曹勝彥 Allen Tsao

嗨,我叫曹勝彥,來自台灣南投,大學就讀國立中山大學物理學系,畢業後因對數據科學/分析充滿熱情,參加了:「勞動部勞動力發展署-物聯網應用設計班」、和「財團法人資訊工業策進會-AI / Big Data 資料分析師養成班」,讓我有一定能力跨入數據科學/分析的領域。 

  1. 結訓後在「欣興電子」擔任智能大數據整合工程師,負責集團大數據技術路線規劃、推展、與建置,協助公司轉型邁入【工業4.0/智慧製造】; 
  2. 接著到「日月光半導體」擔任數據分析師,利用統計分析、機器學習等方法,協助製程單位分析數據、建立模型、數據化解讀製程參數分析結果,並協同轉化成可執行方案、追蹤方案成效;協助工廠掌握生產/設備狀況,以提高競爭力; 
  3. 目前在「同欣電子工業」擔任數據分析師,藉由工作深化技能與能力,並持續累積半導體封裝與測試產業(領域)知識。 
 工作的意義不在盡可能賺更多錢,而是在去做你喜歡的、能讓你每天早上開心起床的事,如有更好的工作/合作機會,歡迎通知我。
Hello, I'm Allen Tsao from Nantou, Taiwan. I'm currently a student majoring in Physics at National Sun Yat-sen University. After graduation, I developed a strong passion for data science and analysis. I pursued additional training to gain the skills needed to enter the field. I completed the "IoT Application Design Program" offered by the Ministry of Labor's Workforce Development Agency and the "AI/Big Data Data Analyst Training Program" by the Institute for Information Industry.  
  1. After completing my training, I worked as an Intelligent Big Data Integration Engineer at 「Unimicron」, where I was responsible for planning, promoting, and establishing the group's big data technical roadmap. I also assisted the company in transitioning into Industry 4.0 and smart manufacturing. 
  2. Subsequently, I joined 「ASE Group」 as a Data Analyst. In this role, I used statistical analysis and machine learning techniques to help process units analyze data, build models, and interpret process parameter analysis results. I collaborated on turning these results into executable plans and tracking their effectiveness. My work also involved helping the factory gain insights into production and equipment conditions to enhance competitiveness. 
  3. I am currently working as a Data Analyst at 「Tong Hsing Electronic」, where I continue to deepen my skills and knowledge in the semiconductor packaging and testing industry.
To me, the significance of work is not solely about earning more money but doing what you love and waking up happy every morning. If there are better job or collaboration opportunities, please feel free to reach out to me.

數據分析師 / Data Analyst 
新竹縣竹北市, 台灣 / Hsinchu County, Zhubei City, TW
  • Cell Phone: 0928-743748


工作經歷

 -「所有的經驗,都是人生的養分,會在意想不到之處,發揮作用。 」

同欣電子工業股份有限公司, 數據分析師(Data Analyst), 

Oct 2023 ~ Now

  1. 進行資料收集、標籤定義與數據清理
  2. 依據分析需求,進行資料數據分析與邏輯運算作業
  3. 數據視覺化報表產出,製作相關簡報
  4. 有效利用分析結果,提供單位擬定決策
  5. 跨部門溝通與需求訪談
  1. Collecting data, defining labels, and cleaning data.
  2. Performing data analysis and logical operations based on analytical needs.
  3. Generating data visualization reports and creating related presentations.
  4. Effectively utilizing the analysis results to assist the department in making decisions.
  5. Communicating across departments and conducting requirement interviews.

日月光半導體製造股份有限公司, 數據分析師(Data Analyst), 

May 2021 ~ Oct 2023

  1. 使用統計、機器學習等方法和分析工具協助工廠掌握生產/設備狀況,以提高競爭力
  2. 協助製程單位分析數據、建立模型、數據化解讀製程參數分析結果,並協同轉化成可執行方案,並追蹤方案成效
  3. 利用數據分析與儀表板設計軟體將分析的結果進行資料視覺化
  4. 專案執行與各單位溝通協調
  1. Assist the factory in understanding production/equipment conditions and enhancing competitiveness using statistical, machine learning, and other analytical tools.
  2. Aid process units in data analysis, model building, and data-driven interpretation of process parameter analysis results. Collaboratively transform these into executable plans and track their effectiveness.
  3. Utilize data analysis and dashboard design software for data visualization of analysis results.
  4. Execute projects and communicate and coordinate with various units.

欣興電子股份有限公司 , 【工業4.0/智慧製造】智能大數據整合工程師, 

Apr 2020 ~ May 2021

  1. 負責集團大數據技術路線規劃、推展、與建置
  2. 負責集團大數據資料探勘、挖掘、整併、預處理、運算、管理、與分析
  3. 各項製程良率報表開發,以及圖表繪製分析
  4. 分析集團機台生產數據,運用統計分析方法及機器學習提出適合演算模型,並建置分析模型,提供工廠營運改善建議
  1. Responsible for planning, promoting, and establishing the group's big data technical roadmap.
  2. Responsible for data exploration, mining, integration, preprocessing, computation, management, and analysis of the group's big data.
  3. Developing yield reports for various processes and creating charts for analysis.
  4. Analyzing production data from group machines, using statistical analysis methods and machine learning to propose suitable algorithm models, and establishing analytical models to provide operational improvement recommendations to the factory.

資策會_財團法人資訊工業策進會, AI / Big Data 資料分析師養成班 - 學員, 

Jun 2019 ~ Nov 2019

共計670小時:
  1. 關聯式資料庫,30小時
  2. 資料倉儲與商業智慧,24小時
  3. 巨量資料分析技術與工具應用,84小時
  4. 巨量資料儲存與處理,30小時
  5. 資料採礦分析,30小時
  6. NoSQL,24小時
  7. 物件導向系統分析與設計(UML),36小時
  8. 網路爬蟲,60小時
  9. Python程式設計與資料分析實作,66小時
  10. R軟體與資料探勘,24小時
  11. 專題實作,180小時
  12. Python程式設計及AI人工智慧導論,82小時
結訓專題:利用機器學習、深度學習在影像上的處理和辨識、配合消費者對商品的評分、點擊行為建立推薦系統,幫助使用者快速找到與自己喜好類似的鞋款外觀樣式。

In total, 670 hours of training were completed, including:
  1. Relational Databases: 30 hours
  2. Data Warehousing and Business Intelligence: 24 hours
  3. Big Data Analysis Techniques and Tools: 84 hours
  4. Big Data Storage and Processing: 30 hours
  5. Data Mining Analysis: 30 hours
  6. NoSQL: 24 hours
  7. Object-Oriented System Analysis and Design (UML): 36 hours
  8. Web Scraping: 60 hours
  9. Python Programming and Data Analysis Implementation: 66 hours
  10. R Software and Data Mining: 24 hours
  11. Project Implementation: 180 hours
  12. Python Programming and Introduction to AI (Artificial Intelligence): 82 hours
The training project involved the use of machine learning and deep learning for image processing and recognition. It incorporated consumer ratings and click behavior to create a recommendation system, helping users quickly find shoe styles that match their preferences.

私人工作室, 金融研究員, 

Apr 2018 ~ Jun 2019

  1. 經濟數據蒐集及資料庫建立,Python相關應用程式開發與維護
  2. 數量統計分析、策略研發
  3. 全球總體經濟、股市商品及產業研究,並撰寫分析專題報告
  4. 其他金融新創開發暨研究相關事務
  5. 主管交辧事項
利用網路爬蟲爬取網站股價資訊+作圖分析,找尋符合設想條件之挑資標的,架設網站並定期更新,從事資料蒐集、分析、撰寫研究報告之工作,同時須隨時注意大環境中可能影響獲利的各項經濟因素,以提供進行投資決策時的參考。
  1. Collecting economic data and establishing databases, developing and maintaining Python-related applications.
  2. Conducting quantitative statistical analysis and strategy development.
  3. Researching global macroeconomics, stock and commodity markets, and industries, and writing analytical research reports.
  4. Engaging in other financial innovation and research-related tasks.
  5. Supervising delegated tasks.
Using web scraping to gather stock price information from websites and conducting chart analysis to identify potential investment targets that meet specified criteria. Building and regularly updating a website for data collection, analysis, and research report writing. Concurrently, staying vigilant about various economic factors in the larger environment that could affect profitability, providing reference for investment decision-making.

勞動部勞動力發展署北基宜花金馬分署, 物聯網應用設計班 - 學員, 

Apr 2018 ~ Sep 2018

共計920小時:
  1. Linux作業系統,40小時
  2. 微控制器原理與應用,40小時
  3. 電子電路學,32小時
  4. C語言實習,61小時
  5. 網頁設計實習,40小時
  6. 感測器應用實習,60小時
  7. 專題實作,24小時
  8. 硬體操作實務與維護,69小時
  9. 電腦繪圖(電路圖),80小時
  10. 積體電路設計與應用,40小時
  11. 網頁圖文編輯設計實作,24小時
  12. APP程式設計與應用,40小時
  13. VHDL程式設計,32小時
  14. 數位乙級術科實作,56小時
  15. 網路與網站架設實習,44小時
  16. 電子工作法(儀表操作),20小時
  17. 數位邏輯電路實習,36小時
  18. 儀表操作,28小時
  19. 檢定實作練習,103小時
In total, 920 hours of training were completed, including:
  1. Linux Operating System: 40 hours
  2. Microcontroller Principles and Applications: 40 hours
  3. Electronics Circuitry: 32 hours
  4. C Language Practice: 61 hours
  5. Web Design Practice: 40 hours
  6. Sensor Applications Practice: 60 hours
  7. Project Implementation: 24 hours
  8. Hardware Operations and Maintenance: 69 hours
  9. Computer Graphics (Circuit Diagrams): 80 hours
  10. Integrated Circuit Design and Applications: 40 hours
  11. Web Graphics and Layout Design Practice: 24 hours
  12. App Programming and Applications: 40 hours
  13. VHDL Programming: 32 hours
  14. Digital Electronics Practical Skills: 56 hours
  15. Network and Website Construction Internship: 44 hours
  16. Electronic Work Practices (Instrument Operation): 20 hours
  17. Digital Logic Circuit Practical Skills: 36 hours
  18. Instrument Operation: 28 hours
  19. Certification Exam Practical Training: 103 hours


學歷

國立中山大學, 學士學位, 物理學系

2012.09 ~ 2016.06

【活動和社團】

  • 物理系桌-隊長
  • 桌球校隊-隊員

Pjnzjkm3ytqp9faqkjqh

專長


資料工程(Data Engineering) 

  • 資料預處理與分析(Data ETL/Analysis) 
    1. Python:Pandas、Dask、Numpy、Scipy、Pandas Profiling... 
    2. SAS:JMP 
  • 資料探勘(Data Mining) 
    1.  關聯/非關聯資料庫(SQL/NoSQL):Pyodbc、Cx_Oracle、Pymongo 
    2. 網頁自動化測試與爬蟲(Web Automation/Testing/Crawler):Selenium、Beautifulsoup4、Scrapy


資料視覺化(Data Visualization)

  • 圖表(Chart):Matplotlib、Seaborn、Bokeh、Plotly、Pydot 
  • 網頁應用框架(Web Application Framework):Django、Flask、Fastapi、Plotly Dash、Streamlit 
  • 網頁設計(Web Design):HTML、CSS、JavaScript、Bootstrap 
  • 投影片簡報(PowerPoint):Python-pptx 
  • 試算表(Excel):Xlsxwriter


人工智慧(Artificial Intelligence, AI)

  • 機器學習(Machine Learning, ML) / 深度學習(Deep Learning, DL):Scikit-Learn、TensorFlow、Keras、PyTorch、XGBoost 
  • AutoML:SAS Viya、AutoKeras、H2O


資料庫/大數據框架(Database/BigData Framework) 

  • 資料庫(Database) 
    1. 關聯式資料庫(SQL):MS SQL、Oracle 
    2. 非關聯式資料庫(NoSQL):MongoDB、Redis 
  •  大數據框架(BigData Framework) 
    1. Hadoop:建立多台電腦組成的叢集,以更快地平行分析大型資料集(Building a cluster of multiple computers to parallelly analyze large datasets for faster processing) 
    2. Spark:採用記憶體內快取並將查詢執行最佳化,以快速分析查詢任何規模的資料(Employing in-memory caching and query optimization for quick analysis of data of any scale) 
    3. Kafka:處理即時資料提供一個統一、高吞吐、低延遲的平台(Handling real-time data to provide a unified, high-throughput, low-latency platform)


系統開發建置(System Development)

  • 產品面向(Product Level) 
    1. 統計製程管理(Statistical Process Control, SPC) 
    2. 配方管理系統(Recipe Management System, RMS) 
    3. 良率管理系統(Yield Management System, YMS) 
  • 過程面向(Shop Floor Level) 
    1. 失效偵測與分類(Fault Defection and Classification, FDC) 
    2. 設備預防保養管理系統(Preventative Maintenance System, PMS) 
    3. 工程/探索式資料分析(Engineer/Exploratory Data Analysis, EDA) 
    4. 即時監控機制及設備健康預診斷與管理(Prognostic and Health Management, PHM) 
  • 未來持續發展方向(To Be Continued) 
    1. 全自動虛擬量測(Automatic Virtual Metrology, AVM) 
    2. 智慧型預測保養機制(Predictive Maintenance, PdM) 
    3. 先進製程控制(Advance Process Control, APC)


其他(Others)

  • 作業系統(OS):Linux 
  • 容器化(Container):Docker 
  • 版本控制(Version Control):Git、Github、GitLab