8 answers
yujoe
Student
Jinan, Shandong, China
1
Question
Updated
1725 views
Request for Career Advice: Data Analyst vs. Financial Researcher Path?
I completed my undergraduate studies in Economics at an ordinary university. Through hard work, I was admitted to a prestigious university for my master’s degree, which is an excellent institution. However, after enrolling, I realized that most of my classmates had studied at top universities for their bachelor’s degrees, so my background feels somewhat less competitive in comparison.
Currently, I am pursuing a master’s degree in Applied Statistics, and I will be entering the job market next year. At this stage, I feel rather uncertain about my career path—whether I should become a data analyst or a financial researcher. I am not sure if my current abilities are strong enough, nor do I know what specific skills I should focus on developing to make myself more qualified for these roles.
Another concern is that I have no work experience so far. When I apply for internships related to these positions, I am often rejected due to my lack of prior experience. This situation makes me feel even more anxious about my future career development.
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8 answers
Lin Jia
Data Scientist
16
Answers
Amsterdam, North Holland, Netherlands
Updated
Lin’s Answer
First, I want to acknowledge how you're feeling. It’s completely normal to feel uncertain and even a bit of imposter syndrome, especially when you're in a demanding, high-achieving environment. Please don't discount your own journey—working your way from an "ordinary" university to a prestigious master's program demonstrates incredible drive, intelligence, and resilience. That story of growth is a huge asset, not a weakness. Your background in Economics and Applied Statistics is a powerful combination that gives you a unique edge.
Your Secret Weapon: An Edge in Causal Inference
Your economics background is a massive advantage because of causal inference. Most data analysis finds correlations (what happened together), but your training helps you prove causation (what caused it to happen).
This skill is highly valued by top companies to answer critical questions like, "Did our marketing campaign actually cause sales to increase?" This opens a third, high-demand career path: Data Scientist (Causal Inference) at major tech firms.
Two Key Career Paths
The skills for these roles overlap significantly. The main difference is the questions you answer.
📊 The Data Analyst
Mission: Solves business problems across any industry. Asks: "What do the data tell us about improving our business?"
Core Skills: SQL, Python/R, Tableau/Power BI, A/B Testing.
📈 The Financial Researcher (or "Quant")
Mission: Aims to find a statistical edge in financial markets. Asks: "How can we use data to predict the market?"
Core Skills: Advanced Python, Econometrics, Time-Series Analysis, Market Knowledge.
Action Plan for the "No Experience" Trap
When you can't get a job to gain experience, you must create your own. A portfolio of projects is more valuable than an empty resume.
1. Create Your Experience:
Build Personal Projects (Highest Priority): Find a dataset on a topic you love, solve a problem, and present your findings. This is your single best way to demonstrate skill.
Enter Competitions: Use platforms like Kaggle to tackle real-world problems.
Contribute to Open Source: Start small by improving documentation for a library you use.
2. Showcase Your Work:
Develop a GitHub Portfolio: Treat your GitHub as your professional resume. Document each project with a clear README.md file explaining your process and results.
Optimize Your LinkedIn Profile: Share your projects and connect with professionals.
3. Leverage Your Network:
Start at Your University: This is your most valuable resource. Join clubs, use career services, and ask professors if you can assist with their research.
Attend Meetups: Learn about industry trends and meet people in the field.
By actively building your own experience, you show the exact initiative that companies look for. You are in a much stronger position than you think.
Your Secret Weapon: An Edge in Causal Inference
Your economics background is a massive advantage because of causal inference. Most data analysis finds correlations (what happened together), but your training helps you prove causation (what caused it to happen).
This skill is highly valued by top companies to answer critical questions like, "Did our marketing campaign actually cause sales to increase?" This opens a third, high-demand career path: Data Scientist (Causal Inference) at major tech firms.
Two Key Career Paths
The skills for these roles overlap significantly. The main difference is the questions you answer.
📊 The Data Analyst
Mission: Solves business problems across any industry. Asks: "What do the data tell us about improving our business?"
Core Skills: SQL, Python/R, Tableau/Power BI, A/B Testing.
📈 The Financial Researcher (or "Quant")
Mission: Aims to find a statistical edge in financial markets. Asks: "How can we use data to predict the market?"
Core Skills: Advanced Python, Econometrics, Time-Series Analysis, Market Knowledge.
Action Plan for the "No Experience" Trap
When you can't get a job to gain experience, you must create your own. A portfolio of projects is more valuable than an empty resume.
1. Create Your Experience:
Build Personal Projects (Highest Priority): Find a dataset on a topic you love, solve a problem, and present your findings. This is your single best way to demonstrate skill.
Enter Competitions: Use platforms like Kaggle to tackle real-world problems.
Contribute to Open Source: Start small by improving documentation for a library you use.
2. Showcase Your Work:
Develop a GitHub Portfolio: Treat your GitHub as your professional resume. Document each project with a clear README.md file explaining your process and results.
Optimize Your LinkedIn Profile: Share your projects and connect with professionals.
3. Leverage Your Network:
Start at Your University: This is your most valuable resource. Join clubs, use career services, and ask professors if you can assist with their research.
Attend Meetups: Learn about industry trends and meet people in the field.
By actively building your own experience, you show the exact initiative that companies look for. You are in a much stronger position than you think.
Lusine Madoyan
Analytics
4
Answers
Los Angeles, California
Updated
Lusine’s Answer
There is a plethora of data-centric roles—ranging from Marketing Analytics and Product Analytics to Portfolio Management and Risk Modeling—and the only way to find your fit is to get that front-row experience. Don’t let a lack of experience paralyze you; treat the application process like a data project itself. Focus on applying aggressively for internships that allow you to flex your statistical muscles, as internships are the primary bridge to full-time offers for Master's students. Whether you end up in the fast-paced world of Financial Research or the strategic domain of Product Analytics, your ability to handle complex datasets is a universal currency. Apply, apply, apply—once you get that first real-world project under your belt, your background will matter far less than the extraordinary statistical value you bring to the table.
Stefany Pham
Business Analyst
1
Answer
Boston, Massachusetts
Updated
Stefany’s Answer
Congratulations on getting into the master's program!!
Here are some pieces of advice if you'd like to get into data analytics:
1. Focus on practical data analysis projects in your program: If your master's program offers classes where you can practice solving business problems with data analysis, please dedicate more efforts to those as they can become valuable content for you to speak about in your interviews, especially as a fresh graduate. During the projects, work on identifying the business problem you want to solve, what kind of data you use to answer those questions, what analysis methodologies/models you use and the pros and cons of those models and how you evaluate the accuracy of the model. Also reflect on your experience and understand what you could have done better in the project as those questions often come up in interviews.
2. Understand the statistical foundations of your methodologies: As AI becomes better at helping you code and generating model results, statistics knowledge becomes more important to understand how AI arrives at an answer and evaluate the accuracy of those answers. So dedicate more time to learn statistics theory
3. Be proficient in coding tools: The most important tools for data analysis are SQL, Tableau/Power BI and Python.
Hope this helps!
Here are some pieces of advice if you'd like to get into data analytics:
1. Focus on practical data analysis projects in your program: If your master's program offers classes where you can practice solving business problems with data analysis, please dedicate more efforts to those as they can become valuable content for you to speak about in your interviews, especially as a fresh graduate. During the projects, work on identifying the business problem you want to solve, what kind of data you use to answer those questions, what analysis methodologies/models you use and the pros and cons of those models and how you evaluate the accuracy of the model. Also reflect on your experience and understand what you could have done better in the project as those questions often come up in interviews.
2. Understand the statistical foundations of your methodologies: As AI becomes better at helping you code and generating model results, statistics knowledge becomes more important to understand how AI arrives at an answer and evaluate the accuracy of those answers. So dedicate more time to learn statistics theory
3. Be proficient in coding tools: The most important tools for data analysis are SQL, Tableau/Power BI and Python.
Hope this helps!
Adeola Raji
Academic Research/Consultation
11
Answers
Ede, Osun, Nigeria
Updated
Adeola’s Answer
Hi, Yujoe
I can really relate to your situation, and I want to reassure you that you’re not alone. Many students feel the same way when transitioning from school to the job market, especially when they compare themselves to classmates with different backgrounds. But your path actually shows resilience, you worked your way from an ordinary university to a top master’s program, and that speaks volumes about your ability to grow and succeed.
Here’s a few things I’d recommend:
Build hands-on skills now. Whether you’re leaning toward data analysis or financial research, employers look for practical ability. Try working on personal projects, analyzing datasets you find online, joining Kaggle competitions, or contributing to open-source projects. These can serve as “experience” you can show in your portfolio, even if you haven’t had a formal job yet.
Target internships strategically. Since rejection is normal early on, consider smaller firms, startups, or research labs. They’re often more flexible with experience requirements, and you’ll still gain valuable skills to put on your resume.
Clarify your direction. If you enjoy programming, statistics, and working with data directly, a data analyst (or data scientist) role might be a good fit. If you’re more interested in economics, markets, and applying data to financial questions, financial research may suit you better. Try informational interviews with people in both fields to see what excites you most.
Leverage your network. Many opportunities don’t come through job postings, but through professors, classmates, or alumni. Let them know you’re eager to gain experience, they may connect you to internships or research assistant roles.
Don’t underestimate your story. Employers value determination. Being able to say, “I worked my way into one of the best universities and built strong statistical skills along the way” is actually a great narrative, it shows persistence and drive.
Don’t panic about not having everything figured out right away. Careers in data and finance often evolve step by step. Focus on building your skills, getting any experience you can, and keeping an open mind. Each opportunity will help you get clearer about where you want to go.
I can really relate to your situation, and I want to reassure you that you’re not alone. Many students feel the same way when transitioning from school to the job market, especially when they compare themselves to classmates with different backgrounds. But your path actually shows resilience, you worked your way from an ordinary university to a top master’s program, and that speaks volumes about your ability to grow and succeed.
Here’s a few things I’d recommend:
Build hands-on skills now. Whether you’re leaning toward data analysis or financial research, employers look for practical ability. Try working on personal projects, analyzing datasets you find online, joining Kaggle competitions, or contributing to open-source projects. These can serve as “experience” you can show in your portfolio, even if you haven’t had a formal job yet.
Target internships strategically. Since rejection is normal early on, consider smaller firms, startups, or research labs. They’re often more flexible with experience requirements, and you’ll still gain valuable skills to put on your resume.
Clarify your direction. If you enjoy programming, statistics, and working with data directly, a data analyst (or data scientist) role might be a good fit. If you’re more interested in economics, markets, and applying data to financial questions, financial research may suit you better. Try informational interviews with people in both fields to see what excites you most.
Leverage your network. Many opportunities don’t come through job postings, but through professors, classmates, or alumni. Let them know you’re eager to gain experience, they may connect you to internships or research assistant roles.
Don’t underestimate your story. Employers value determination. Being able to say, “I worked my way into one of the best universities and built strong statistical skills along the way” is actually a great narrative, it shows persistence and drive.
Don’t panic about not having everything figured out right away. Careers in data and finance often evolve step by step. Focus on building your skills, getting any experience you can, and keeping an open mind. Each opportunity will help you get clearer about where you want to go.
Goodera Careervillage
63
Answers
Bengaluru, Karnataka, India
Updated
Goodera’s Answer
Hi Yujoe,
I hope you reach all your goals! If you've gotten into a top university, pick what feels right for you. For me, financial research offers a great chance for a stable career, but it might be different for you. It’s not just about math and stats; it also involves networking and understanding financial markets.
If you're interested in this field, consider starting with unpaid internships at small boutique firms related to your interests. This shows your commitment and eagerness to learn. Instead of only applying on LinkedIn, try reaching out to companies directly. Build a network and let them know about you before applying for internships. This way, you might hear about opportunities first or even create one where none existed.
Enjoy the journey and wishing you the best!
I hope you reach all your goals! If you've gotten into a top university, pick what feels right for you. For me, financial research offers a great chance for a stable career, but it might be different for you. It’s not just about math and stats; it also involves networking and understanding financial markets.
If you're interested in this field, consider starting with unpaid internships at small boutique firms related to your interests. This shows your commitment and eagerness to learn. Instead of only applying on LinkedIn, try reaching out to companies directly. Build a network and let them know about you before applying for internships. This way, you might hear about opportunities first or even create one where none existed.
Enjoy the journey and wishing you the best!
James Constantine Frangos
SOFTWARE ENGINEER SINCE 1972; NUTRITIONIST SINCE 1976.
7094
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Hello Yujoe!
It's a great time to learn Python, Yujoe!
In the field of financial research, a financial analyst gathers and examines financial data to evaluate company performance, market trends, and economic conditions. They create reports, make forecasts, and support decision-making to enhance financial health and meet goals. Their tasks include analyzing financial statements, tracking market trends, helping with budgeting, and presenting findings to stakeholders. This expertise can be used in business consulting.
Data analysis, financial research, and business consulting are connected fields. Analysts use data to find insights that guide business strategies and improve financial outcomes. A financial data analyst interprets financial data, a data scientist sets up data systems, and analytics consulting uses data to boost business performance and profits through various analysis methods: descriptive (explaining what happened), diagnostic (explaining why it happened), predictive (forecasting future events), and prescriptive (suggesting actions).
Key Skills and Activities:
- Data Interpretation: Using statistics to understand datasets.
- Reporting and Visualization: Making financial reports and using visuals to share insights.
- Strategic Planning: Using data to find opportunities, reduce risks, and shape strategies.
- Technical Proficiency: Knowing programming, database design, and data capture is vital for data scientists.
How These Fields Connect:
- Financial Research: Uses data to understand market trends and company performance.
- Business Consulting: Uses insights to help clients improve their financial health and strategies.
- Data Analysis: Provides the foundation for both financial researchers and business consultants.
In China, the market is complex. Companies use consulting firms like Daxue Consulting and Caixin Insight for data analysis, financial research, and business consulting to understand consumer behavior and market dynamics. These firms use technology-driven data collection and qualitative research to provide strategic insights and support informed decisions.
Key Services Offered:
- Market Analysis: Research to understand market size, growth, competition, and consumer segments in China.
- Consumer Understanding: Identifying the needs and behaviors of Chinese consumers.
- Financial Research & Consulting: Macroeconomic analysis and risk assessment for financial institutions and businesses.
- Business Intelligence: Analyzing data from various sources to provide actionable insights.
- Strategic Consulting: Developing strategies for market entry and navigating China's economic environment.
How They Operate:
- Data-Driven Approach: Using tech for data collection and analysis to turn raw data into useful insights.
- Local Expertise: Employing local experts with deep knowledge of the Chinese market.
- Customized Solutions: Tailoring research to meet specific client needs.
- Integrated Services: Combining market research, consumer insights, financial data, and strategic guidance for a comprehensive view.
Examples of Consultancies:
- Daxue Consulting: Offers market analysis and consumer insights in China with custom research methods.
- Caixin Insight: Provides big-data solutions and consultancy for navigating China's financial markets.
Take care!
It's a great time to learn Python, Yujoe!
In the field of financial research, a financial analyst gathers and examines financial data to evaluate company performance, market trends, and economic conditions. They create reports, make forecasts, and support decision-making to enhance financial health and meet goals. Their tasks include analyzing financial statements, tracking market trends, helping with budgeting, and presenting findings to stakeholders. This expertise can be used in business consulting.
Data analysis, financial research, and business consulting are connected fields. Analysts use data to find insights that guide business strategies and improve financial outcomes. A financial data analyst interprets financial data, a data scientist sets up data systems, and analytics consulting uses data to boost business performance and profits through various analysis methods: descriptive (explaining what happened), diagnostic (explaining why it happened), predictive (forecasting future events), and prescriptive (suggesting actions).
Key Skills and Activities:
- Data Interpretation: Using statistics to understand datasets.
- Reporting and Visualization: Making financial reports and using visuals to share insights.
- Strategic Planning: Using data to find opportunities, reduce risks, and shape strategies.
- Technical Proficiency: Knowing programming, database design, and data capture is vital for data scientists.
How These Fields Connect:
- Financial Research: Uses data to understand market trends and company performance.
- Business Consulting: Uses insights to help clients improve their financial health and strategies.
- Data Analysis: Provides the foundation for both financial researchers and business consultants.
In China, the market is complex. Companies use consulting firms like Daxue Consulting and Caixin Insight for data analysis, financial research, and business consulting to understand consumer behavior and market dynamics. These firms use technology-driven data collection and qualitative research to provide strategic insights and support informed decisions.
Key Services Offered:
- Market Analysis: Research to understand market size, growth, competition, and consumer segments in China.
- Consumer Understanding: Identifying the needs and behaviors of Chinese consumers.
- Financial Research & Consulting: Macroeconomic analysis and risk assessment for financial institutions and businesses.
- Business Intelligence: Analyzing data from various sources to provide actionable insights.
- Strategic Consulting: Developing strategies for market entry and navigating China's economic environment.
How They Operate:
- Data-Driven Approach: Using tech for data collection and analysis to turn raw data into useful insights.
- Local Expertise: Employing local experts with deep knowledge of the Chinese market.
- Customized Solutions: Tailoring research to meet specific client needs.
- Integrated Services: Combining market research, consumer insights, financial data, and strategic guidance for a comprehensive view.
Examples of Consultancies:
- Daxue Consulting: Offers market analysis and consumer insights in China with custom research methods.
- Caixin Insight: Provides big-data solutions and consultancy for navigating China's financial markets.
Take care!
Cesar Alonso
Head of Strategic Impact
8
Answers
Madrid, Community of Madrid, Spain
Updated
Cesar’s Answer
Hi Yujoe,
Don't worry too much about where you got your degree. We've all been in your shoes, so try to relax a bit.
It's normal to face rejection when applying for internships, but don't let that stop you. Consider creative options, like reaching out to small businesses that might need help but can't afford a business analyst. Propose a project where you gain experience and earn a bit of money.
The main goal is to gain experience and show that you can connect with the business world, which is a valuable skill. Believe in yourself!
Wishing you all the best.
Don't worry too much about where you got your degree. We've all been in your shoes, so try to relax a bit.
It's normal to face rejection when applying for internships, but don't let that stop you. Consider creative options, like reaching out to small businesses that might need help but can't afford a business analyst. Propose a project where you gain experience and earn a bit of money.
The main goal is to gain experience and show that you can connect with the business world, which is a valuable skill. Believe in yourself!
Wishing you all the best.
David Seides
Finance
29
Answers
Alpharetta, Georgia
Updated
David’s Answer
Getting into a top grad school is a big achievement, especially considering the college you attended. This shows your strength and determination, which many employers will admire. As you move forward in your career, the focus will be on where you earned your last degree, and your undergrad school won't be as important.