Symbolic Connection

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/ --- Send in a voice message: https://podcasters.spotify.com/pod/show/symbolic-connection/message

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 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

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/

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

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/

In this episode, we have another guest from AI Singapore. He is Ryzal Kamis, Senior Platform Engineer. In this episode, we discuss a great deal on MLOps, what it is, and for anyone who is interested in the MLOps area, what are the learning resource, etc. Another topic that we discuss is data versioning, what it is and why is it important. We also discuss more on programming, debugging, and how to get better at it. Ryzal also shared how he transitioned from a Banking and Finance degree to an AI Engineer. Ryzal's LinkedIn: https://www.linkedin.com/in/ryzalkamis/ Recommended Learning Resources: https://madewithml.com/, MLOps Reading Groups, YouTube

In this episode, we take a break and talk about documentation, something that is a "necessary evil" for any software engineer and data professional. Thu Ya and Koo, tackle the documentation topic by sharing their experience, their pains, and frustrations when documentation is not done well. They also shared what is documentation to be done for each stage of the project, the data preparation, the modeling process etc. Tackle documentation with less frustration and more effectiveness by listening to the episode. Trust us, it will help! :) Check it out and spread the word! :) References: Airflow:https://airflow.apache.org/docs/apache-airflow/stable/ Any feedback for us? Here is the form: https://forms.gle/fnnJ6QGrjj4Yv74z5

This week we invited Michael Ng from Agilent Technologies to share his background and career journey. :) What got him interested in the field? What are the key skills in dealing with business stakeholders? What are the questions he asked his interviewees? What makes a good analysis? These are the questions we tackled during the podcast. Michael also shared his interview experience after he has graduated from his Masters, for instance, the "interesting" questions he was asked. One 'hot' question we tackled is how a Masters degree can help your data career. We had a substantial discussion on taking up and having a Masters. Interested? Give us a listen! Michael's LinkedIn profile: https://www.linkedin.com/in/michael-ng-59814011/

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 episode's guest is Low Yi Xiang, a data scientist at Traveloka. He is also a member of the DataScience SG Working Committee. He showed his unique perspectives on his career and data science and how he got started. Yi Xiang shared his journey from a “dashboard data scientist” to putting models in production in a technology unicorn. He also shared his process on important lessons and take backs being a data scientist.  Last but not least, we also did a discussion on Python and R, how the industry is using it. Check out the full episode so as not to miss any "juicy" parts, especially if you are a current undergraduate looking to a career in Data Science. Low Yi Xiang's LinkedIn: https://www.linkedin.com/in/yi-xiang-low-b349137b/

In this episode, Chong Zi Liang continues to share what steps he took to land his current role after the boot camp. He also discussed his current job scope, the tools he is using and what is he learning from his job. Hear what interview tips Zi Liang has as well. Last but not least, we discussed the different roles of data professionals and how they relate to one another. Check out this episode if you are interested in being a data professional, especially if you’re not from a STEM background. LinkedIn Profile: https://www.linkedin.com/in/zi-liang-chong/ Chong Zi Liang's Newsletter: https://artsciencemillennial.substack.com/   Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

In this episode, we are very happy to invite Chong Zi Liang, a data analyst at 99.co, to share his career journey, particularly on his transition from a non-data, non-STEM background. There is a lot of content to share again so this podcast has a few parts! In this part, we discussed why Zi Liang chose the Data Analytics field and how he started his career change, including how he managed his self-learning. Listen to his bootcamp experience and how he went about hunting for an analytics job. Have a listen to get some tips, especially if you are a mid-career changer! LinkedIn Profile: https://www.linkedin.com/in/zi-liang-chong/ Chong Zi Liang's Newsletter: https://artsciencemillennial.substack.com/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

We continue our Data Myths busting in this second episode given the raving reviews and feedback from the audience. Yes, we are back with myth-busting! Here are the myths we discussed. 1 - "If the mathematics is not complicated/complex, it is not Data Science." 2 - "R vs Python...any winner?" 3 - "Data Scientist is the top job! Data Analyst & Data Engineer should aim to be a Data Scientist." Like to know more why they are myths and also how we debunk them, check out this episode and also Part 1 too! The data science industry needs everyone to participate  and share the real picture of what Data Science is. Check it out and  spread the word! :) Any feedback for us? Here is the form: https://forms.gle/fnnJ6QGrjj4Yv74z5

In this episode, we continue our conversation with Chris Leong. We mostly discuss what is the difference between software engineering and machine learning. Chris also shared more on the projects he worked on during AI Singapore days. And last but not least, some pointers on getting into the Data Science profession. Have a listen!  LinkedIn Profile: https://www.linkedin.com/in/cleongks/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

If you like Machine Learning and Gaming, this episode is really for you. We invited Chris Leong to join us in this episode, where Chris shared how as a software engineer, he managed to include machine learning into his capabilities. We had a surprising conversation talking about AI in gaming and that is how it leads to a 2-part episode. If you are interested to know how Chris joined the AI profession and AI in gaming, the back-end of how it can be used to generate game assets, do give us a listen.  LinkedIn Profile: https://www.linkedin.com/in/cleongks/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

Ever wonder what does a data scientist in an academic institution does? Wonder no more, we invited Ang Shen Ting, Data Scientist @ INSEAD to share his working experience. In this episode, he will be sharing his different experiences, working in the private, public, and academic sectors. If you are thinking about choosing a Masters, have a listen to what was Shen Ting's consideration and...how did he land his job after his Masters. Shen Ting also shared his experience joining hackathons and what he gained from it. Shen Ting's LinkedIn Profile: https://www.linkedin.com/in/angshenting/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

In part 2, Chin Hwee shared some career tips on getting a job and staying competitive. She also talked about the challenges of being a data professional, how much of the time we spent to clean data, and shared her thoughts about the difference between reality and the courses offered in terms of data work. An additional sweetener: Chin Hwee shares how she contributes to the community and what she gained from it. And...another sweetener, we shared on what are the community you can join to enhance your tech career! :) Chin Hwee's LinkedIn Profile: https://www.linkedin.com/in/ongchinhwee/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

We have another guest on our show! We have Chin Hwee, another data engineer to share her work and how she got into the field. She has spoken about data in many conferences as well. In Part 1 (yes a lot of content shared, start listening!), she shared how she interacted with her stakeholders, how she approached each project, and also what are the valuable skills in a data career. We suggest you have a listen now so that you can look forward to the second part that is packed with more content. :) Chin Hwee's LinkedIn Profile: https://www.linkedin.com/in/ongchinhwee/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

In this episode, Thu Ya and Koo will be debunking some common myths that are swirling around in the industry. The myths that will be debunked are the following: 1 - "Got data can do Data Science" 2 - "Data Science is only for big organizations" 3 - "Big data is needed for Data Science" 4 - "Data Science is just about building models" Like to know more why they are myths and also how we debunk them, check out the episode! The data science industry needs everyone to participate and share the real picture of what Data Science is. Check it out and spread the word! :)

Loo Choon Boon, a data engineer from Sephora SEA is our guest for this episode. :) He will be sharing how he become a data engineer and how to do well as a data engineer. He also discussed how important it is to seize opportunities and finding support to get into the field. There is a discussion on what is the difference between data scientist, machine learning engineer and data engineer. Thu Ya also joined into the discussion as well. Do listen to understand more about the role of a data engineer LinkedIn Profile: https://www.linkedin.com/in/loo-choon-boon-6231b1140/

Symbolic Connection reached a milestone! We have our tenth episode. In this episode, we invited Sky You, Talent Hunter from ThoughtWorks to share a typical hiring process in hiring a data professional, as well as tips and tricks to stand out from the crowds. She also cleared our burning question on the impacts of having a personal project(s), having a master degree, and doing a Bootcamp in the hiring process. This is the episode you don't want to miss out on if you are trying to get a data-related role! Sky's LinkedIn: https://www.linkedin.com/in/sky-you/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

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

This episode, we have Cliff Chew, a Data Analyst with Grab. He will be sharing his transition from economist-by-training into a data analyst. He also shares some tips on how economics major can be working on data, what are the tools they can start with. Give a listen to understand his career journey and his unique perspective of life. :) LinkedIn Profile: https://www.linkedin.com/in/kuo-ting-cliff-chew-22001925/ Cliff Chew's Blog: https://cliffchew84.github.io/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

We are discussing about Data Visualization! We discuss why is data visualization important in the Big Data era, talked about the different tools of visualization and more importantly what is a Good visualization and how to get to it. In the episode, we share how one can design good visualization for audiences. Join us in this learning journey to understand a key data science toolkit, data visualization.:) Reference: John Snow's visualization (The Guardian) Visual Cues (Github) Tableau Visual Vocabulary (Tableau)

Our next guest is Charin Polpanumas from Bangkok! He is the Lead Data Scientist at Central Retail, Thailand's largest owners of shopping malls, supermarkets, office depots, and so on. He works on search, ranking, recommendation, CRM,, and all the omnichannel retail shenanigans. In this episode, he will be sharing about his career journey, what are the tools he is currently using and also how does he hire for his team. :) LinkedIn Profile: https://www.linkedin.com/in/cstorm125/ Questions for our Guest: https://forms.gle/YhEtzQ3W7JVTNbHN9

In this episode, we turned the table around. Koo interviewed Thu Ya Kyaw, his co-host, to find out how he becomes a machine learning engineer at NE Digital (a subsidiary of NTUC). He will be sharing his career journey, what got him interested in the field, and what steps he has taken to get into and stay on top of the field. Join us, to understand the work of a machine learning engineer. :) Thu Ya Kyaw (LinkedIn) Einstein Riddle (link) Start here with Machine Learning (link) Docker tutorial (link) Git tutorial (link) TensorFlow Completed Course (link) 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.