【PyConTW 是誰先開始的？創辦人 YYC 現身說法啦～】 PyConTW 11 週年了啦，想知道當年是誰先開始的嗎？ 我們沒有忘記?，也沒有害怕想起來?， 我們讓始作俑者，啊不是，創辦人 YYC 說說， 如何從開心小聚會，成為現在 600 人的大型研討會， 十一年來持續著前人挖坑後人填坑的優良傳統～ （小編聽說他們以前也很爆肝，心裡平衡多了） 重點是～還活得這麼久！！ 快拉板凳? 點擊影片?聽他娓娓道來?‍? 線上觀望不如購票參與： ? 個人票 ： https://pse.is/PyConTW2023individual ? 團體票 (三人成行，即享優惠)： https://forms.gle/13aneQgTF55srNnY6 ? 企業團體票 (五人成行，報帳方便)： https://forms.gle/mNAMXFkjaXsEsKUM7 #pycontw2023 #pycontw #Founder #Insight #python #十週年歷屆主席紀念企劃 Follow “PyCon Taiwan” ⭐️ Official Website: https://tw.pycon.org ⭐️ Facebook: https://www.facebook.com/pycontw ⭐️ Instagram: https://www.instagram.com/pycontw ⭐️ Twitter: https://twitter.com/PyConTW ⭐️ LinkedIn: https://www.linkedin.com/company/pycontw ⭐️ Blog: https://conf.python.tw/ ... https://www.youtube.com/watch?v=0iR-Vk6XgbU

Day 2, 11:30-12:00 Abstract Imagine you are a data engineer and in charge of a data pipeline. What do you think is the most important thing for the data pipeline? I think it is definitely the quality of data! However, the more complex your data pipeline becomes, the harder it is to maintain the data quality. For example, what if the format of some source data is changed without being noticed? What if some program update includes a bug? Such things cause data issues. It can take a long time to find the issues. Or even worse, your stakeholders may find the issues before you do! Great Expectations helps you solve such problems. It is a Python-based open-source library for validating, documenting, and profiling your data. It allows you to define the shape of data, test data, and document the results. In this talk, I will introduce you Great Expectations and share my experience with it. Let's make your data pipeline robust with Great Expectations! Description Great Expectations A Python-based open-source library for validating, documenting, and profiling your data. Slides: https://docs.google.com/presentation/d/1ttwYJdFYLT9x87fusVAPBgcxZpEAE3g7AZ5eic34I7s/edit#slide=id.p HackMD: https://hackmd.io/@pycontw/2021/%2F%40pycontw%2FHJRVK7qMt Speaker: Keisuke Nishitani A data engineer and python programmer in Osaka, Japan. Working on a data pipeline built on Amazon Redshift, Amazon S3 and AWS Lambda. Interested in data workflow frameworks, data analysis and data visualization. ... https://www.youtube.com/watch?v=e8XhXG-NiWk

PyCon Taiwan 2023｜Talk 演講｜Day 1, R1 16:00–16:45 ? 說明 Description ? A layered data design pattern is a modern data architecture for building ETL/ELT data pipelines comprised of multiple stages so that each stage processes the data and improves the quality of the data progressively. Compared to the imperative way how data engineers build ETL/ELT data pipelines in the last decade, layered data architecture could be of great help in improving data quality steadily and progressively, and reducing data silos while project-specific teams are autonomously producing various data products. We will introduce, in this share, a technical solution based on layered data architecture. The solution is implemented by means of Dagster, a cloud-native data orchestrator with integrated lineage, observability, and a declarative programming model. A simple example will be presented in this talk to demonstrate concepts, principles, and data stack of the solution. In the end, the benefits we have gained from the implementation experience will be conveyed as well. ? 投影片 Slides：https://1drv.ms/p/s!AtNklwocKzYg8AEp6C6A-0jq8XN6?e=3ZFwU6 ? 講者介紹 About Speaker - George T. C., Lai ? A data practitioner with data analysis background who has been developing career mainly in Big Data and DevOps based on cloud-native ecosystem for 12 years. In the recent 7 years, I have been focusing on Data Architect, team management, and DevOps. As to technical experience, I got 6 years on Hadoop ecosystem, especially on Hortonworks HDP, 7 years on Kubernetes and 4 years on AWS/GCP. My personal vision is to make each data practitioner have a better life. I am approaching the vision by exploring new tools, discovering best practices, and delivering well-designed data architectures and technical solutions for data practitioners to relief their pain points and frustrations when coping with data. Follow “PyCon Taiwan” ⭐️ Official Website: https://tw.pycon.org ⭐️ Facebook: https://www.facebook.com/pycontw ⭐️ Instagram: https://www.instagram.com/pycontw ⭐️ Twitter: https://twitter.com/PyConTW ⭐️ LinkedIn: https://www.linkedin.com/company/pycontw ⭐️ Blogger: https://conf.python.tw/ ... https://www.youtube.com/watch?v=wyO8VSkH81o

PyCon Taiwan 2016｜一般演講 Talks ? 摘要 Abstract ? Time series prediction has become one of the most popular field for applying in the real world. Because there are various models to forecasting the future data, how to choose a suitable model has become a significant issue for every companies who want to join the data driven trend. In this talk, we are going to share our experience and result of the implementation of time series forecasting models. The topic will include the following points: 1. How to choose a suitable model for variety datasets, 2. Why did we choose the current models (ARIMA+SVR, SdA), 3. How to implement the models on python, 4. What problems did we face when we are implementing the model. ? 關於講者 About Speaker - 古宣佑 Hsuanyo ? 目前於 Soocii 任職後端工程師，對機器學習演算法有興趣，工作之餘，會拿工作上面臨的問題，當作練習的題目。之前曾經接觸過時間序列預測的模型開發，在學時期則是專注於自然語言處理 (Sentiment Analysis) 的相關研究。 ? 關於講者 About Speaker - Trudie ? 目前就讀於台灣大學資訊管理碩士，研究推薦系統與社群網絡，於物聯網分析公司擔任資料分析師，興趣在於開發資料分析應用。 #python #pycontw #pycontw2016 Follow “PyCon Taiwan” ⭐️ Official Website: https://tw.pycon.org ⭐️ Facebook: https://www.facebook.com/pycontw ⭐️ Instagram: https://www.instagram.com/pycontw ⭐️ Twitter: https://twitter.com/PyConTW ⭐️ LinkedIn: https://www.linkedin.com/company/pycontw ⭐️ Blogger: https://pycontw.blogspot.com ... https://www.youtube.com/watch?v=ivkrU4n_ZRg