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Jieun Kim

Data analyst
I find patterns and trends in the data. 
I present data-driven insights with interactive visualizations.

ABOUT ME

Data-driven solutions 

for every need

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Welcome! I am Jieun, a consulting data analyst and Economist.

I hold a Master’s degree in Economics, University of Bonn. 

Fascinated by delivering values in the data, I have paved my unique way to be a data analyst. With solid economics and statistical backgrounds, 

my work experiences - across the national bank, accounting, consulting, and data-driven travel platform - have enriched my practical knowledge and skills with the analytical mindset. 

I find the essence of data analysis in uncovering insightful patterns and trends in the data, empowering us to ask more meaningful questions.

With my expertise in a holistic analysis, I can help you understand the structure, characteristics, and potential issues of your data. I can support you by identifying insights and hypotheses that I can further investigate through more advanced analysis techniques. 

Iyou'd like to explore the data from fresh perspectives, I would be happy to hear from you and share my insights!

Languages

Python, SQL, R, STATA

​ETL

  • Data Extraction  

  • Transformation 

  • Load 

Data analysis

  • Trend

  • Prediction

  • Recommendation

  • Strategy 

Visualization

  • Dashboard

  • Reporting

with Tableau, Power BI, Excel

PORTFOLIO

Recent Projects

Here you can find my projects by category: Reporting & Dashboards, Python & SQL Analysis  

Click the View All button to see all the projects.

Reporting & Dashbards
Financial Report for Blumen Tech_edited.jpg

Featured Project

In this report, I provide the comprehensive financial analysis of the virtual company called Blumen Tech. All analysis results and dashboards are produced exclusively with Power BI. The contents are as follows:

  • Income Statement

  • Financial Details

  • Balance Sheet

  • Cash Flow

  • Aged Trials Balance

  • Revenue Insights

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Tableau

With a chrun dataset of a fictious Telecom company, I discover why customers are leaving and offer insights to reduce churn. 

  • Calculate chrun rates, investigate churn reasons, and map churn rates by state. 

  • Investigate churn patterns by demographics, payment methods, contract types, and service tenures. 

  • Interactive dashboard with KPIs.

Image by Dominik Lückmann

Power BI

The goal of this case study is to build a comprehensive report using a fictional Sales and Market share dataset for a manufacturing company called Sintec

  • Top N analysis

    • Who are the top competitors by revenue?

  • Best performing items

    • What are the best performing segments and products?

  • Market share analysis

    • What is the total market share for the specific year?

  • % Growth calculation

    •  What is the % Growth for the Sintec UE-05 product under the Extreme category in 2021?

Python & SQL Analysis
Image by Tim Johnson

With comprehensive analysis of the Android app market, I aim to devise strategies to drive growth and retention of mobile apps.

  • App pricing trend across categories with the strip plot.

  • User's preference to weights of apps.

  • Distribution of app ratings and the average performance of apps.

  • Sentiment analysis of user reviews with polarity scores for paid and free apps.

Image by Mika Baumeister

I write SQL queries to calculate the key metrics to measure the company's performance and produce report-ready results. I use data from a fictional food delivery startup, called Delivr, modeled on data from real companies.

  • Profitability

  • User-centric metrics.

  • Unit economics and distribution

  • Executive report

SQL

Python

Featured Project

Python

Statistical inference rests upon probability. We use probabilistic language to make quantitative statements about data. 

This is why the last crucial step of a data analysis pipeline hinges on the principles of statistical inference. This project aims to build the foundation to think statistically.

  • Graphical and Quantitative EDA

    • US 2008 president election ​

    • Summary stats and plot ECDF

  • Think Probabilistically

    • Hacker statistics

    • Bank loan default​

    • Major league no-hitter and hitter cycles

    • ​Distributions: Normal, Binomial, Poisson, Exponential

Image by Alexander Shatov

Python

With comprehensive analysis of the Android app market, I aim to devise strategies to drive growth and retention of mobile apps.

  • App pricing trend across categories with the strip plot.

  • User's preference to weights of apps.

  • Distribution of app ratings and the average performance of apps.

  • Sentiment analysis of user reviews with polarity scores for paid and free apps.

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