Hi, I'm Kristy Natasha Yohanes
I am a Data Analyst who loves to immerse myself in technology and hands-on development: experimenting with machine learning, geospatial data analysis, coding challenges, cloud automation, and Raspberry Pi projects to explore data science applications within geoscience and environmental fields. To unwind, I find joy in the occasional thrill of wall climbing and archery during my free time.Here's my contact info, CV, and portfolio of my qualifications. You can download the PDF version of my portfolio (with certifications) below:
# ABOUT ME
I hold a Bachelor's degree in Meteorology from Institute of Technology Bandung (ITB), Indonesia.
My expertise lies in Python programming, specializing in ML, NLP, CV, time-series analysis, and graph networks.
I'm proficient in SQL databases, BI tools, GIS, and cloud automation with GCF & VM.
Projects & Case Studies
Throughout my academic and professional journey, I've actively participated in projects focused on developing forecasting models and conducting weather data analysis utilizing machine learning techniques, notably the hybrid ANN-ARIMA for predicting monsoonal patterns.Additionally, I contributed to community service initiatives by integrating the Weather Research and Forecasting (WRF) computational model to develop advanced flood risk assessment tools and designed user-centric web applications to distribute early warning alerts, enhancing proactive disaster mitigation strategies.
Achievements & Publications
Top 10 Scientific Paper Physics Fair Padjadjaran University
Top 10 Scientific Paper Economic Finance Study Club Diponegoro University
PUBLISHED: Propagation Characteristics of Madden Julian Oscillation in the Indonesian Maritime Continent: Case Studies for 2020-2022, Agromet Journal, doi: 10.29244/j.agromet.38.1.1-12PREPRINT / UNDER REVIEW: An Elementary Approach to Predicting Indonesian Monsoon Index: Combining Ann-Arima Hybrid Method and Practical Use
GitHub: Mock Projects
One of my enjoyable side projects I'm proud of is the YouTube video recommendation insights project. I utilized the YouTube Data API and NLP to analyze users' watch histories, uncovering insights into video recommendations. Key features include OAuth 2.0 integration, transcript analysis, keyword extraction, visualization, and data export capabilities.
Another one is on Customer Goods Data Modeling project that addresses industrial challenges through predictive modeling for daily sales quantity and customer segmentation. It utilizes PostgreSQL and DBeaver for data ingestion, Tableau Public for interactive dashboards, Python in Google Colab for predictive modeling, including time series ARIMA, and clustering techniques.
Professional Certifications
AML and Data Governance: Risk-Based Mentoring Program for Crimes of Money Laundering and Terrorism Financing in Human Trafficking and Financial Technology Crimes - PPATK 2024
Data Science: Certificate of Competencies - Kalbe Nutritionals Data Scientist Project Based Internship Program 2023
Coursework Certifications
Intensive Bootcamp: Data Analysis (MySkill x Deloitte)
Intro to Data Analytics - RevoU
Artificial Intelligence on Microsoft Azure - Microsoft
Data Programming - Sololearn
Data Visualization ShortClass - MySkill
Power BI Essential Training - LinkedIn Learning
SQL for Data Science - UC Davis
Python for Data Science, AI & Development - IBM
The Full Stack - Meta
Companies and Climate Change - ESSEC Business School
The Science of The Solar System - Caltech
The Evolving Universe - Caltech
WORK EXPERIENCE
Developed fraud monitoring dashboards, optimizing SQL queries in BigQuery and utilizing Looker Studio Dashboard, leading to reduced time and storage costs for projects.
Implemented a machine learning-based scoring system to detect collusion and syndicate activities between merchants and customers, enhancing visibility of fraudulent activities.
Collaborated with AI/ML team to automate cloud processes using Cloud Function and VM, integrating Python code and applying techniques like Principal Component Analysis (PCA) for efficient feature engineering and parameter storage in BigQuery.
Enhanced fraud detection by replacing rule-engine program with semi-supervised learning approach, utilizing Relational Graph Convolutional Network (RGCN) for comprehensive evaluation of merchants' risk levels and improved accuracy.
Worked with business tribe users to analyze and mitigate potential fraud risks in campaign programs, providing insights and comprehensive mitigation plans.
Utilized PostgreSQL in DBeaver for data exploration, developed interactive Tableau dashboards, employed ARIMA time-series regression in Python to estimate daily product quantities for inventory management, and applied K-Means Clustering to optimize marketing strategies and providing personalized promotions based on customer segments.
Retrieved univariate monsoonal index and geospatial data through API request, conducted in-depth time-series analysis and wavelet analysis using Python in Google Colab, implemented SciKit Learn and Keras libraries to construct hybrid ARIMA-ANN models with varying configurations to optimize forecasting.
Analyzed monsoonal index data across three monsoons, conducted ARIMA modeling utilizing R and Minitab, performed comprehensive time series analysis, employed data preprocessing techniques, and created data visualizations using Python's Seaborn library to inform strategic agriculture initiative.
Technical Skills | Soft Skills |
---|---|
Programming (Python, R) | Communication |
SQL (BQ, PostgreSQL) | Adaptability |
BI tools (Looker Studio, Tableau, Power BI) | Collaboration |
Cloud Automation (GCF, VM) | Social awareness |
GIS (ArcGIS, GEE, QGIS) | Creative problem solving |
Water Management System | Ability to work under pressure |
NWP model (WRF) | Self-motivation |
LCA software (SimaPro) | English, German |