Lecture 9

Career Paths in Data Analytics and Beyond

Byeong-Hak Choe

bchoe@geneseo.edu

SUNY Geneseo

👥 Andy and Oliver

October 30, 2026

👥 Meet Andy and Oliver

Profiles at the October 30, 2026 session

Jaehyung “Andy” Lee

Geneseo Class of 2022

Major: Mathematics

Concentration: Data Analytics

Founder, UZU Studio

Previously an Analytics Associate at Invisible Technologies

Data pipelines, dashboards, and a startup supporting dental-practice owners

Oliver Stordahl

Geneseo Class of 2022

Major: Economics

Minors: Mathematics and Data Analytics

Regulatory Data Analyst, Federal Reserve Bank of New York

Bank-data checks, follow-up with reporting firms, and monthly reports

Profile facts and portraits: supplied October 30 session poster. Academic backgrounds: October 24, 2025 career-session lecture and the instructor’s update. Work descriptions: Oliver’s opening introduction and monthly-work explanation; Andy’s introduction at 1:18–4:13. These roles describe the time of the recorded session.

👋 What are your career backgrounds and current roles?

4:05

Source: 2026-10-30 career session, original recording 00:00:00.000–00:01:08.125; 00:01:16.688–00:04:14.438.

💼 How did you find your first job, and what helped you prepare for interviews?

2:58

Source: 2026-10-30 career session, original recording 00:05:08.000–00:08:06.938.

🗓️ What does your day-to-day work look like?

3:52

Source: 2026-10-30 career session, original recording 00:08:38.188–00:12:31.125.

🧭 How did you handle uncertainty about your career after graduation?

2:13

Source: 2026-10-30 career session, original recording 00:13:12.812–00:15:26.125.

🔎 How did you discover the kind of work you wanted to pursue?

2:14

Source: 2026-10-30 career session, original recording 00:15:46.000–00:18:00.625.

🛠️ Which skills matter most at work, and how did college help you develop them?

3:45

Source: 2026-10-30 career session, original recording 00:18:50.812–00:22:36.250.

🤖 How is AI changing your work and the job market?

3:35

Source: 2026-10-30 career session, original recording 00:23:20.750–00:26:56.125.

🎓 How can students make the most of college to prepare for their careers?

2:23

Source: 2026-10-30 career session, original recording 00:27:40.562–00:30:04.188.

🎙️ Marcie Hogan

November 4, 2026

🎙️ Meet Marcie Hogan

Profile at the November 4, 2026 session

Marcie Hogan

Geneseo Class of 2023

Geography major with minors in physics and mathematics

Product Marketing and Customer Education Lead at Cyvl, a Boston government-technology startup

Earlier experience at Brookhaven National Laboratory, Crown Castle, Meta Mapillary, and the MBTA

Uses data in customer education, dashboards, and product strategy

Profile and portrait: supplied November 4 session poster and Marcie’s introduction at 8:14. Her career history is described at 9:40–20:38. Cyvl is the spelling on the source presentation and poster. Roles describe the time of the recorded session.

👋 What is your background, and what do you do at Cyvl?

1:42

Source: 2026-11-04 career session, original recording 00:07:55.000–00:09:37.625.

🗺️ How did internships and early roles shape your career in data and geospatial technology?

7:25

Source: 2026-11-04 career session, original recording 00:09:37.625–00:17:03.125. The heading frames the topic of Marcie’s prepared talk.

🧭 What did changing jobs teach you about finding the right career fit?

3:40

Source: 2026-11-04 career session, original recording 00:17:03.125–00:20:43.812. The heading frames the topic of Marcie’s prepared talk.

🚀 What is startup work like at Cyvl, and how does the pay compare?

5:49

Source: 2026-11-04 career session, original recording 00:20:58.000–00:26:18.250; 00:26:44.250–00:27:13.750. The heading frames the topic of Marcie’s prepared talk.

🤖 Why do strong data skills still matter when AI can write code and build dashboards?

4:30

Source: 2026-11-04 career session, original recording 00:27:13.750–00:31:43.750. The heading frames the topic of Marcie’s prepared talk.

🌱 What career lessons helped you find work you enjoy and continue growing?

2:37

Source: 2026-11-04 career session, original recording 00:31:43.750–00:34:20.812. The heading frames the topic of Marcie’s prepared talk.

🤝 How can students find job opportunities and build authentic professional connections?

5:02

Source: 2026-11-04 career session, original recording 00:34:20.812–00:39:23.250. The heading frames the topic of Marcie’s prepared talk.

🗓️ What does a typical day look like in your role at Cyvl?

2:22

Source: 2026-11-04 career session, original recording 00:39:45.938–00:42:08.062.

🔗 How did a LinkedIn conversation turn into a job offer at Cyvl?

1:40

Source: 2026-11-04 career session, original recording 00:42:20.938–00:44:01.562.

💻 If you could learn only one programming language, which would you choose and why?

1:03

Source: 2026-11-04 career session, original recording 00:44:14.812–00:45:18.500.

🌟 How can students manage career uncertainty and prepare for their first internship?

2:55

Source: 2026-11-04 career session, original recording 00:45:53.875–00:48:49.000.

🧭 Career lessons and my thoughts

Shared themes and career preparation

🧭 Career uncertainty and finding a fit

From the guests

• Andy and Oliver: interests in programming, problem solving, and economics gave them starting points. Their first jobs helped clarify what they enjoyed.
• Marcie: research, GIS, product work, and other roles taught her what fit and when to change direction.

My thoughts

I also felt uncertain as an undergraduate, and my own path included unexpected turns. Meet people, gather information, and take informed risks as you refine your next step.

Combines October 30 questions on uncertainty and discovering interests (12:33–18:04) with Marcie’s career changes and uncertainty answer (17:07–20:38 and 45:52–48:20). Instructor reflection paraphrases Choe’s closing recaps on October 30 (31:00 onward) and November 4 (classroom recap around 49:51–55:10).

🧪 Projects and first internships

From the guests

• Andy: a meaningful project gave him work he could explain deeply in interviews.
• Marcie: courses, physics research, and campus experience helped her obtain internships. Her Crown Castle internship became her first full-time job.
• Oliver (2025 session): Fed Challenge and FDIC Challenge provided applied data work, teamwork, and concrete interview stories.

My thoughts

Use college to develop critical thinking and creative problem solving. The exact material may differ from your future job, but learning how to approach an unfamiliar problem carries over.

Andy’s project: October 30, 6:57–8:08. Marcie’s internships: November 4, 10:19–14:46; preparation for a first internship: 45:52–48:20. Oliver’s competition example is from the October 24, 2025 lecture, “Experiential Learning: Competitions,” rather than the 2026 recording. FDIC stands for Federal Deposit Insurance Corporation. Instructor reflection paraphrases Choe’s October closing recap: critical thinking and creative problem solving remain useful even when course material does not directly match the job.

🤝 Finding opportunities

From the guests

• Oliver: broad applications and deliberate interview practice were his route into work.
• Andy: interviewing and networking improve with practice.
• Marcie: follow people in your field, have authentic coffee chats, and consider roles beyond the title “data analyst.”

My thoughts

Use the Career Center and Geneseo network. Combine outreach with preparation so you can demonstrate your skills when an opportunity appears.

October 30, 5:14–8:08; November 4, 34:23–39:25 and 42:22–44:03. This combines applications, interviews, LinkedIn, and networking. Choe’s November recap explicitly encourages campus resources, preparation, and networking. No single route guarantees an offer.

🗓️ Daily work and workplace fit

From the guests

• Oliver: a monthly cycle of bank-data checks, questions, reporting, and process improvements.
• Andy and Marcie: stakeholder or customer conversations, technical work, and varied startup responsibilities. Andy also described work–life balance challenges.

My thoughts

Technical skills work alongside communication and business judgment. Understand the problem people are trying to solve and explain what your analysis means for them.

October 30 day-to-day Q&A, 8:40–12:26; November 4 startup discussion, 20:46–26:38, and typical-workday answer, 39:47–42:05. Instructor reflection paraphrases Choe’s November classroom recap on quantitative skills, communication, and business intuition. The guests’ startup experiences and pay comments describe their own circumstances.

🛠️ Data foundations across careers

From the guests

• Andy: course skills helped with a hiring task.
• Oliver: learning to transform and visualize data helped him solve unfamiliar problems independently.
• Marcie: data skills remained useful as her role shifted into product marketing and customer education. Python, R, and SQL can each provide a foundation.

My thoughts

Not everyone needs to become a data analyst. Quantitative reasoning helps across careers. Learn the logic in R—transforming data, comparing groups, and interpreting patterns—and carry it into new tools and roles.

October 30, 18:51–22:38; November 4, 27:17–31:44 and 44:15–45:20. Instructor reflection paraphrases Choe’s quantitative-skills comments in both recaps and the programming-language follow-up around 1:00:20–1:01:40 in the November recording.

💻 Languages, dashboards, and Excel

My thoughts

• Python and R: both respected; Python is common in industry, R in statistics and research.
• SQL: query, join, and summarize database tables.
• Dashboards: practice Tableau and Power BI.
• Excel: clean and summarize data, and check results.
• NYS Data Analyst 1: preferred tools include Python, R, SQL, and Tableau; duties include data wrangling and clear reporting.

Revisit Lecture 2: R vs. Python—IBM overview

This refines Choe’s November programming-language follow-up and his clarification for this deck. O*NET’s Business Intelligence Analysts table uses Lightcast U.S. job postings from January–December 2025: SQL 35%, Python 20%, R 10%. These are shares of postings mentioning software, not shares of employed workers or a ranking of all analyst skills. The ONS identifies both R and Python as core languages for analysis workflows. We do not claim R is more popular than Python across all academia or public-sector employers. The practical recommendation is to retain transferable reasoning and add tools based on target roles.

NYS example: supplied “Data Analyst 1 flyer 9.2026-1.pdf,” page 2, Preferred Qualifications and Typical Job Duties. The flyer names Python, R, SQL, and Tableau among several preferred tools and asks for experience with one or more of them. Duties include joining, merging, cleaning, aggregating, and transforming data, presenting results through dashboards and other formats, and writing clearly for policy, business, and technical audiences. Minimum qualifications are separate from these preferred tools. Power BI and Excel are instructor recommendations for broader tool practice. Learn one language well, then add tools that match the work you want to do.

Supporting references: O*NET, https://www.onetonline.org/link/hot_tech/15-2051.01; UK Office for National Statistics, https://www.ons.gov.uk/aboutus/whatwedo/programmesandprojects/therighttools.

🎓 Communication and learning with others

From the guests

• Andy and Oliver: interesting courses, clubs, friendships, and group projects are opportunities to explore and practice collaboration.
• Marcie: explaining ideas to customers and learning from colleagues are central to her work and growth.

My thoughts

Much information is available online. One value of university is learning and working with other people. Use group projects, courses, and campus activities to develop communication skills and learn from your peers.

October 30, communication discussion at 19:11–22:38 and college advice at 27:41–about 30:03; November 4, career-growth discussion at 33:02–34:21 and daily-work answer at 39:47–42:05. Choe’s recaps emphasize communication, peer learning, and the value of a university network.

🤖 AI and analytical judgment

From the guests

• Andy and Marcie: AI changed parts of their coding, reporting, and dashboard workflows. Choosing useful questions and evaluating results still mattered.
• Oliver: entry-level work and training raise difficult questions. He avoided a confident prediction about long-run employment.

My thoughts

I use AI in my work, and I still need the skills to evaluate its output. Analytical understanding helps you decide whether a result makes sense.

Next: What do the labor-market signals tell us, and how can students prepare?

AI is the final merged career topic. October 30, 18:51–26:58; November 4, 27:17–31:44. Choe describes using AI while evaluating the quality of its output in his November classroom recap. Andy’s account of analyst bots at his former employer was secondhand and company-specific. These alumni experiences raise questions; they do not establish an economy-wide employment effect.

🐦 AI and young workers: signals to watch

1. Stanford: finds weaker employment growth among young workers in AI-exposed occupations, mainly through reduced hiring. The authors describe this as observational evidence, not proof of causation. Stanford study
2. Yale: finds no clear AI-related shift in broad labor-market measures so far. Yale analysis

Early evidence suggests weaker hiring for some young workers, while broader employment effects remain unclear.

Brief context before focusing on preparation. Stanford’s revised working paper, August 12, 2026, uses ADP payroll data through June 2026. It reports employment of ages 22–25 in AI-exposed occupations 19% below the path implied by less-exposed peers; this is a relative comparison, not a 19% fall in all young-worker jobs. The authors describe early indicators, not causal estimates. Gaps shrink with education controls, some trends predate generative AI, and national-survey patterns are smaller. Yale’s October 14, 2026 tracker finds no clear AI-related change in the occupational mix or employment/unemployment. Different populations and measures can produce both findings. Neither study estimates a student’s chance of getting a job. The fuller AI unit examines this evidence and its limits.