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ANALYTICSAPPLYING TO OVERSEAS GRADUATE PROGRAMS IN
by Jimmy Chen and Joseph Li
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Introduction 7 Our background and experience 8
Intended audience 8
Part 1: Career planning 9 Reasons for studying abroad 9
1. Remain abroad to work 9
2. Transition from a non-analytics major 10
No target schools for analytics roles 11
保研 VS 考研 VS 留学 12
A breakdown of the different data-related roles/functions 13
BA vs. DS vs. PhD 14
More on industries and employers of BA/DS 15
A typical day for a junior data science professional (foreign tech firm) 16
A typical day for a junior data science professional (Chinese 996 firm) 17
A typical analytical framework for a data science professional 18
Several sample career paths 19
Part 2: School Selection 20 US vs. non-US 20
留美 tier list 21
回国 tier list 21
Other Non-BA Programs in US to consider 22
AdditionAl Information for Tier 1 programs 23
MIT MBAN 23
UCLA MSBA 24
CMU MSIM-BIDA 25
USC MSBA 26
Emory MSBA 27
UMN MSBA 28
Columbia MSBA 29
Duke MQM 30
UCD MSBA 31
IC BA 32
LBS MAM 33
Sample school selection cases 34
1. Top 985 High GPA Business Major & Overseas Undergrad high GPA 34
2. Good 985 & 两财⼀贸 Mid-High GPA 35
3. Good 211 & Non-Target 985 Mid-High-GPA 36
4. ⾮ 211/985 High GPA 37
Part 3: Applications 38 Relative importance of application criteria 41
Application timeline 42
Round 1 vs. Round 2 44
Standardized exams 45
GMAT vs. GRE 46
Recommendation letters 47
Essays 48
Career goals essay 49
Why school essay 50
Contribution essay 51
Interviews 52
Waitlist management 53
Part 4: Where to get help (and more information) 54 Chasedream 55
⼀亩三分地 55
LinkedIn 56
Campus visits and school information sessions 57
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Choosing an agency 58
Know your consultant 59
Read the fine print 61
Summary 62
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APPLYING TO OVERSEAS GRADUATE PROGRAMS IN ANALYTICS by Jimmy Chen and Joseph Li
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INTRODUCTION
This document is a lateral extension of a similar guide on graduate finance applications that I shared last month, which itself grew from two realizations: that there is often a lot of overlap in what I tell students about graduate business applications, and that my knowledge could help many more applicants if I write it down.
In this guide, I’ve also enlisted the help of a former student, Joseph, who shares his views on career planning and school/program selection. As a recent applicant who successfully made a transition from a non-technical major (finance) to analytics, Joseph can provide a unique and valuable perspective that’s more relevant than mine in many areas.
留学, or studying abroad, is both an interesting life experience and an interesting industry. Like other “once in a lifetime” experiences (weddings, for example), studying abroad is not something that most people do repeatedly. This gives rise to a phenomenon - nearly all people who wish to study abroad have no experience with the application process that will determine which schools grant them admission.
Perhaps due to the above, there is a lot of inaccurate or misleading information. Through this document, Joseph and I hope to shed light on the oft-times confusing process of applying to study abroad. Everything we have written here is accurate to the best of our knowledge.
Finally, the information presented herein is intended to be freely shared with anyone who might find it useful. Please pass it on if you know others who would benefit.
Jimmy Chen Shanghai, June 2021
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OUR BACKGROUND AND EXPERIENCE
Jimmy began helping Chinese students apply to foreign business schools in 2014. He used to oversee graduate business applications at 新东⽅前途出国 in Shanghai. He has personally worked with a few dozen applicants to MBA programs as well as a few dozen applicants to graduate business programs in finance, accounting, business analytics, marketing, and management. Past students of Jimmy’s have been admitted to nearly every top business school in the world (including six of the M7, LBS, and INSEAD). Those who have since graduated now work in a range of organizations (including MBB, Google, and Amazon) in China and abroad.
Joseph is a recent student of Jimmy’s who applied for MSBA programs. He has achieved a smooth career transformation from finance to data science, having secured internship offers from nearly all top tech companies based in China (eg. BAT). Joseph has comparatively rich industry experience and deep understanding of the data science field, and he will join a Tier 1 MSBA program in US in 2021 fall.
INTENDED AUDIENCE
This document will only discuss topics related to graduate programs in analytics.
You should be a Chinese undergraduate student who wishes to pursue a career in analytics either in traditional companies undergoing digital transformation or tech companies with robust data analytics processes. Your overall profile should be competitive for the graduate programs that can support you to achieve such career objectives.
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PART 1: CAREER PLANNING
REASONS FOR STUDYING ABROAD
1. REMAIN ABROAD TO WORK
1The salary here includes base, bonus, and equity, so it’s the total compensation before tax. 2All sample companies here are tech (Internet) companies, since most BA/DS graduates will choose a tech company as their career starting point. 3Except for some 外资企业 such as Amazon, Microsoft, PayPal and eBay in China, nearly all Chinese tech companies (国内互联⽹) will execute 996 and ⼤⼩周 work schedules.
The table above shows the comparison between Chinese companies and US companies on salaries, reputation, hours and so on. Obviously, even if we consider the higher tax and cost in the US, for a junior level data science/analytics professional, staying in the US definitely has a better salary and hours.
US - ⼤⼚ CN - 外企 CN - ⼤⼚ US - 中⼚ CN - 中⼚ US - ⼩⼚ CN - ⼩⼚
Represent-ative companies
FLAG/FAANG
Amazon, PayPal, eBay
BAT, 快⼿, 滴滴, 美团
Big start-ups (after D round)
⼩红书, 携程, B站
A or B round start-ups
A or B round start-ups
Salary after graduation1
$150k-250k (100-200W)
20-35W 25-45W $100-200k (70-150W)
20-35W $70-150k (50-100W)
15-25W
Reputation Very high Middle-high
High Middle-high
Middle Low Low
Hours2 Most are work-life balance (WLB)
Most are WLB
996 and ⼤⼩周
Most are WLB
996 and ⼤⼩周
Most are WLB
996 and ⼤⼩周
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2. TRANSITION FROM A NON-ANALYTICS MAJOR
About ten years ago, finance and consulting companies (Investment Banks, Big Four, MBB) were dream career targets for nearly all students from business backgrounds. However, with the rapid growth of the Internet (more powerful online devices such as the iPhone, more powerful apps such as Facebook, TikTok) and the amount of time people spend online, there has been a continuous spike in big data. Therefore, job positions related to big data are now in high demand. Also, compared to the slow-growth, “内卷”, and “夕阳产业” of traditional roles in finance/consulting, the jobs that combine business acumen and technical skills in Internet industries show their strength: high potential for growth and decent/better salaries.
In the meantime, in Mainland China, nearly no specific undergraduate majors aim to develop data science professionals. There are only related majors such as statistics, MIS, computer science, etc.
Based on high demand & analytics’ attraction, many undergraduate students from business backgrounds (accounting, finance, marketing, management etc) will choose an MS Business Analytics degree to help them have a smooth career transformation from business to analytics field.
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NO TARGET SCHOOLS FOR ANALYTICS ROLES
Compared to traditional finance and consulting jobs, analytics roles do not heavily prefer a graduate degree from target schools and programs, but a great program can certainly be a bonus when seeking full-time positions (alumni network, technical foundations).
Since most analytics roles attach to engineering and product roles, they do not need to meet their clients/users/consumers face to face, and the most important evaluation for their job is the growth/performance of KPIs, eg. how the products grow and growth rates through data analytics methods.
Therefore, the general qualifications for analytics roles consist of three parts:
Technical skills Programming skills (SQL, Python, R) Statistics and probability A/B testing and experiment design Machine learning and deep learning
Business skills Product sense Communication skills Collaborative/leadership skills
Relevant experience Internships (length, content) Previous full-time work (industry, length, impact) Projects/research (relevance, evidence of quantitative skills employed, results)
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保研 VS 考研 VS 留学
A simple chart can describe the main differences between the three parts fo BA programs:
1考研 is a very risky option because the rules restrict students to take the exam just once in any given year and choose one program as their target. If you fail, you’ll need to either try again next year or seek alternate paths. 2For an MSBA program that’s longer than 1 year, the students can utilize the summer period to seek internships in China and secure a return offer. Conversely, students at MSBA programs that are than less than 1 year in length need to directly seek full-time jobs.
保研 考研1 留学
Program cost Low Low High (400k - 1 million RMB)
Time to complete 2-3 years 2-3 years 9 months - 2 years
Standardized exams Not required Very competitive exam, once per year
TOEFL (IELTS) and GRE (GMAT)
GPA requirement High (3.7+) Not required Medium-high (3.5+)
Securing a job in China Easier due to location in China
Easier due to location in China
Ranges depending on length of program2
Securing a job abroad Unlikely Unlikely Possible from top programs
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A BREAKDOWN OF THE DIFFERENT DATA-RELATED ROLES/FUNCTIONS
We need to have a clear view of different roles in data-related jobs. We have differentiated data-related jobs into three tracks/functions:
1.Algorithm & Engineering Track (技术,算法导向)
2.Analytics Track (业务,分析导向)
3.Data Engineering Track（数仓导向）
*Also, some companies have full-stack data scientist roles, which means the candidate should be good at algorithms, analytics methods, and engineering coding.
Functions Algorithm and Engineering Track
Analytics Track Data Engineering Track
Relevant job titles Data Scientist; Machine Learning Engineer; Applied Scientist; Researcher
Data Analyst; Product Analyst; Business Intelligence Engineer
Data Engineer; Big Data Developer; Software Engineer; Back-end Engineer
Skillsets Machine learning, deep learning, natural language processing, SDE coding skills
A/B testing, exploratory analytics, data storytelling, metrics reporting, data visualization
Data pipelines and ETL, SDE coding skills
Education background Prefer PhD, top CS majors, top MSDS
MSBA, MSDS MSCS, MSDS, other engineering majors
Interviews SDE background + LC (hard) + algorithms
SQL + statistics + product sense
LC (medium/hard) + Python + big data tools (Hive, Hadoop, Spark, AWS)
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BA VS. DS VS. PHD
The following chart describes the main differences between MSBA, MSDS and PhD degrees:
Degree MSBA MSDS PhD
Representative programs
USC MSBA; Columbia MSBA; Emory MSBA; UCD MSBA
NYU DS; UPenn DS; USC ADS; Duke MIDS
Statistics, MIS, and economics PhD programs
Program length 1-1.5 years 1.5-2 years >= 5 years
Undergraduate background requirement
Non-STEM majors are fine; quantitative experience is a bonus
STEM majors and quantitative experience
Rich research experience
Career after graduation Business Analyst; Data Analyst; Data Scientist (Analytics)
Data Scientist; Data Analyst; Software Engineer
Research Scientist; Applied Scientist; Machine Learning Engineer
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MORE ON INDUSTRIES AND EMPLOYERS OF BA/DS
Tech & Internet:
E-Commerce: Amazon, eBay, Shopee, Wish, Shopify
Video: Youtube, Netflix, TikTok, Instagram, B站, 快⼿
Social Media: WeChat, Facebook, WhatsApp, Instagram, Weibo
Transportation: Uber, DiDi, Lyft
CRM: Salesforce, Oracle, SAP, Microsoft
Traditional industries:
Finance/Fintech: PayPal, Capital One, Bloomberg, Visa
Retail: Walmart, Target, Wayfair
Finance/Consulting: Big Four, MBB
Healthcare: CVS
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A TYPICAL DAY FOR A JUNIOR DATA SCIENCE PROFESSIONAL (FOREIGN TECH FIRM)
10 AM ~ 10:30 AM: Check emails / schedule meetings with stakeholders / list to-do lists for a day
10:30 AM ~ 11:30 AM: Coding by Python & SQL to do data extraction and analysis
11:30 AM ~ noon: Meet with product managers to sync one A/B Testing experiment’s performance
Noon ~ 1:30 PM: Lunch / nap
1:30 PM ~ 3:00 PM: Debug SQL queries/Python Scripts & Draft analysis reports & Write previous analysis documents
3:00 PM ~ 4:00 PM: Meeting with stakeholders to discuss their data analysis requests
4:00 PM ~ 5:30 PM: Investigate opportunities of new programs / product features; conduct exploratory analysis
5:30 PM ~ 6:00 PM: Recap and review today’s work, make plans for future projects
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A TYPICAL DAY FOR A JUNIOR DATA SCIENCE PROFESSIONAL (CHINESE 996 FIRM)
10 AM ~ 10:30 AM: Check emails / schedule meetings with stakeholders / list to-do lists for a day
10:30 AM ~ 11:30 AM: Investigate the reasons behind a KPI’s drop or data discrepancy
11:30 AM ~ noon: Meet with product managers/operations (需求⽅) to sync product / campaign’s performance
Noon ~ 1:30 PM: Lunch / nap
1:30 PM ~ 3:00 PM: Debug SQL queries/Python Scripts & Draft analysis reports & Write previous analysis documents
3:00 PM ~ 4:00 PM: Meeting with stakeholders to discuss their data analysis requests
4:00 PM ~ 5:30 PM: Investigate opportunities of new programs/product features, conduct exploratory analysis
5:30 PM ~ 6:00 PM: Team meetings to get feedback from mentors and teammates
6:00 PM ~ 7:00 PM: Dinner / rest
7:00 PM ~ 9:00 PM: Coding by Python & SQL to do data extraction and analysis & build dashboard
9:00 PM ~ 10:00 PM: Recap and review today’s work; prepare for tomorrow’s team round tables
*⼤⼩周 required (ie. each alternating Saturday is a working day)
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A TYPICAL ANALYTICAL FRAMEWORK FOR A DATA SCIENCE PROFESSIONAL
Difference:
1. Technique: Internet industries are better than traditional industries, more advanced and forward-looking.
2. Data Culture: Internet industries rely more on data, and the culture for most companies is data-driven while the traditional industries are more client-driven. It’s hard to compare which culture is better. In other words, tech companies have better requirements on technical ability, while traditional companies focus more on soft-skills, especially data science roles in consulting companies.
Process flowchart Business example
1. Investigate the opportunity / identify the problem Topic: Add [Popular Comments] feeds along with titles when users enter Bilibili can improve CTR
2. Sync with stakeholders (业务⽅) Communicate with PM to design A/B test to verify that this new feature has positive impacts
3. Deep dive analysis (descriptive analysis, experimentation, data visualization, and modelling)
Conduct A/B testing experiment to track different group’s CTR performance
4. Sync with line manager Compile A/B test results and challenges
5. Presentation / report Report A/B test results to PM/SDE
6. Track performance of new features Track the new feature over the long-term
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SEVERAL SAMPLE CAREER PATHS
IC (Individual Contributor) track sample career path:
Data Scientist - Senior Data Scientist - Principal Data Scientist - Staff Data Scientist
Management track sample career path:
Data Scientist - Senior Data Scientist - Data Science Manager - Data Science Director/VP
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PART 2: SCHOOL SELECTION
US VS. NON-US
Location US Non-US
Program maturity Most were started around 2015 (eg. USC in 2016, and UMN in 2014), so they have comparatively longer history and better alumni network
Started very recently and most follow the US model (eg. LBS in 2019, and NTU in 2020)
Curriculum Very practical; nearly every BA program has a capstone project. Technical depth varies between programs. Tech-oriented: MIT, UMN Business-oriented: WFU
Some programs are practical (eg. Imperial College and NTU) while others are not industry oriented (eg. Warwick, HKU)
Tuition 40-65W 20-30W
Career outcomes Graduates from top programs mostly remain in the US. Graduates from mid-level programs (eg. Rochester, WFU) half ~50% chance of remaining in the US.
Graduates of most programs return to China (exceptions include Imperial College and Canadian programs due to better immigration policy).
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留美 TIER LIST
Tier 0: MIT MBAn, UCLA MSBA
Tier 1: CMU MISM-BIDA, USC MSBA, UT Austin MSBA, Emory MSBA, UMN MSBA, Columbia MSBA, Duke MQM, UCD MSBA
Tier 2: UW-Seattle MSBA, UCSD MSBA, ND MSBA,UR MSBA, Miami MSBA, WUSTL MSBA,WFU MSBA, Brandeis MSBA, WM MSBA
Tier 3: UCI MSBA, BU MSBA, SMU MSBA, NEU MSBA, WiSC MSBA, GWU MSBA,JHU MSBA, RPI MSBA, Fordham MSBA, UMD MSBA
Jimmy’s note: Michigan Ross has introduced a new MBAn program with applications opening in July 2021. While there is no data yet on the admissions bar or employment outcomes, it has great potential to quickly become a Tier 1 program.
回国 TIER LIST
Tier 1: IC MSBA*, NUS MSBA, LBS MAM, UT Rotman MMA, HKUST MSBA,NTU MSBA, HEC+X DS
Tier 2: CUHKSZ MSDS, NYUSH DABC, McGill MMA, Western Ivey MSBA, UBC MBAN, HKU MSBA, ESSEC+Centerale MSBA, Bocconi DSBA, UCL MSC BA
*Many IC MSBA candidates remain in the UK to work (41% of students were from Asia-Pacific, but only 11% returned to Asia-Pacific to work after graduation)
Joseph’s note: Because I did not consider graduate studies outside the US, I am less familiar with the above programs. However, from conversations with industry contacts and students in these programs, I am fairly certain of their approximate tier ranking.
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OTHER NON-BA PROGRAMS IN US TO CONSIDER
When applying for MSBA programs, students can also consider some BA-related programs, but all of them are not in business schools.
Note: I did not include traditional MSDS programs here, since most BA candidates do not have a strong quantitative background.
Tier 1: Stanford MSE, NWU MS Analytics, Georgia Tech MS Analytics, NCSU MS Analytics, UCB IEOR, Columbia MSOR
Tier 2: USF DS, UChicago MS Analytics, UW MSIM, Columbia QMSS, USC ADS, NYU MSIS
Tier 3: Columbia Statistics, UNC MSIS, Georgetown MS Analytics, USC Analytics
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ADDITIONAL INFORMATION FOR TIER 1 PROGRAMS
MIT MBAN
Admission: In the history of MIT MBAn admissions, fewer than 3 陆本 were admitted. From ⺠间传闻, only a talent from Fudan University majoring in Statistics & Data Science has been admitted into this program. This program prefers top 30 美本 with STEM background and quantitative skills; the qualified background for this program is 3.9+/330+/110+ with data science experience (internship, research). This program also required in-person interview, which has technical questions. ~60 class size
Curriculum: Very technical curriculum, machine learning-based BA program. Capstone projects with leading companies (McKinsey, BCG, StubHub, etc.)
Employment: Excellent career outcomes; nearly 100% stay in the US. Many graduates will choose data scientist roles in MBB (McKinsey, BCG GAMMA) and tech companies (Amazon, Facebook, Google). The sample positions are applied scientist, data scientist and data analyst. Even better than most MSDS Programs.
Average base starting salaries: ~$12W
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UCLA MSBA
Admission: Similar to MIT MBAn, UCLA MSBA also prefers 美本. During first two years of UCLA MSBA program, some 陆本 were admitted (Top 10 985); however, during the 2020 & 2021 admission cycles, fewer than 5 陆本 secured offers for this program. And this program does not require strong quantitative skills during application. With interview invitation, 90% will be admitted. ~60 class size
Curriculum: Great balance between tech and business, suitable for analytics role, while not machine learning engineer. Capstone projects with a wide variety of companies (such as Microsoft, Visa, or some start-ups)
Employment: Excellent career outcomes. Nearly 100% stay in the US. Locations mainly in the Bay Area and LA. Sample companies: Visa, LinkedIn, Amazon, Microsoft etc.
Average base starting salaries: ~$10W
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CMU MSIM-BIDA
Admission: CMU MISM Program has three pathways: MISM-General 16 Month, MISM-BIDA 16 Month, MISM-Global 21 Month. Since quite a few students on MISM-General 16 Month & Global 21 Month will transfer to SDE career path, we mainly focus on the MISM-BIDA program. BIDA program is relatively friendly for 陆本 compared to MIT and UCLA. Top 985 & 两财⼀贸 are target schools for CMU-BIDA program. This program is not 分控 like USC MSBA or Columbia MSBA; it emphasizes candidates’ analytics experience (eg: internship at eBay is a good bonus to application). ~150 class size. The overall MISM program has 300 students total.
Curriculum: Very flexible curriculum; students can choose many technical (CS) electives and also have practical capstone projects.
Employment: Excellent career outcome though the class size is bigger than UCLA’s program, due to CMU’s good reputation at tech companies and students’ strong job seeking abilities. This program’s final career outcomes are also very good. Some students will choose more technical roles: machine learning engineer or applied/research scientist; while other students will choose more business roles: data analyst or business intelligence engineer roles.
*Due to COVID-19, some graduates returned to China, but before COVID-19, nearly all graduates stayed in the US.
Average base starting salaries: $12W
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USC MSBA
Admission: USC Marshall MSBA program is a traditional ‘分控’ program, which means this program prefers candidates with high GPA (3.8 & 3.9+), high GRE (330+), high GMAT (750+) regardless of their undergraduate universities’ title. In another words, unlike other top MSBA programs, USC MSBA will also choose students from ⾮211&985 & 美本. Also, due to the high volume of candidates, many students’ admission will be deferred to subsequent rounds. The class size for USC MSBA is over 150.
Curriculum: traditional BA-oriented curriculum, flexible schedule for BA students; they can choose a program length of either 1.5 years or 2 years.
Employment: Generally good career outcomes. Some students will go back to China to join top Internet companies such as Alibaba, Bytedance and Tencent. Others will stay in the US. However, with the expansion of class size and decline in student profile quality, USC MSBA’s career outcomes have worsened, which means many graduates cannot go to ⼤⼚ like before.
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EMORY MSBA
Admission: Emory MSBA program is a very boutique program with a 40~50 class size, which means every student can get enough attention and care. The interview is very important for this program; it prefers candidates with rich work experience and excellent verbal ability. Most offers will be released during the first two rounds.
Curriculum: relatively technical and intensive curriculum; project-based, many projects, very practical.
Employment: Good career outcomes. Some capstone partners will directly give full-time offers to Emory MSBA students (Home Depot, FedEx); great career center support. Employment rate is 100%, and most graduates will stay in Atlanta. Maybe its low relocation percentage is the only disadvantage; ⼤⼚ at West coast don’t have many Emory MSBA alumni.
Average base starting salaries: $8W, very good data point in local Atlanta.
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UMN MSBA
Admission: UMN MSBA is the best program at Carlson School of Management. UMN MSBA prefers candidates with rich data-related experience or candidates who show enough passion for data analytics. Also, UMN MSBA is not a ‘分控’ 项⽬ like USC. The class size for UMN MSBA is ~80.
Curriculum: technical and intensive curriculum. The MSBA program has the Carlson analytics’ lab and capstone projects, where students will do many live cases to analyze real data from leading companies. This program’s curriculum covers all analytics’ skill sets for a data science professional.
Employment: Excellent career outcomes: UMN MSBA program retains for 100% employment rates in US for the past 5 years. Every year, 50% of job offers are from school-facilitated channels. Employers include Amazon, Capital One, Target and Uber, which means UMN MSBA is a target school for those companies. Also, many graduates will relocate to the west coast or other bigger cities.
Average base starting salaries: $10W
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COLUMBIA MSBA
Admission: Columbia MSBA program is the newest programs among all the Tier 1 MSBA program in the US, and its admission also is called ‘保研式录取’ , which means this program will only send offers to target schools (Top 985) and will not care much about students’ data-related experience. So, with high GPA & ranking in a target school, you can be safely admitted to Columbia MSBA program. ~150 class size.
Curriculum: since Columbia MSBA program is a joint program between the business school and engineering school, students can have flexible choices between technical electives and business electives. This program also has capstone projects with leading companies.
Employment: due to intensive internal competition with other majors (MSOR, MA Statistics, QMSS, MSDS, MSAA, etc.), Columbia MSBA program doesn't have dedicated career services. However, with the help of its NYC location and student strong profiles, the top students in this program have excellent career outcomes. Around 60% of graduates will stay in the US, while others will go back to China.
*Target schools: if 学⻓学姐 got admitted to the same program you apply for, your school is target school.
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DUKE MQM
Admission: like the program's required essays ‘25 random things’, Fuqua’s admission process is also quite random. Generally speaking, even with strong quantitative background & standard scores, it’s still unclear that Duke MQM is a safe choice. All admitted students will receive interview invitations. Duke MQM program has four tracks: strategy, finance, marking and forensics. The total class size is 240.
Curriculum: business-oriented curriculum, not technical, can be seen as a “mini-MBA” program. Has capstone projects; plenty of projects are consulting projects, not highly related to data science.
Employment: Fuqua has excellent career services and alumni network. But due to the short program length (9 months) & non-technical curriculum & location, the overall employment statistics is not as good as other tier 1 MSBA programs. In 2020, around 50% of graduates went back to China to seek full-time jobs.
Average starting base salary for those staying in US: $8W
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UCD MSBA
Admission: UCD MSBA program has two rounds of interviews: Spark interview (similar to Kira) and in-person interview. During the in-person interview, there are statistics, coding, or other technical questions. Generally, like UMN, UCD MSBA prefers students with rich data-related experiences. The class size is 80~90.
Curriculum: Practical curriculum with breadth and depth, the highlight for UCD MSBA program is the practicum project throughout the whole study period. The partner companies consist of start-ups in the Bay Area.
Employment: Located in downtown SF, UCD MSBA graduates have many career opportunities, especially at tech companies. Before the COVID-19 pandemic, nearly 100% of graduates can secure full-time data-related positions in the US. Sample companies include Bird, Wish, REEF, Amazon, PayPal, etc.
Average starting based salary: $10W
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IC BA
Admission: From an admission perspective, IC BA is the most competitive program outside the US. This program highly prefers undergraduates from outside China Mainland. For 陆本, top 985 and high GPA are important factors for admission. The class size for this program is ~100.
Curriculum: relatively technical curriculum, great balance between tech and business, have flexible electives.
Employment: A minority of Chinese graduates will go back to China to seek full-time jobs at Internet companies and traditional financial institutions. The overall statistics are good; sample career outcomes include BAT, top consulting/finance companies.
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LBS MAM
Admission: LBS is famous for its alumni network and essays & interviews. Like Duke MQM, this program is not a traditional BA program; it’s more like a management program with the addition of data analysis courses. Therefore, qualified candidates for this program mostly have rich finance & consulting experience instead of quantitative experience. The class size for LBS MAM is ~80.
Curriculum: Very business curriculum; have practicum projects, but the maturity of the practicum still need to be improved (lack of big names).
Employment: Dedicated career services. Most Chinese students will go back to China and seek full-time jobs in consulting, finance and Internet sectors. Unlike other BA programs where many graduates will become data analysts, LBS MAM graduates will choose more business-oriented roles such as consultant, product manager, strategy analyst or business analyst.
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SAMPLE SCHOOL SELECTION CASES
1. TOP 985 HIGH GPA BUSINESS MAJOR & OVERSEAS UNDERGRAD HIGH GPA
Undergraduate institution: Top 985( eg. 上海交通⼤学、复旦⼤学) , top 30-40 US undergrad (NYU, Rochester, UC Irvine, UIUC)
Major: Finance / Accounting / Economics
GPA: 3.8+/4.0
GRE: 330+
Internships: Big Four/券商⾏研&IBD/Consulting, Tech Companies BA/DA (eBay, Amazon, Bytedance, Tencent)
Sample program pool:
CMU MISM-BIDA UCLA BA USC BA Columbia BA Emory BA UT Austin BA IC BA NUS MSBA LBS MAM
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2. GOOD 985 & 两财⼀贸 MID-HIGH GPA
Undergraduate institution: Good 985 (eg. 中⼭⼤学、武汉⼤学、同济⼤学)
Major: Finance / Accounting / Economics
GPA: 3.6+
GRE: 325+
Internships: Big Four/券商⾏研&IBD/Consulting, Tech Companies BA/DA(eBay, Amazon, Bytedance, Tencent)
Sample program pool:
CMU MISM-Global/BIDA Columbia MSOR/QMSS/Statistics Emory BA USC BA UMN BA UCD BA Duke MQM UCD BA WUSTL BA WFU BA HKUST MSBA NTU MSBA
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3. GOOD 211 & NON-TARGET 985 MID-HIGH-GPA
Undergraduate institution: Good 211 (eg. 苏州⼤学，上海外国语⼤学，⻄南财经⼤学), non-target 985 (eg. 重庆⼤学、华东师范⼤学)
Major: Finance / Accounting / Economics
GPA: 3.6+
GRE: 325+
Internships: Big Four auditing/券商⾏研/Marketing Analytics
Sample program pool:
Columbia MSOR/Statistics/AA/ERM UMN BA UCD BA Duke MQM WUSTL BA UCSD MSBA UR MSBA HKU MSBA CUHKSZ DS UCL BA
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4. ⾮ 211/985 HIGH GPA
Undergraduate institution: ⾮ 211&985 (eg. 上海对外经贸⼤学，华东政法⼤学)
Major: Finance / Accounting / Economics
GPA: 3.8+
GRE: 325+
Internships: Big Four auditing/券商⾏研/Marketing Analytics
Sample program pool:
Columbia Statistics/AA/ERM WUSTL BA UCSD MSBA UR MSBA UCI MSBA NEU MSBA JHU MSBA Wisc MSBA CUHK MSBA
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PART 3: APPLICATIONS
A little-known fact is that most BA applicants do not come from math/statistics/computer science backgrounds. Rather, most are looking to supplement their undergraduate studies (usually in non-technical disciplines) with the analytics skills that an MSBA can provide. As evidence of this, let’s look at class profile data regarding undergraduate majors of candidates from several representative MSBA programs.
UCSD Rady MSBA:
Engineering/Science: 26% Economics: 18% Math/Statistics: 17% Business/Management Science: 15% Accounting/Finance: 12% Other: 12%
UCLA Anderson MSBA:
Economics/Finance/Banking: 36% Information/IT/Communication Engineering: 14% Industrial/Mechanical/Biological Engineering: 12% International Business/Business Administration: 12% Marketing/Advertising/Communications: 10% Mathematics/Statistics/Accounting: 10% Other: 7%
Duke Fuqua MQM:
Business & Accounting: 55% Engineering/Natural Science: 20% Economics: 20% Liberal Arts/Other: 5%
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London Business School MAM:
Business/Management: 31% Economics: 26% Engineering: 18% Finance/Accounting: 14% Maths/Science: 6% Social Science: 3% Humanities: 1% Other: 1%
The exception is MIT’s MBAn, a highly technical program:
Math & Science: 38% Engineering: 30% Business: 10% Economics: 7% Computer Science: 2% Other: 13%
To reiterate my earlier point, the key takeaway from the data is: as an MSBA applicant, you’ll usually be competing against a candidate pool composed mostly of people with undergraduate backgrounds in business, finance, and economics, while a significant minority will have backgrounds in engineering or math/statistics.
Given the above, a priority when considering your application strategy is to know which “applicant pool” you’re in. Are you:
1. From a business/economics background and pursuing a BA degree to gain data/quantitative skills?
2. From a technical background (engineering/math) and pursuing a BA degree to gain business-oriented skills?
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Whether you’re in group 1 or group 2 will greatly influence how you should present your profile to the admissions team to demonstrate that you’re a good fit for the program.
If you’re in group 1, you’ll want to demonstrate that you’ve already begun learning and using the technical skills required for analytics professionals, and ideally you’ve completed at least one formal internship which demonstrates this.
If you’re in group 2, you’ll want to demonstrate that you’re passionate about the business side of analytics; you absolutely do not want admissions officers who read your application to feel that you’re more suitable for data science programs and are only applying to some MSBA programs as a “backup plan”.
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RELATIVE IMPORTANCE OF APPLICATION CRITERIA
How important is each element of your application? This is quantified in the following table from GMAC’s 2015 Application Trends survey (the only year it published this information after collecting responses from the vast majority of business schools):
From the above, it’s clear that although your GPA and standardized exams are important, they just account for about half of the admissions criteria weighting. For most top programs, the weighting is usually even heavier towards the non-GPA/exam aspects of your application. Your resume (internship experience), interview, essays, and recommendation letters are all significant factors in admission decisions.
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APPLICATION TIMELINE
The following is the application/career timeline of Jimmy’s former student (identity withheld to preserve privacy) who made a very successful transition to analytics.
Applicant’s initial profile (April 30, ⼤三下)
Academic background 211 university; finance major; GPA 87+/100
Standardized exams TOEFL: < 90; GRE: not taken
Internship Consulting internship in a Big Four firm (summer between 2nd and 3rd year)
Extracurriculars Involvement in university sports team, volunteering, student union, ICM
Technical skills MS Office
Other Exchange experience to a European university
Application Timeline Event/Action Notes
May 2019 Second TOEFL attempt ~90
July to Sep 2019 Internship at leading mutual fund company Decided to transition to analytics
July 2019 First GRE attempt >310
September 2019 Second GRE attempt ~320
October 2019 Third TOEFL attempt ~95
October 2019 Third GRE attempt ~320
November 2019 Applied to RPI and WFU as backup plans DDL Nov 15/Dec 1
November 2019 Fourth GRE attempt >330
December 2019 Fourth TOEFL attempt >105
December 9, 2019 First school selection meeting WUSTL, Rochester, Duke, UCSD, UMN, Emory, USC, Columbia, UC Davis, Notre Dame
December 16, 2019 School selection change Remove UCSD/UCD, add CMU
December 2019 Submitted Rochester and UMN
Application Timeline
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Many students think: “Unfortunately I am from a non-analytics major, so I cannot apply for MSBA programs.” This is a common misconception about the technical requirements. As the above student’s application journey proves, success can be achieved with enough commitment and the appropriate target program choices.
One area this student could have done better was prepare and take the standardized exams earlier. As seen in this case, it’s often necessary to take them 3-4 times. Therefore, if you want to submit your applications by the round 1 deadlines (October/November in most cases), leave enough time in your schedule to take each exam at least three times before September.
December 2019 Interview with RPI
December 2019 Offers received from RPI and WFU
January (2020) COVID-19 becomes a global problem Lockdowns begin
January 6, 2020 School selection change Remove Emory and CMU, add Toronto
January 2020 Submitted Notre Dame, Duke, and WUSTL
February 2020 Submitted Toronto, USC, and Columbia
February 2020 Interviews with UMN and Notre Dame
Feb to July 2020 Data analyst internship at 互联⽹中⼚
March 2020 Offers received from Rochester, WUSTL, UMN WL at USC, Notre Dame
March 2020 Decide to defer admission to 2021
Aug 2020 to April 2021
Data scientist internship at foreign tech company
Also received offers from 快⼿, 滴滴, 字节)
October 2020 Received return offer - but declined it Commitment to 留学, 留美, and foreign ⼤⼚
May 2021 to now Data analyst internship at foreign ⼤⼚ Also receivedoffers from BAT, 美团, etc.)
Event/Action NotesApplication Timeline
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ROUND 1 VS. ROUND 2
Apply early if possible. Round 1 is generally the most “fair”, and gives you more flexibility. Schools will base their admissions decisions on previous years’ applicant data and are more likely to waitlist a good number of candidates as a hedge against a decline in applicants in later rounds. In the case of newly created programs (still a common occurrence for BA programs), the admissions team has no data to work from and may admit a large number of qualified applicants in round 1.
It’s also particularly important to apply early to programs at the top of your range because this reduces the chance of you paying a deposit for a program that you ultimately will not attend. Such a scenario sometimes happens when a candidate is admitted to a “safety” school (保底学校) in round 1 and is subsequently admitted to another school that’s higher on their list.
Don’t underestimate the importance of getting on the waitlist early. If a program rejects you outright, you probably had no chance regardless of whether you applied in round 1 or round 2. Meanwhile, programs that are slightly beyond your reach will tend to waitlist you and see more applications in later rounds before making a final decision. By getting on the waitlist early, you will have more opportunities to prepare and submit profile updates, and the competition for getting off the waitlist will be less fierce.
There are two types of programs that I think are good candidates for round 2: “dream” schools and “safety” schools. I define a dream school as one that would normally reject candidates with your profile in round 1, and that you will attend regardless of whether you’ve paid a deposit for any other program you applied to. In some application seasons, round 2/3 application numbers decline severely, and some programs end up having to lower their bar for admission significantly in the later rounds. A notable example of this was Duke Fuqua’s MQM program a few years ago. Therefore, if you know you’re not competitive for a particular program but it’s your dream to attend, my recommendation is to apply in round 2 or 3, just in case such a scenario occurs.
As for safety schools, you should of course only apply to these choices if your round 1 applications to better programs were unsuccessful.
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STANDARDIZED EXAMS
For American programs, the GMAT or GRE accounts for about 20-25% of your admissions criteria weighting. For British programs, it’s a bit less - perhaps 10-15%. A great GRE score is 330+ with 4.0 AWA, while a great GMAT score is 740+ with 5.0 AWA (the essay portion for GMAT is easier).
The TOEFL (or IELTS) is frankly not that important, but you cannot apply without it. A high score is never a reason for admitting a candidate. However, a low score can - and often is - a reason for rejecting a candidate. Aim for a TOEFL score above 110 if possible, with each sub-score at 25 or higher, as this is the minimum requirement for Oxford and Cambridge. The IELTS requirement for these schools is 7.5 with 7.0 in all sections. This is easier than the TOEFL requirement so if you plan to apply to UK programs, I recommend that you take the IELTS.
For most candidates who score ~110, their listening and reading sub-scores are often the maximum 30 points or very close to it. The speaking and writing sections are the main weaknesses of nearly all Chinese TOEFL takers.
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GMAT VS. GRE
This is a more complicated question to answer (than TOEFL vs. IELTS). For applications to business schools, the short answer is to take the GMAT if you can get a score that’s higher than the average GMAT of the programs you’ll apply to. (Another significant reason is LSE’s finance-related programs either require or prefer the GMAT, depending on the specific program.)
When business schools publish class profiles for their programs, they almost always include average GMAT of the class (in addition to average undergraduate GPA in many cases). Few include average GRE. Business schools have a long history of using the GMAT as a criterion for admission, while accepting the GRE has been a relatively recent development. The US News ranking of business schools, the most influential MBA ranking of the top American MBA programs, includes average GMAT (but not average GRE) as one of its ranking criteria and gives it twice as much weight as average GPA.
For better or worse, business schools in the US (and those that broadly follow the American business school model, such as Oxford Saïd, Cambridge Judge, LBS, and HEC) consider average GMAT of the entering class an important indicator of the selectivity and student quality. Therefore, it’s safe to assume that many business schools’ admissions offices have “increase next year’s average GMAT” as one of their key performance indicators.
In other words, if you take the GMAT and your score is higher than a school’s average GMAT, I would except it to improve your admission chances more than a similarly higher GRE score would.
That said, there are still some very good reasons to take the GRE. It’s easier to get a high score, some people just prefer to memorize vocabulary, and the GRE is accepted by programs outside business schools (while the GMAT is not).
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RECOMMENDATION LETTERS
A great recommendation must:
1. Clearly rank you highly among a relevant group (eg. all students taught or all interns supervised in career). Ideally, they rank you in at least the top 5%.
2. Contain specific supporting evidence for the ranking given. Descriptions of excellent work you’ve done are ideal.
3. Contain verifiable quantification of your success and impact.
In my experience, many applicants mistakenly believe that a recommender’s title is the most important criteria. This is not remotely true. There are two criteria that are critically important and always take precedence.
The first is how well your recommender knows you. This is easy enough to understand - if your recommender and you have not interacted much in the past, there is very little of value that they can say about you in the reference letter. They would not be able to cite specific evidence nor provide verifiable numbers quantifying the impact of your work.
The second is credibility. I hope this doesn’t come as a surprise - admissions officers at business schools know that there’s a lot of shady business going on in the application process. Many applicants paid money to secure “internships”. Nearly all use some form of “机经” for standardized exams. Most don’t write their own application essays. Some even Photoshop their academic transcripts. And you can bet that some recommenders don’t exist, or don’t know they’ve been unknowingly used in a student’s application.
Therefore, you may notice that many business schools insist that you provide institutional emails for your recommenders. Additionally, they prefer recommenders who have studied abroad and/or can be found on public channels such as Google or LinkedIn. Such a preference for “credibility indicators” goes beyond recommendations; business schools also prefer students with internships from MNCs, who attended top undergraduate institutions in China and/or have exchanged to reputable universities abroad.
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ESSAYS
It would be a major endeavor to list every single essay questioned by every finance or finance-related program in top business schools around the world. Some are unique; for example, Rochester asks you to share one interesting fact about you, while Duke Fuqua asks you to list 25 such facts. Many programs ask you to tell a story - about a challenge, achievement, collaborative experience, or failure, among others.
To keep things manageable, I’ll focus on the three most common essay topics:
1. What are your short and long-term career goals, and how did you arrive at them?
2. Why are you interested in our program/school, and how will our program help you to achieve your goals?
3. How will you contribute to our class and/or our community?
The above prompts are usually referred to as the “career goals essay”, “why school essay”, and “contribution essay”. While it’s difficult to give specific advice, I’ve included some notes and advice below on each of these three essay prompts.
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CAREER GOALS ESSAY
Business schools ask this question because it tests how clearly we have visualized our career goals, and how convincing our motivations are. This is important because they measure success in the program through employment outcomes, and the candidates who are the most successful at securing jobs tend to have very clear career goals and are very committed to them.
The best answers to this essay question incorporate the following elements:
Specific goal that details industry, function and location. For example, fintech, data analytics, Shenzhen/Beijing/Shanghai.
Motivation for this goal in the form of a story or “spark”. For example: “As an undergraduate student studying abroad in Canada, I remember being amazed by the speed of technological change in China when I spent my last summer there. One scene stands out in my mind: I went out with some friends for lunch, and I noticed that I was the only one in my group to use a credit card; everyone else just whipped out their phones and used a mobile payments app. None brought cash. This was when I realized that financial technology innovations will eventually take over how we save, invest, and do business, and I remember thinking that I wanted to be a part of it professionally as soon as possible.”
Skills and experience required to achieve career goal. Ideally, we can name specific companies we want to work for, as well as specific roles we will apply to and their job requirements. (See the career planning section of this document for an overview.)
Detailed “plan B”, in case our career goal cannot be attained. Ideally, this should be as detailed as our “plan A”, even if we do not incorporate all of this information into the essay itself.
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WHY SCHOOL ESSAY
Business schools ask this question because they prefer to admit students who truly want to attend. They want to be the dream school of each admitted candidate. No school wants to be your safety school. The more work you’ve put into getting to know the program well, the more convinced they are that you’re committed to attending if they admit you.
As a side note, this is why some programs purposefully pose unique essay questions (eg. Duke Fuqua with their “25 random things”); they do this to filter out those applicants who do not want to spend time to craft truly compelling responses.
I like to break “why school” down into several “levels” of research you could do.
Level zero - public information. The official website, which generally publishes a program’s curriculum structure, class profile, employment report, etc. is a good resource but one that nearly all applicants will use. This is the most basic research you can do, and if it’s all that you do, you will not stand out.
Level two - public forums and sharing sessions. Such platforms and events disseminate information shared by admitted students, current students or recent graduates. This is better because you may come across some “insider information”.
Level three - connecting privately with current/former students. You can meet these through the aforementioned platforms and events or through LinkedIn (which I will discuss later in this document). The benefit here is you can mention such people by name in a “why school” essay.
Level four - campus visit. This shows the most commitment as it requires the most effort to execute (assuming you’re an international applicant living in China). Mentioning that you’ve visited the campus might help them to remember who you are and put a face to your profile. In fact, the person reading your application may even be someone you met during your visit!
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CONTRIBUTION ESSAY
Business schools ask this question because many important aspects of these schools are run by the students. Clubs, recruitment events, industry summits, case competitions, career treks, holiday parties - these are almost always led and organized by volunteers from the student body. Therefore, business schools need to ensure that the candidates they admit will contribute meaningfully to such events and organizations within the community.
The best predictor of what you will do in the future is what you have done in the past. Specific examples of how you were involved in the student union, various clubs, business competitions, volunteering, etc. are the most convincing evidence that you will contribute similarly to the business school you’re applying to.
My advice is to spend half of the essay, detailing your prior experiences, and the other half of the essay discussing specific ways you will contribute and why you’re interested in doing so. As with the “why school” essay, the best content to include would be drawn from conversations you had with current/former students.
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INTERVIEWS
For top business schools, interviews are an important part of the application process because they mirror a requirement for securing full-time jobs. Therefore, the majority (though not all) of the questions asked will be similar.
Interviews come in several forms. The formats include in-person with an admissions officer, in-person with an alumnus of the program, or online with either type of interviewer via Zoom or Skype. Some programs interview you more than once, or interview groups of candidates at the same time. Some others have decided to replace human interviewers with a “video interview” process through platforms such as Kira Talent or InitialView, where you register log on to a platform and sit through a process that’s somewhat similar to the TOEFL speaking section.
Most applicants scour online forums and websites for “⾯经” and often feel pressured due to the sheer number of different questions that could be asked. My recommendation is to understand that there are only three different types of interview questions, and that you should use different frameworks for each. They are:
1. Questions asking for facts. These could be inquiries about your background/profile or technical/current news questions that test your academic/industry knowledge.
2. Questions about your experience, also known as “behavioral questions”.
3. Questions asking for your opinion, including commonly asked examples such as “why school”.
Interview preparation is a multi-stage process that cannot realistically be taught through document such as this. Research the types of questions that will be asked by interviewers of the programs you’re applying for, prepare frameworks for responding to each type of question, and constantly update an extensive document with structured notes for every interview question that you think you could encounter. Most importantly, practice again and again! Find a partner or mentor to conduct mock interviews with you multiple times prior to each interview.
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WAITLIST MANAGEMENT
What some applicants tend to overlook is that the programs they most want to attend are likely “reach” programs, which tend to waitlist them as they might be qualified but aren’t quite a clear-cut admit.
The key is to understand that after a school waitlists you, it cares more about your interest in the program than about how qualified you are. By waitlisting you, the school has already signalled that you’re qualified for admission. You’re just slightly less competitive than some others they admitted before you. Schools manage a waitlist because they have several problems that a waitlist helps to solve.
Problem one: many admitted candidates will not attend the program.
Problem two: in round 1, they don’t know how good round 2 applicants will be.
Problem three: candidates get to wait until the deposit deadline, which could be as late as April or even May, to signal whether they will attend (by paying the deposit)
Problem four: some candidates don’t attend even after paying the deposit!
As a waitlisted candidate, you want to communicate to the school: “If you admit me, I will attend the program with 100% certainty.”
Just imagine - it’s bad enough that some students wait until May to tell the school that they won’t attend; it would be worse if they replaced those students with waitlisted candidates, who tell them later in June that they won’t attend. Business schools want to avoid this type of scenario if at all possible.
Therefore, while profile updates such as a new GMAT score or internship may help, what’s even more important is to show your unwavering commitment. “Love letters”, recommendations from current students or alumni that confirm your interest in the program, campus visits, and scheduled chats with admissions officers are some of the best ways to achieve your objective.
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PART 4: WHERE TO GET HELP (AND MORE INFORMATION)
If you’re currently looking for a reliable 留学中介, we suggest that you begin by reading the last section of this document (“choosing an agency”) where Jimmy gives some logical advice on how you should approach this matter.
If you’d like a specific recommendation, we suggest ⻢尚留学, as it’s the agency Jimmy and Joseph are personally affiliated with. Jimmy is a partner of ⻢尚 and oversees much of the work we do with students, so he’s very familiar with all our consultants and the quality of our processes and results. You can find us online quite easily by searching for our 微信服务号.
If you wish to work with Jimmy directly for the upcoming application season, the best course of action is to reach out to one of his former students and ask for a referral. Nowadays, this is normally the way he takes on new students.
For those of you who plan to DIY or who want to learn more about the application process, aside from the section on choosing agencies, we’ve listed several resources below that you could explore on your own for more information.
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CHASEDREAM
As one of the oldest online platforms for GMAT preparation and graduate business school applications, there’s a lot of information stored on the site’s forums. If you’re preparing for the GMAT or applying to schools, you will likely be a regular visitor of Chasedream. Some common uses of the platform include:
1. GMAT resources including the “机经” (which I’m not going to talk about in detail here because its use is frowned upon by the GMAC and business schools).
2. Interview reports posted by applicants.
3. Invitations to “申请群” for specific business programs. This is a good way to connect with applicants with similar backgrounds and goals.
4. Information about official business school events (such as information sessions held in China). Due to the site’s ubiquity among applicants, business schools often advertise this information there.
5. Random information hidden in various posts that might be valuable, such as former applicants’ backgrounds and application journeys, tips on how to write the essays for different program applications, waitlist management, school selection “定位”, and advice on which program to attend if holding multiple offers.
⼀亩三分地
This is a newer site with more of a focus on technical majors such as computer science. It’s a good platform to collect information about school selection and application results. Perhaps more importantly, there’s a lot of information about study and life in the US and job-related resources/advice.
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The two most relevant things to know about LinkedIn are:
1. Nearly all business school students abroad have LinkedIn. Since you plan to become a business school student abroad, you might as well register an account now.
2. It’s currently not possible to register a new LinkedIn account in China. You will need to find a way around this (usually by asking for help from someone living outside China).
LinkedIn is a powerful networking tool that helps you to understand the programs you’re interested in. By browsing the profiles of students in those programs, you’ll get a sense of the approximate bar for admission and employment outcomes upon graduation. Furthermore, LinkedIn allows you to connect with these students. If they’re nice (and many are), they will respond to you and perhaps even agree to a call.
Talking to a current student or recent graduate is by far the best way to learn more about a program you’re interested in, and LinkedIn is a great way to meet such people especially in the current pandemic situation where campus visits are not possible.
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CAMPUS VISITS AND SCHOOL INFORMATION SESSIONS
In my experience, I have never worked with a student who regretted doing campus visits before submitting their applications (or even after submitting their applications).
Think of campus visits as:
1. Guaranteed opportunities to speak with current students
2. A way to meet admissions officers and leave a good impression (of course, we assume that you will leave a good impression!). Very few international candidates fly across the Pacific Ocean to attend a campus visit, so it clearly shows a strong commitment.
3. Experiencing the program in-person. As they say in Chinese, 百闻不如⼀⻅. You can stay a night nearby, attend a lecture, eat with students, and walk around campus to get a feel for the energy and culture of the place. This sort of experience will be very helpful later if you’re admitted to several programs and need to make a decision on which one to attend. It would be better if your decision is informed by personal experiences with each school.
Finally, nearly every business school asks you the classic “Why our school?” at some point in the application process. Visiting the campus is the best way to ensure that your response does not disappoint the admissions team.
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CHOOSING AN AGENCY
As the last topic for this document, I’ll discuss 留学中介, a topic that’s probably foremost in the minds of most applicants. The vast majority of Chinese undergraduates who apply for graduate studies abroad choose to work with an agency for their applications. Therefore, as a public service, I’m going to repost an article I wrote some time ago that discusses how to choose an agency.
To keep the discussion at a manageable length, I’ll focus on the two most important aspects that you should consider: the consultant (whom you’ll be working with) and the contract (the terms of the services to be provided).
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KNOW YOUR CONSULTANT
If you’re a rational consumer, you are choosing to work with an application consulting agency because you believe the following: the value that the consultant creates is worth more than the fee that you pay. What are some indicators of value? There are three general categories.
1. Experience. Your consultant has helped other students with profiles like yours get into your target/dream schools.
2. Expertise. Your consultant has English/communication skills that are superior to yours, and therefore can add value by editing your essays and coaching you for interviews.
3. Efficiency. Your consultant has access to resources that can save you time. He/she has documents with the application requirements and deadlines for each program, and has supporting staff who assist with administrative duties.
Knowing all this, doesn’t it make sense that you should at least meet your consultant before you sign a contract?
Let me make something clear. The first person from the agency who contacts you on the phone is most certainly not your application consultant. In most cases, it’s also not the second person from the agency who meets with you in person at the agency’s office. When you walk into an agency’s office, one of the most important questions you should ask is: “Can I meet with the consultant who will actually help me with my application essays and interviews?”
When you meet this consultant, you may want to do the following:
1. Speak with him/her in English; even better, ask for a brief 5-minute mock interview in English.
2. Ask to briefly see samples of old application essays edited by him/her.
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An expert consultant would be able to give you specific details about the schools/programs you want to apply to (deadlines, interview policies, essay requirements), describe previous students who were admitted, and quickly pull up some successful essays and explain why they were successful. The best ones will voluntarily offer to connect you with previous students who have profiles like yours, so that you can ask them directly about their experience working with this consultant and this agency.
If you’ve already signed a contract and have not done this, do it immediately. Finding out whom you’ll be working with is the most important action you can take before applications are fully underway this autumn.
One final note that’s based on my experience:
The better your own profile is, the better your consultant needs to be to generate the requisite value. If you’re a student at Peking University, you’ll probably want to work with a consultant who has extensive experience with applications to top schools abroad (such as LSE) and has superior English abilities (significantly beyond IELTS 7.0 level). Such consultants are expensive. If you’re at an agency and they quote a price that’s significantly lower than their competitors, ask them why and seriously consider whether they’re hiding anything.
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READ THE FINE PRINT
All application consulting agencies require a contract to be signed before services can begin. These contracts are written by the agencies without any input from clients, and their main purpose is to limit the agency’s liability in case something goes wrong.
In my view, it’s far more important that you choose to work with a consultant and a company whom you can trust to do the right thing. Ethical and caring consultants will go out of their way to help their students, beyond what is required by the letter of the contract. Regarding the specific provisions outlined in the contract, there are three main considerations that you should be aware of and inquire about:
1. Is the process transparent? Will both you and the agency have access to a designated email address used for the applications, as well as the login/password information for all online application forms? It’s an immediate red flag if the agency is not willing to share this information with you.
2. What happens if your consultant leaves? Professional services firms generally have high turnover, and the application consulting industry is no different. You absolutely need to have a contingency plan for when your consultant resigns or is otherwise no longer able to work with you. At the very least, you should be able to dissolve the contract and receive a full refund in such a situation.
3. Will the agency respect your privacy? Agencies often use their past students’ results as a core part of their marketing efforts. Will the agency promise to never disclose your real name or contact information unless you give prior written permission?
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SUMMARY
To get full value from working with an application consultant, you must understand that you’re not outsourcing your applications. Work with your consultant. Remember that you’re paying for help to maximize your chances of admission, not for someone to do your applications for you. The two are not the same! Find an agency or consultant that subscribes to this philosophy. (They’re rarer than you might think.)
Do adequate research before your first meeting with the agency, and certainly before you sign a contract. If I must boil everything down to just one piece of advice, make sure you’re working with an experienced, capable and ethical consultant who cares about your success. As long as you’ve made sure of this, the resulting experience should meet your expectations.
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