Work Experience Writing Toolkit About Let’s talk ↗
Open to data analyst roles · 2026

Data, made
legible.

I’m Jakkawat Sukkasam — ป๋วย to most people — a data analyst in Chonburi, Thailand. I clean, question, and visualise numbers until they say something a business can actually act on.

Selected work The short version
Focus

Analytics · reporting · forecasting

Stack

SQL · Python · R · Excel

Base

Sriracha, Chonburi · TH

fig. 01 — the field
n = every question so far
Points are tools I reach for; lines are how they connect.
01 Selected work

Built to answer one question well.

Two tools I designed and shipped on my own — one is live, the other runs my own bookkeeping. Both started as questions I actually had.

Case 01 · Finance · Streamlit

JustPortOpt

Pick a set of tickers, set your weights, and see where your portfolio sits against 3,000 simulated ones on the efficient frontier.

PythonStreamlityfinancePlotlyNumPyMonte Carlo
Live demo ↗ GitHub ↗
+ Details

The problem. Choosing portfolio weights by feel hides the trade-off you are actually making — a little more expected return usually costs a little more risk.

What it does. Pulls one year of daily closes from Yahoo Finance, annualises return and volatility from the covariance matrix, then simulates 3,000 random weightings. Your portfolio is plotted as a star over that cloud, coloured by Sharpe ratio, with the max-Sharpe portfolio marked as the reference point.

Scope. An educational tool, not investment advice. Prices come from yfinance, the risk-free rate is assumed to be zero, and nothing is persisted between sessions.

YOURS MAX SHARPE RISK →RETURN ↑
schematic · see live demo
Case 02 · Personal finance · Streamlit

Personal Expense Tracking

A spending dashboard fed by a Google Form: log an expense on your phone, open the app, and see today, this week, or this month — without touching a spreadsheet.

PythonStreamlitpandasPlotlyGoogle Sheets
GitHub ↗
+ Details

The problem. Tracking spend in a spreadsheet works right up until the spreadsheet itself becomes the chore.

What it does. Reads the published CSV of a Google Form response sheet, converts submission times to Bangkok time, filters to today, this week, or this month, then renders a category bar chart, a spend-trend area chart, category averages, and the last ten transactions.

Scope. A personal tool pointed at a private sheet — the repository is public, but there is no public demo because the data is mine.

THIS MONTH · THB FOOD & LIFESTYLETRANSPORTSHOPPINGBILLSOTHER TREND · 30 DAYS
schematic · categories, not real amounts

Also on GitHub: DSB11 — the data-science bootcamp curriculum I worked through, sprint by sprint, from Google Sheets and SQL to R, Python, and Looker Studio.

02 Experience

Learning where the numbers meet reality.

One internship, a lot of spreadsheets, and the habit of checking a total twice before it leaves my desk.

Dec 2025 — Apr 2026

Data Analyst Intern

Data First Co., Ltd. · Bangkok
  • Wrote and maintained SQL / BigQuery queries behind recurring reports, keeping definitions consistent between runs.
  • Prepared raw datasets in Python (pandas) and Excel Power Query — deduplicating, reshaping, and documenting what was changed.
  • Turned findings into charts and short decks for readers who needed the answer, not the method.
  • Reviewed outputs with senior analysts before delivery; kept a note of every assumption and edge case.
FocusSQL · BigQuery
Python · Excel
2022 — 2026

BBA, Finance

Naresuan University · Phitsanulok
  • Coursework across statistics, econometrics, corporate finance, and business analytics.
  • Final-year work centred on applied analysis rather than theory: cleaning real datasets, then defending the result.
ResultGPA 3.34
Graduated 2026
03 Writing

Explaining statistics in Thai.

Eight posts and counting on probability, machine learning, and marketing analytics — the explanations I wanted when I was learning them.

Full archive: puay101.com ↗ — written in Thai, mostly in Python and R, with the working shown.

04 Toolkit

Tools I reach for, honestly rated.

No bars inflated to full. What follows is what I can do on a Monday morning without looking it up.

Analysis

Python — pandas, NumPy, statsmodels

Confident

Query

SQL — joins, CTEs, window functions; BigQuery

Confident

Statistics

R — tidyverse, ggplot2, regression

Working

Spreadsheets

Excel — PivotTables, Power Query, model building

Confident

Modelling

Forecasting, marketing mix (MBA/MMM), scenario work

Working

Communication

Data viz — seaborn, ggplot2, Google Sheets dashboards

Confident

Engineering

Git, scheduled jobs, REST APIs, Cloudflare

Working

Languages

Thai native · English working proficiency

Mixed

Still learning, and saying so: Power BI, dbt, and warehouse modelling at production scale. I’d rather be useful on day one with SQL and Python than claim a tool I’ve only opened twice.

05 Education

The foundation behind the work.

Finance taught me what the numbers are supposed to mean; analytics taught me how to check whether they do.

2022 — 2026 · Phitsanulok

B.B.A. Finance

Naresuan University

Statistics, econometrics, corporate finance, and business analytics — with an increasing share of the coursework done in Python, R, and SQL.

GPA3.34
Graduated2026
DegreeB.B.A.
Dec 2025 — Apr 2026 · Bangkok

Data Analyst Internship

Data First Co., Ltd.

First real exposure to production reporting: SQL that other people depend on, datasets that arrive dirty, and the difference between a correct number and a trusted one.

Duration5 months
TeamAnalytics
06 About

A little context.

JS
13.17° N, 100.93° E — Sriracha

I studied finance because I wanted to know how businesses actually make money. I stayed for the part I enjoyed most: the numbers underneath the story.

Since then I’ve been building toward analytics as a craft — clean the data, state the assumption, show the working, then answer the question that was asked. My internship put me inside real reporting cycles; my own projects put me at the other end, deciding what a useful output even looks like.

I work best when a question is still fuzzy. Give me a messy export and a decision that needs to be made, and I’ll come back with the smallest analysis that settles it — plus a note on what the data can’t tell you.

01

Check the denominator before the chart.

02

Write down the definition, then reuse it everywhere.

03

If a number can’t be explained in one sentence, it isn’t finished.

07   Contact

Hiring for a data role?
Let’s talk.

puay101@gmail.com
Email GitHub ↗ Blog ↗ Work
© 2026 Jakkawat Sukkasam · Sriracha, Thailand
Local time --:-- ICT
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