财经类院校大二在读求指点，想申BA（整理版，求不删）

背景：TOP15  财经类211 金融专业
GPA:3.49（不考虑延毕的话就只能再刷一学期，预计最终在3.6上下）
大三Notre Dame交流一年，两学期一共可以选8门课。（毕业的时候拿到两份成绩单，不并入一份成绩）
课程：
数学：数学分析（3学期，但成绩不太理想），线性代数，概率论，数理统计（大二下）
计算机：c＋＋（大二下）
专业课：中微、中宏、公司金融、货币银行学、投资学、财务会计etc.（感觉对申请没什么帮助，但因为是必修的也没办法）
实习：
目前有一份资产评估的实习，感觉没什么卵用。疑惑：
(Q1)打算努力找一些数据类实习，但如果家里可能可以找到一些券商的实习，还有必要去吗？
(Q2)不考虑大四上课的话，可以再上三学期的课，需要补充哪些核心课程？（数学＋统计＋cs，最好稍微具体一些，救救小萌新！）
(Q3)cs需不需要去上一些学校信息学院的cs专业课（数据结构、数据库之类的），还是说学习python和r，再加上一些统计软件的课程就足够了？
(Q4)目标：能够留美就业，不管能不能抽到H1B都工作个三年，之后再考虑跳槽回国与否。毕竟研究生开销很大，如果纯粹为了镀金感觉有些不值。还有一个问题就是，申请了BA专业，以后的职业方向似乎很难再和金融接轨，是不是对于本科的学历有些浪费？
(Q5)如果在现有数学课的前提下，为了申BA提升的统计和cs背景混申MFE够不够格？
(Q6)本校非必修的金融类课程（如财务报表分析、风险管理etc...）还有没有必要去上？

对于大三在圣母上些什么课程还是有点疑惑，以下是一些比较感兴趣的课，不知道对申请有没有帮助。
Business Analytics:（在商学院下，一学期最多选1-2门，其他课程无限制）
ITAO30210 DATA ANALYSIS WITH PYTHON (3 CREDITHOURS)
It is very important in the current age ofbig data and data-driven business models to have basic skills of programming.This course introduces students to Python, a widely used programming languageamong data scientists, with the goal of cleaning, modeling, transforming andanalyzing data. Students will learn fundamentals of programming, use pythonpackages for acquiring data from various sources, learn skills to slice anddice the data and produce data visualizations. They will gain experience inPython and apply these skills in generating reproducible reports in businesscontexts. Also, this course prepares them for more advanced data science andmachine learning methods.
ITAO 30230 DATA MANAGEMENT (1.5 CREDIT HOURS)
Effective business analytics requiresdeveloping an infrastructure for collecting, managing, and retrieving data informs that are appropriate for analysis and decision making. This courseprovides an understanding of key concepts and models for data management inbusiness organizations. Topics that are introduced include relational databaseconcepts, structured query languages, data cleaning and transformationprocesses, and issues associated with the management and analysis ofunstructured data.
ITAO 30240 / MGTI 40640 DATA EXPLORATIONAND VISUALIZATION (1.5CREDIT HOURS)
Visualization techniques are increasinglyimportant for understanding what can be learned from unstructured data sets,and demand is strong for analytical skills in this area. A typical examplemight involve analyzing all tweets for a particular topic to better understandconsumer sentiment, where traditional analytical approaches face challenges notonly due to the massive scale of the data, but also because it is unstructured.Even semi-structured data, which may be described by tags or in other ways, donot conform to the standard table structures. Students in this class will gainexperience capturing, storing, and visualizing large sets of unstructured andsemi-structured data using a variety of contemporary technologies such asHadoop, PowerPivot and Tableau.
ITAO40420 MACHINE LEARNING (1.5 CREDIT HOURS)
Machine learning is the science of gettingtechnology systems to act without following prescriptive software. Most AI isunknowingly used daily by humans in their cars, homes, companies and experienceit in the infrastructure of our nation. Most think we are in the midst of a newindustrial revolution that is driven by AI software accompanied by sensors andbig data that feed the software what it needs to act. This course will teachmachine learning techniques and the application of those techniques. The coursewill cover supervised learning, unsupervised learning, best practices and AIsafety or the ethics of AI. The course will examine real life examples such asrobotic control, text understanding, medical informatics, and many other areasbeing impacted by machine learning.

Stat:
ACMS 10150. Elements of Statistics II
(3-0-3)
Prerequisite:MATH 10140. The goal of this course is to give students an introduction to avariety of the most commonly used statistical tools. A hands-on approach withreal data gathered from many disciplines will be followed. Topics includeinferences based on two samples, analysis of variance, simple linearregression, categorical data analysis, and non-parametric statistics. Thiscourse counts only as general elective credit for students in the College ofScience.
ACMS 30600. Statistical Methods & Data Analysis I
(3-0-3 Prior to Fall 2013) (3.5-0-3.5 beginning Fall 2013)
Prerequisite:ACMS 30440 OR ACMS 30530 OR MATH 30530. Introduction to statistical methodswith an emphasis on analysis of data. Estimation of central values. Parametricand nonparametric hypothesis tests. Categorical data analysis. Simple andmultiple regression. Introduction to time series. The SOA has approved this coursefor VEE credit in Applied Statistics.
ACMS 40875. Statistical Methods in Data Mining
(3-0-3)
Data mining is widely used to discover useful patterns and relationships indata. We will emphasize on large complex datasets such as those in very largedatabases or web-based mining. The topics will include data visualization,decision trees, association rules, clustering, case based methods, etc.