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© Thu Ya Kyaw & Koo Ping Shung
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Interested in Data Science, Analytics and Artificial Intelligence?
This podcast, Symbolic Connection will help you to understand all aspects of Data Science and Artificial Intelligence.
Run by practitioners with a combined experience of more than 10 years+, they share what they have learned.
The topics will vary from data, algorithms, implementation, business applications, and more. All from an applied perspective.
Find out what’s developing in the field. Give it a listen 👇
Feedback: https://forms.gle/fnnJ6QGrjj4Yv74z5
Contact: symbolic.connection@gmail.com
This podcast, Symbolic Connection will help you to understand all aspects of Data Science and Artificial Intelligence.
Run by practitioners with a combined experience of more than 10 years+, they share what they have learned.
The topics will vary from data, algorithms, implementation, business applications, and more. All from an applied perspective.
Find out what’s developing in the field. Give it a listen 👇
Feedback: https://forms.gle/fnnJ6QGrjj4Yv74z5
Contact: symbolic.connection@gmail.com
39 Episodes
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We are back! For this episode, we discussed topics about generative AI. We actually used Google's Bard to generate the outline for this episode. It covers topics: What is generative AI? How does it work? What are some of the benefits of generative AI? Challenges of generative AI, the future of generative AI, and many more! Overall, this episode provides a comprehensive overview of this rapidly developing field. It could also explore the ethical implications of generative AI, and its potential impacts on society. Let us know what you think. You can reach us directly from our LinkedIn page.
Koo Ping Shung: https://www.linkedin.com/in/koopingshung/
Thu Ya Kyaw: https://www.linkedin.com/in/thuyakyaw/
For this episode, we invited Low Yi Xiang, a Data Scientist at Traveloka again to have a chat about Data Product Management. Yi Xiang covered what is data product management in a nutshell, how it differs from the other product management practices, what are the stages of data product management lifecycle, and many more interesting topics. We hope you enjoy this episode as much as we had fun recording and producing it.
Yi Xiang is an experienced data scientist who likes to work on various sorts of data problems. He also possesses a strong track record of being a strong individual contributor and/or taking on lead positions delivering huge impact. Although his title is data scientist, he works on a wide range of scopes, whether it is analytics, data engineering, building models and moving models to production and post monitoring. If you want to catch up with Yi Xiang, he can be reached via his LinkedIn: https://www.linkedin.com/in/yi-xiang-low-b349137b/
In this episode, we invited one of our popular guests, Charin Polpanumas, back! We got him to share a project that he is passionate about, PyThaiNLP. In this episode, we discuss the challenges of Natural Language Processing and also creating and working on an open-source project. This episode is definitely for anyone who is interested in Natural Language Processing as we discuss many aspects of NLP, building corpus, challenges in translation, and challenges on the limited training datasets! Do check it out if you are someone passionate about NLP!
Reference Source: https://github.com/PyThaiNLP/pythainlp
It has been a while since we released an episode on this channel. Apologies, we were both busy with work and couldn't find a common dedicated time to record an episode. We also changed the intro and outro music. Let us know what you think?
In this episode, Koo Ping Shung and I discussed the nitty gritty things about AI ethics. We also voiced our opinions on the different aspects of AI ethics and whether having a governing body to control the ethics aspect of AI is a good thing or not. Have a listen!
If you have any feedback, you can send them our way from here: https://forms.gle/cdgUUtsmdnsrNUPMA
Want to appear in our podcast episode? Let us know from here: https://forms.gle/g9xoC12eEUSA6vhdA
This week we invited Ivan who is a Lead Data Scientist at Tech in Asia. Ivan has a unique set of technical competencies, project management, interpersonal skills and problem solving abilities. He is also experienced in deploying scalable machine learning systems, data engineering pipelines, dashboards and delivering actionable insights through the use of statistics and data visualization. In this episode, Ivan shared his career journey from being an undergraduate to leading a data science team. He also shared whether doing an internship is useful and many more interesting tips and tricks to land a job in the data industry. Check this out!
Data collection is a crucial step for any data related projects. So much so that you might have encountered something along the lines of the “GIGO” (garbage in, garbage out) concept. Some might even say having the right data is more important than having tons of data that can’t be used.
As web scraping being one of the ways to collect data, for this episode, we invited Cliff, a data consultant, back to discuss his personal experience with web scraping. He shared topics such as the basics of web scraping, web scraping tools, the challenges that he faced while trying to scrape web contents, ethics of web scraping, learning materials, and more!
Resources:
Cliff's medium post 1: https://medium.com/codex/scraping-singapore-libraries-f74c541f1f94
Cliff's medium post 2: https://cliffy-gardens.medium.com/iterations-for-my-nlb-scraper-github-code-provided-b4e1f1bd422e
Selenium: https://www.selenium.dev/
BeautifulSoup: https://www.crummy.com/software/BeautifulSoup/bs4/doc/
TagUI: https://github.com/kelaberetiv/TagUI
Web Scraping with Python: https://www.oreilly.com/library/view/web-scraping-with/9781491985564/
Happy New Year everyone! We are back with an episode that may help planning your Data Science & Artificial Intelligence career! In this episode, you will find career tips to build a solid foundation in your Data Science career. Thu Ya & Koo discuss taking up an internship, contract, and full-time job and their possible impact on your career path, They also discuss the possible Data Science experience gained working in a Start-Up, Small Medium Enterprises, and MNCs. And should you join a consulting firm or work in a specific industry. How about Certifications and their impact on your career?
Psst...Koo also shared a new community initiative! Listen to the end to find out more! :)
For this episode, instead of having a guest over, Koo interviewed his co-host, Thu Ya Kyaw, Machine Learning Engineer @NE Digital to share his programming journey including the motivations, the opportunities, and the struggles. Oh, did you know about 'tab vs space' war? You should definitely check out this episode.
This week we invited Leo Tay who is also an AIAP graduate like Thu Ya and currently working as a Data Science Engineer at Allianz. He shared his interesting journey of becoming a Data Science Engineer without having a comp science degree, alongside with learning materials and useful tips and tricks to get into the fields. He also mentioned his struggles and how he overcame them in a short period of time. Lastly, we end the session by talking about a non-data related but useful topic for our listeners as always.
Materials:
Humble Bundle (https://www.humblebundle.com/)
AWS Sagemaker (https://aws.amazon.com/sagemaker/)
TheRealPython (https://realpython.com/)
Udemy (https://www.udemy.com/)
Terraform (https://www.terraform.io/)
Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9
We have a special guest for this episode. He is Thia Kai Xin, co-founder of DataScience SG and currently a Senior Data Scientist in Refinitiv Labs working on Natural Language Processing project. He shares his career journey, what are the skills and knowledge that got him his jobs and provided some advice on how to get into Data Science. Travel with him through his career journey and see how you too can become a data scientist. :)
LinkedIn Profile: https://www.linkedin.com/in/thiakx/
Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9
In this episode, we discussed how someone, regardless of their background, can get into data science. The topics include the necessary skills and knowledge he/she need to be equipped with as well as recommendations on whether to choose boot camp, official degree program or self-learning to get started. We also briefly touched on what you need to do for your project portfolio, to improve your chances during job interviews.
Resources
Learn Mathematics (Khan Academy, MIT - Linear Algebra, MIT-Calculus)
Open Datasets (UCI Irvine Machine Learning Repository, Kaggle, SF City Open Data Sets, Singapore Open Data)
Starting the Artificial Intelligence Learning Journey (Koo's Blog Post)
How to Prepare Your Data Science Resume and Portfolio (Koo's Blog Post)
Selecting Data Science Boot Camp/Training (Koo's Blog Post)
Starting Your Data Science Project (Koo's Blog Post)
What is shared is to the best of our knowledge at the time of recording. We strongly encourage our listeners to continue seeking more knowledge from other resources. Have fun in your learning journey and thanks for choosing us as learning companions. :)
In this episode, we explore what is Data Science, Machine Learning, and Artificial Intelligence. We also discussed the relationship and differences between them. How did Data Science come about, what are the common branches of machine learning and what do they do, are some of the questions we answered in the episode. We also covered briefly the difference between Data Scientist and Software Engineers.
References:
What are all these terms? (Koo's Blog Post)
Difference between Data Analyst & Data Scientist. (Koo's Blog Post)
Supervised Learning (Wikipedia)
Unsupervised Learning (Wikipedia)
Reinforcement Learning (Wikipedia)
Outline of Machine Learning (Wikipedia)
Her (Film) (Wikipedia)
Difference between Software Engineer & Data Scientist (CareerKarma's Post)
What is shared is to the best of our knowledge at the time of recording. We strongly encourage our listeners to continue seeking more knowledge from other resources. Have fun in your learning journey and thanks for choosing us as learning companions. :)
In this episode, we did a brief introduction to who we are. We discussed briefly what is Artificial Intelligence and the history of it, namely Symbolic AI and Connectionist AI. The difference between them, and how did we move from Symbolic AI to Connectionist AI was discussed as well. Take your first step together with us in our learning journey of Data Science and Artificial Intelligence. Below are a few resources you can refer to after the podcast. Will be happy to discuss the topic with our audiences. :)
Resources:
Symbolic AI (Wikipedia)
Connectionist AI (Wikipedia)
History of AI (Wikipedia)
John McCarthy (Wikipedia)
Marvin Minsky (Wikipedia)
Geoffrey Hinton (Wikipedia)
The story on identifying camouflaged tanks [Host Notes: turns out to be an urban myth much like diapers and beers]
Identifying Wolfs & Dogs (YouTube)
What is shared is to the best of our knowledge at the time of recording. We strongly encourage our listeners to continue seeking more knowledge from other resources. Have fun in your learning journey and thanks for choosing us as learning companions.
This week, we invited Kelvin Tham, an MLOps Data Program Manager at ViSenze - AI for Visual Commerce. Kelvin has a wide range experience across ML Ops, data analytics, and business process improvement. He is currently working on design, development and shipping of ML Ops model management.
In this episode, he shared about how is it like to be working at an AI startup company and his war stories of wearing multiple hats at one go. He also talked about the differences between being a program manager and a developer, pros and cons of each role, and shed a light on what to look out for when you are exploring your future career options. Have a listen.
You can connect with Kelvin here: https://www.linkedin.com/in/kelvinthamkh/
This week, we invited Teck Liang Tan (PhD), a Senior Data Scientist @ NTUC Enterprise to have chat with us. He walked us through his unconventional career move from Physics to Data Science. Also, his reason of getting into the industry instead of continuing in academia.
By the way, do you know what is complexity science? If you are curious, you should definitely give this episode a listen! He also shared learning resources for getting into the field as well as keeping the skills sharp.
We also talked about #MajulaGCP season 5 which is happening right now and many more! Teck Liang's profile: https://www.linkedin.com/in/teck-liang-tan-47a47327
So what is MLOps? This is a topic we covered in this episode. We discuss the different aspects of MLOps, for instance, data, business requirements, and also measuring the performance metrics. We discuss also data quality and feature engineering and its impact on the ML pipelines as well. We also do a short introduction on the different tools used in MLOps, such as Containers, Kubernetes, and Airflow. And let us throw in one more technical term...data versioning. Give us a listen to understand what that is!
Learning Resources:
1. What is MLOps (https://whatis.techtarget.com/definition/machine-learning-operations-MLOps)
2. Getting started with MLOps (https://ml-ops.org/)
3. MLOps Fundamentals with GCP (https://www.coursera.org/learn/mlops-fundamentals)
4. Difference between Data Scientist and MLOps Engineer (https://towardsdatascience.com/data-scientist-vs-machine-learning-ops-engineer-heres-the-difference-ad976936e651)
5. Learn Docker (https://www.youtube.com/watch?v=fqMOX6JJhGo)
6. Learn Kubernetes (https://kubernetes.io/docs/tutorials/kubernetes-basics/)
8. https://www.deeplearning.ai/program/machine-learning-engineering-for-production-mlops/
In this episode, we have another guest - Amelia, a chemical engineer turned data scientist! Listen to the episode to understand more about her successful transition, what are the skills that she finds valuable as a data scientist, and how did she cope with studying for Master's and working at the same time. We had a great discussion on the topic of coping with work, studies, and everything else! If you want some tips and tricks, tune in to this episode to find out more!
Amelia also shared about her Global Health Fellowship experience with an NGO in Seattle, and how she used her data science skills for a better world! It was an eye-opening experience that taught her about change management and how to ensure the data science momentum persists in an organization.
Amelia's LinkedIn Profile: https://www.linkedin.com/in/pehyingqi/
Symbolic Connection takes a break from interviewing guests and has two non-experts, the co-hosts Thu Ya and Koo to share what they understand about a privacy-preserving model training technique called Federated Learning. We have a discussion on Federated Learning, its relationship with Edge Computing, how the industry solved the challenges associated with implementing Federated Learning, what is centralized and non-centralized FL. Curious and/or preparing for an interview? Hit that "Play" button! :)
Resources on Federated Learning
https://federated.withgoogle.com/
https://docs.google.com/presentation/d/1uXX_nbgzWC95phW_7P5JBR-bDBqLpuNKckf2e8Fw5SA/edit?usp=sharing (Thu Ya's presentation slides on FL)
https://github.com/IBM/federated-learning-lib
https://github.com/tensorflow/federated
In this episode, we interviewed another lady in tech, Poh Wan Ting. She shared her career journey, how she started from computational biology to now leading a data science and engineering team in a well-known Financial Institution. She also shared how she manages her data team and retains them. We also discussed what shall one do when an opportunity comes, to take or leave it, and what are considerations one should take. And of course, being a hiring manager, we asked her how she selects her teammates, what questions does she ask during interviews. We also have a quick discussion about talent development here in Singapore as well and last but not least, how can we reduce the gender gap in the tech industry! Want to know the answers, hit that "Play" button!! :)
Wan Ting's LinkedIn: https://www.linkedin.com/in/pohwanting/
Books that Wan Ting recommends:
Midnight Library by Matt Haig
https://www.goodreads.com/book/show/52578297
Deep Work by Cal Newport
https://www.goodreads.com/book/show/25744928
Learning Resources that Wan Ting recommends:
https://www.morningbrew.com/daily
https://www.morningbrew.com/emerging-tech
https://www.thedailyupside.com/
https://www.nytimes.com/section/business/dealbook
We have a guest from the banking industry for this episode, Jeanne, from the Bank of Singapore. She shared her journey, how she moved from studying animals to being an AI lead in the banking industry. We discussed how to encourage more females to join the tech industry, how does conducting training help one's career. Jeanne also shared how it is like working on tech in the banking industry and the weirdest interview questions she encountered. As a hiring manager, what is Jeanne looking for in a candidate? Want to know the answer? And one more thing, how can talents working in AI improve the industry? Check out this episode! :)
Jeanne's LinkedIn: https://www.linkedin.com/in/jeanne-choo-8149711a3/
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Accurate data collection is vital—quality over quantity matters. GIGO reminds us that poor input leads to flawed output, making reliable, usable data essential for meaningful results in any data project. I also read the post How to scrape google news https://groupbwt.com/blog/how-to-scrape-google-news/ to understand how you can use information to obtain data