WiDS Taipei
 
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WOMEN IN DATA SCIENCE
TAIPEI CONFERENCE 2019

 
 

The Global Women in Data Science (WiDS) Conference 在 2015 年 11 月於美國史丹佛大學 (Stanford University) 成立, 是一個國際知名的科技論壇, WiDS 創立有三個目標:

  • 不分性別並致力於教育發展, 期望提供所有參與者一個吸收最新研究, 技術發展以及產業趨勢的平台。

  • 期望能激發學生對於資料科學各項研究領域以及應用的學習熱忱, 同時使更多女性能成為學生未來的楷模, 也讓學生能探索及了解未來資料科學相關領域的發展機會及挑戰。

  • 支持及鼓勵女性參與資料科學相關領域, 不管是從事相關領域工作, 亦或是成為學術研究或教育者, 並且協助她們提昇曝光的機會, 增加與同業交流分享的機會以及協助她們的職涯發展。

WiDS 每年舉辦研討大會,邀請行業內頂尖的女性擔任演講嘉賓,並有超過150個活動由全球的WiDS大使於各地區舉辦。

WiDS Taipei Conference & Workshop

今年將是我們在臺灣舉辦的第二屆 WiDS Conference 全球區域活動,邀請了來自國內外產業界、學術界的女性研究員、工作者以及科學家來分享最新行業趨勢、職涯規劃,以及資料科學在各個領域所帶來的重大改變。每年,我們也期望與其他社群合作, 舉辦一系列資料科學相關工作坊作為論壇的暖身活動。我們希望這些活動能夠為資料科學專業人士提供一個互相交流以及分享的平台。今年我們與 Google Taiwan 合作規劃一系列免費的機器學習工作坊 (Machine Learning Crash Course Workshop),循序漸進帶領初學者學習重要且實務的資料分析觀念,提供有興趣者一個入門以及參與的管道。

日期
工作坊 1: 2019 年 3 月 17 日, 9 AM - 5 PM
工作坊 2: 2019 年 3 月 24 日, 9 AM - 5 PM
年度論壇: 2019 年 3 月 31 日, 9 AM - 6 PM

地點
工作坊: 國立臺北大學 民生校區

年度論壇: 國立臺灣大學 博雅教學館

 
 

活動議程與購票資訊

 

Machine learning Crash course
Concept

09:00-17:00
2019 年 3 月 17 日

Machine learning crash course
coding

09:00-17:00
2019 年 3 月24 日

Wids Taipei conference
 

09:00 - 18:00
2019 年 3 月31 日

 

* 2019 工作坊為免費活動,報名時會收取保證金,並於活動當日退還

 

早鳥票及學生票

不要錯過一年一度的 WiDS 臺北年度論壇, 註冊早鳥票還可享有早鳥優惠,早鳥票數量有限喔!

WiDS Taipei 同時提供學生優惠折購,學生票入場時需出示學生證件

 

講者介紹

 
 
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Sharon chai
翟翎琇

行銷數據分析經理
Athleta (Gap Inc.)

Ann Chen   Machine Learning Researche, KKBOX

Megan Sun
孫敏倫

數據分析專案經理
OLX Group

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Bastiane Huang

產品經理
Osaro

 
 
 
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Guiguan Lin
林冠樺

技術專案經理
GallopWave

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Mosky Liu
劉依語

Python 軟體工程師
Pinkoi

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Galit Shmueli

特聘教授
國立清華大學

 
 
 
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Tammy Yang
楊琬晴

聯合創辦人及資料科學家
DT42

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Irene Chen
陳維君

資深企業解決方案專案經理
iKala

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Mei-hau pan
潘玫樺

資料科學家
Pinkoi

 

 

2019 WiDS 臺北年會議程

 

 
 

09:30 - 09:40

Welcome Adress & Opening Remarks

 

 
 

09:40 - 10:40

(R 102)

Using Health Data to Drive Changes in Rural Health Care

Elvena Fong
Health Data Analytics Program Manager, The Center for Health Systems Innovation (CHSI) at Oklahoma State University

Electronic health record (EHR) data offers vast potential for changing the way that health care is delivered. However, because of the messy nature of the data, there are many data-cleaning issues that must be overcome prior to using this data. Come and learn about Oklahoma State University’s Center for Health Systems Innovation (CHSI), our vision to transform rural health care, how we’ve resolved some of the challenges with using EHR data, and some of our innovations based on health data.

 
 

 
 

10:50 - 11:50

(R 102)

Redefining Robots & Demystify Next Generation AI Enabled Robotics

Bastiane Huang
Product Manager, Osaro

Machine learning has made it possible to shift from manually programming robots to allowing machines to learn and adapt to changes in the environment. We will discuss how AI-enabled robots are used in warehouse automation and how we can use warehouse robotics as an example for other industries such as manufacturing and food assembly. We will describe recent progress in deep reinforcement learning and imitation learning, and discuss the requirements and challenges of various industrial problems, both pipelined and end-to-end systems. Our talk also covers the technology Osaro developed to address the challenges in industrial robotics.

 
 

 
 

10:50 - 11:50

(R 103)

Measurement & Omni-Channel Marketing to Grow Social Enterprise

Sharon Chai 翟翎琇
Analytics Lead, Marketing Effectiveness, Athleta (Gap Inc.)

Why is it important to measure marketing efforts? What are the best practices and processes in theory to ensure that marketing is truly effective? How do you handle the challenges of a real-world retail environment and what are practical steps to optimize marketing spend? Sharon will share her perspective on bringing data to omni-channel marketing at Athleta, a premium fitness and lifestyle brand that is committed to igniting a community of active, healthy, confident women and girls who empower each other to reach their limitless potential. Come listen to hear how her journey from literature to data science and a growth-oriented mindset help her combine the art and science of marketing.

 
 

Lunch Break


 
 

13:00 - 14:00

(R 102)

Tactics for Empowering A Product with Artificial Intelligence

Guiguan Lin 林冠樺
Technical Project Manager, GallopWave

With the advancement of technology, we are connected to artificial intelligence in our daily lives - and indeed it will grow at a quicker pace in the near future. There are some amazing cases AI are used behind the scenes to improve user experience. However, AI is not the panacea and cannot be easily adopted. In this talk, I will share the tactics for applying AI into a product in different phases: from ideation, development, launch, to enhancement.

 
 

 
 

13:00 - 14:00

(R 103)

My Data Science Journey, from Physics to Business World

Tammy Yang 楊琬晴
Founder, DT42

Introduce how high-energy physics is related to data science, how to recruit good data scientists from physics world, and how I use data science skills in business and management.

 
 

 
 

14:10 - 15:10

(R 102)

Value of Data in Business Experiments

Bianca Chen
Principal Consultant, MasterCard Applied Predictive Technology

In the current world, collecting and saving data is much easier than before. Therefore, how to rapidly analyze and utilize the data to generate value, prove hypothesis and facilitate daily business decisions become key. During this hour I will share how our company (APT) specializes in Test and Learn, a testing cycle and a platform that enables companies to maximize learning through business experiments.

 
 

 
 

14:10 - 15:10

(R 103)

Hypothesis Testing With Python: True Difference or Noise?

Mosky Liu 劉依語
Python Charmer, Pinkoi

In an experiment, the averages of the control group and the experimental group are 169.61 and 169.88. Is the experimental group better than the control group? Or is the difference just due to the noise? In this talk, I will introduce statistical hypothesis testing to rule out the noise. Moreover, Python-based visualization, calculation, and simulation of various topics will be demonstrated, including α, β, effect size, sample size, inverse α (false discovery rate), and inverse β (false omission rate).

 
 

Coffee Break


 

15:30 - 16:30

(R 102)

Behavioral Big Data & Healthcare Research

Galit Shmueli
Distinguished Professor, The Institute of Service Science, National Tsing Hua University

Behavioral big data (BBD) refers to very large and rich multidimensional data sets on human and social behaviors, actions, and interactions, which have become available to companies, governments, and researchers. A growing number of data science researchers and practitioners acquire and analyze BBD for the purpose of extracting knowledge and scientific discoveries related to healthcare. However, the relationships between the researcher, data, subjects, and research questions differ in the BBD context compared to traditional behavioral data or inanimate big data. Also, the landscape of risks and harm to human subjects has greatly changed, as evidenced by the new data protection regulations (such as GDPR and revised IRB) that came into effect in 2018. Data scientists using health BBD face not only methodological and technical challenges but also ethical and moral dilemmas. These issues also affect industry-academia collaborations. In this talk I will discuss several dilemmas, challenges, and trade-offs related to acquiring and analyzing BBD for healthcare research.

 
 

 
 

15:30 - 16:30

(R 103)

A Data Project Is Born - From Initiative, Resource to Impact

Megan Sun 孫敏倫
Data & Analytics Project Manager, OLX Group

A data project is an end-to-end journey from data engineering, analyst, BI, product to business. It is never only finished by data scientists. How to start a data project and make all data related people work for your project? How to get all the people resources ready? What kind of difficulties will you face while implementing? Let’s find the answer with 3 stories - within a team, cross-functional and even across countries.

 
 

16:40 - 17:20

Roundtable + Networking Session

Panelists: Irene Chen 陳維君, Mei-Hua Pan 潘玫樺, Guiguan Lin 林冠樺, Tammy Yang 楊琬晴, Bianca Chen, Mosky Liu 劉依語, Galit Shmueli, Megan Sun 孫敏倫

 
 

17:30 - 17:45

Closing Remarks

 
 

籌備團隊

 
 
 

贊助及合作夥伴

 
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