our interdisciplinary education.
我该怎么办呢??
- 会计
- 财务顾问
- 供应链倡导者
- 数据架构师
- 大数据分析师
- 数据更改代理
- 应用业务分析师
- 商业智能分析师
- 数据科学家
我将在哪里工作?
- 医疗组织
- 金融机构
- 咨询公司
- 制造业
- 职业体育
- 能源
- 技术公司
- 政府
- 零售
- 移动服务
- 电信
- 信息技术
- 市场营销
- 人力资源