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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. 

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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. 

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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. 

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If you'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

Financial Report for Blumen Tech  : Company Performance for 4 years

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

Prague telecom tower._edited.jpg

Tableau

End-to-end Telecom Customer Chrun Analysis

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

Case Study: Competitor Sales Analysis 

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
HR Analytics:
Predicting Employee Turnover with Decision Trees

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

Analyzing Business Data in SQL

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 Thinking in 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

The Android App Market on Google Play

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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