Data Science Courses/Degress/Work

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Many post-grad programmes require referee letters, what if I am unable to get enough referee letter? Anyone has this problem as well? I feel deterred by programmes that ask for referee letters.
 

DataScience

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Many post-grad programmes require referee letters, what if I am unable to get enough referee letter? Anyone has this problem as well? I feel deterred by programmes that ask for referee letters.

It is quite unlikely that you are unable to find someone who is able to write you a referral letter unless there really isn't anyone in this world who doesn't have a decent enough impression of you to write for you. You can look for supervisors, and your ex-lecturers for help in this area.

Honestly all the programs that are worth applying to will require referee letters. And conversely those programs that are super easy to get in are just there to milk your money and a waste of time.
 

TL4GG18

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Hi Data science,
Thank you for sharing so much insights in this thread. I have been following with interest. For a mid career change (38 years old in non related field, but has an engineering bachelor degree), are data science Jobs like data analyst still worth a try to switch over? I am actually earning quite ok now. The main reason for changing is "for a change in job scope" and for my case, I am sure it's a pay cut. Work life balance I am unsure too. Not sure is it just me, I am having the millennials' tendency to " try something different" time to time.
 

DataScience

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Hi Data science,
Thank you for sharing so much insights in this thread. I have been following with interest. For a mid career change (38 years old in non related field, but has an engineering bachelor degree), are data science Jobs like data analyst still worth a try to switch over? I am actually earning quite ok now. The main reason for changing is "for a change in job scope" and for my case, I am sure it's a pay cut. Work life balance I am unsure too. Not sure is it just me, I am having the millennials' tendency to " try something different" time to time.
Hi my friend, if you already have a good job which you are happy with (and is paying you well), I am not so sure if a career switch is something which you want to pursue now, especially given that you have a family. I would say it is best not to take such risk unless you are very certain you have interest in this field or if you current job has no prospect.

Once advice I can give you is to take a few courses on data science, and then
- You will be able to see for yourself if it is something that you enjoy
- Even if you decide not to go into the field, I feel it is important that everyone in the workforce are data literate. In fact, it may even help you become better at your current role

And after taking a few courses, what you can do is seek out the data science team in your company, and volunteer your time to help them out with projects. This will help you ascertain whether a career in this is something you really want, help you build some experience, and also it will be better if your company can facilitate an internal transfer for you to join the data science team. This is the less risky option that I can think of for you.
 

TL4GG18

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Hi my friend, if you already have a good job which you are happy with (and is paying you well), I am not so sure if a career switch is something which you want to pursue now, especially given that you have a family. I would say it is best not to take such risk unless you are very certain you have interest in this field or if you current job has no prospect.

Once advice I can give you is to take a few courses on data science, and then
- You will be able to see for yourself if it is something that you enjoy
- Even if you decide not to go into the field, I feel it is important that everyone in the workforce are data literate. In fact, it may even help you become better at your current role

And after taking a few courses, what you can do is seek out the data science team in your company, and volunteer your time to help them out with projects. This will help you ascertain whether a career in this is something you really want, help you build some experience, and also it will be better if your company can facilitate an internal transfer for you to join the data science team. This is the less risky option that I can think of for you.
Thank you Data science for your advice. I will consider 🙏
 
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Hello Guys,

I just finished my AI100 course from Heicoders recently (thanks to DataScience's recommendation).

Here is my take on this course:

It's really a good bridging course if you want to learn Python from scratch (and very recommended if you don't have any programming background).
For me, this course is somehow not really an eye-opener as I have a Software Engineering background and well-versed in C, C++ programming languages.
However, in the last 2 lessons, they touched on data filtering and visualization and I consider those as advanced topics to prepare the students for AI200.

I took this course as I was not confident in my Python skill (I only managed to read some tutorials on Python before this class) and if it was enough for their advanced course AI200.

To conclude,
If you don't have any programming background before and want to learn Python as your first language, this is really a good course as the instructor is very knowledgeable and is very patient in explaining all the details (in a right pace).

Otherwise, if you have a programming background before (be it Python or any other programming language) and considering to take AI200, I would say skip this class and go directly to AI200.

Hope this review helps. :)
 
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today114

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Hello Guys,

I just finished my AI100 course from Heicoders recently (thanks to DataScience's recommendation).

Here is my take on this course:

It's really a good bridging course if you want to learn Python from scratch (and very recommended if you don't have any programming background).
For me, this course is somehow not really an eye-opener as I have a Software Engineering background and well-versed in C, C++ programming languages.
However, in the last 2 courses, they touched on data filtering and visualization and I consider those as advanced topics to prepare the students for AI200.

I took this course as I was not confident in my Python skill (I only managed to read some tutorials on Python before this class) and if it was enough for their advanced course AI200.

To conclude,
If you don't have any programming background before and want to learn Python as your first language, this is really a good course as the instructor is very knowledgeable and is very patient in explaining all the details (in a right pace).

Otherwise, if you have a programming background before (be it Python or any other programming language) and considering to take AI200, I would say to skip this class and go directly to AI200.

Hope this review helps. :)
Thanks for the review! :)

I was slightly worried as I am skipping straight to AI200. Took C before in my undergrad days, and have been self-studying Python.

Will give a review on AI200 once I complete it.
 

nwstbz23

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Thanks for the review! :)

I was slightly worried as I am skipping straight to AI200. Took C before in my undergrad days, and have been self-studying Python.

Will give a review on AI200 once I complete it.
You are in a similar situation as me. I skipped AI100 due to previous data science studies and completed AI200 module recently. AI200 module from my experience, is an efficient way to expose learners to data science essentials and to assess the suitability towards the data science field.

Do enjoy the learning journey and the capstone project is a tough one to reinforce the contents taught :)
 

DataScience

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I am glad people are benefitting from my recommendations. All the best to you guys in your data science endeavours.
 

koolkidz1994

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Hi @DataScience,

I’m currently a Data Scientist working in one of the tech companies (eg. Shopee/Grab/Gojek/Lazada) in SG. I have 2-3 years of relevant working experience and I’m also pursuing OMSCS at GaTech. Your background is really interesting and I would love to hear your thoughts on the following questions:
  1. Can you share more about your experience working as a Data Scientist in the US? How mature was the data infrastructure of the companies you worked at and what kind of problems were you solving?
  2. You mentioned that the level of Data Science in US is much much higher than that in SG, why do you say so? How is the level of US companies compared to tech companies such as Shopee or Grab? Are mid-tier firms in US better than Shopee or Grab?
  3. How do you think someone with my profile can break into the US market and land a data science job in at least a mid-tier firm? To be specific, I’m interested in data science jobs focused on building ML systems and not analytics/BI
  4. You mentioned that you were the Director of an AI firm and now run your own AI firm. I can imagine that changing from a Data Scientist to a Director to a Founder would require quite a mindset shift and gaining of new skills, since you went from technical to managerial to having to run your own firm. How did you manage this transition and can you share any valuable learnings you had in the process?
    • To share more context: While I’m still relatively new in my career, I’m also thinking of my future career path. So far, the immediate step is to continue gaining experience and progress towards being a Senior Data Scientist. Beyond that however, there are still some uncertainties (whether to continue the technical route to become a Chief Data Scientist or pursue the managerial route instead, whether I will start my own AI consulting firm in the future, and whether it’s worth pursuing overseas working opportunities). Therefore, your learnings may be helpful to me in deciding which route to pursue in the future
Thanks a lot and I look forward to your reply!
 

paper82

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Hi @DataScience,

Which 1 good to start to build solid fundamental ?

i know bash , SQL query and C but seldom do programming task.

https://www.tp.edu.sg/schools-and-c...loma-in-engineering-computer-engineering.html
Electronics & Digital Principles
This module provides students with basic knowledge of electronics. Fundamental concepts in digital electronics and electronics devices are taught through analysis of basic functional circuits and their applications. With the knowledge, students would be able to understand the functionality of the devices in common applications. This subject also aims to develop in them proficiency in checking and testing the parameters of these devices, as well as in the analysis of their application in simple digital and analog electronic circuits.

Computing & Programming
It encompasses the process of decomposing a problem into a sequence of smaller abstractions which are implemented in software in a structured top-down approach. Software implementation includes the process of designing, writing, testing, and debugging program code.


Data Structures & Algorithms
This subject introduces the fundamentals of recursion and data structures in solving problems using a programming language. Topics covered include stacks, queues and linked lists. Searching techniques and sorting algorithms are also covered.


https://www.tp.edu.sg/schools-and-c...al-media-applied-artificial-intelligence.html

Computational Thinking
This subject introduces students to the fundamentals of computational thinking and their application in developing programming solutions to problems. Topics covered include programming concepts, simple data structures and programming techniques.
Coding & Development Project
This subject introduces students to coding principles and practices using an object-oriented approach. The subject also introduces the development of an IT application using the latest technologies. Topics covered include object and classes, composition, simple data structures, application architecture, design and development

Database Application Development
This subject will introduce the fundamental concepts of relational database systems, the design methods specific to relational database, database manipulation using a database query language, and the techniques of implementing relational databases. It will also cover implementation of simple application to access relational database.
 

DataScience

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Hi @DataScience,

I’m currently a Data Scientist working in one of the tech companies (eg. Shopee/Grab/Gojek/Lazada) in SG. I have 2-3 years of relevant working experience and I’m also pursuing OMSCS at GaTech. Your background is really interesting and I would love to hear your thoughts on the following questions:
  1. Can you share more about your experience working as a Data Scientist in the US? How mature was the data infrastructure of the companies you worked at and what kind of problems were you solving?
  2. You mentioned that the level of Data Science in US is much much higher than that in SG, why do you say so? How is the level of US companies compared to tech companies such as Shopee or Grab? Are mid-tier firms in US better than Shopee or Grab?
  3. How do you think someone with my profile can break into the US market and land a data science job in at least a mid-tier firm? To be specific, I’m interested in data science jobs focused on building ML systems and not analytics/BI
  4. You mentioned that you were the Director of an AI firm and now run your own AI firm. I can imagine that changing from a Data Scientist to a Director to a Founder would require quite a mindset shift and gaining of new skills, since you went from technical to managerial to having to run your own firm. How did you manage this transition and can you share any valuable learnings you had in the process?
    • To share more context: While I’m still relatively new in my career, I’m also thinking of my future career path. So far, the immediate step is to continue gaining experience and progress towards being a Senior Data Scientist. Beyond that however, there are still some uncertainties (whether to continue the technical route to become a Chief Data Scientist or pursue the managerial route instead, whether I will start my own AI consulting firm in the future, and whether it’s worth pursuing overseas working opportunities). Therefore, your learnings may be helpful to me in deciding which route to pursue in the future
Thanks a lot and I look forward to your reply!

Hi Koolkidz,

1) The data infrastructure was in US was definitely much more matured as compared to SG when I was working in the US. But when I was working in the US, many companies in SG were still new to data science and were still in the midst of establishing their ETL data pipelines and database infrastructure. Meanwhile, many companies in the US already had the infrastructure, and were well on their way to building and deploying models for production. I spent some time on NLP in a search engine company, and worked on recommender systems for an eCommerce company. However, I would say, today SG has definitely picked up the slack quite a fair bit, especially for tech leaders such as Shopee/Grab/Gojek/Lazada.

2) In the US, the minimum bar to enter the industry as a data scientists for many top firms (especially FAANG) is a masters degree in a math/stats/OR degree. Many of the mid-management leads either had many years of work experience, or had a phd and had published some groundbreaking research in the field. I would say, one good thing about the US is that the research university often work very closely with the industry, and so the industry is always able to stay on top of the cutting edge advancements. This is one aspect which is pretty absent in Singapore.

In my company, I was surrounded by phd colleagues. While that environment was pretty intimidating, I learnt a lot. And plus, Singaporeans tend to hold their own when they go overseas. So if you do manage to go over, I am sure you will grow alot if you manage to survive the pressure and rise above it. Key thing is to stay humble and hungry. And it is very hard for me to compare mid-tier US vs Shoppe/Grab. There is a myriad of such firms in US and they tend to vary in terms in tech expertise and their foray in data science. However, if you do intend to work in the US, i would advise you to just shoot for the top companies. Some of the mid-tier companies there can be very fluff (i.e raise a lot of money to build models for a problem that can't be solved)

3) Yes you definitely stand a chance. I have juniors and employees with the same profile as you who were able to break into US. And with Donald trump out of the way, it is now much easier to get company to sponsor your visa. Do note that there are companies out there that can help you with your job application to FAANG, so you may consider engaging the help of those companies. Also, spam leetcode, you will need it. They tend to like to ask a bunch of difficult algorithmic questions in their technical interview.

4) The transition is definitely going to have its challenges. Personally I had to grapple with the imposter syndrome. You just have to fake it till you grow comfortable in your own skin. The second challenge is that you as a manager your main focus now become the "people problem". To learn to deal with that, I spent a lot of time reading management books, and watching interviews of csuite level people to find my own management style. However, regardless of your management style, the key thing I learnt is always that you always have to show care for your employees, and constantly find ways to help them be the best version of themselves. Only then will they work to grow your business/career. This is a motherhood and simple principle, but sometimes is seemingly neglected by many managers.
 

DataScience

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Hi @DataScience,

Which 1 good to start to build solid fundamental ?

i know bash , SQL query and C but seldom do programming task.

https://www.tp.edu.sg/schools-and-c...loma-in-engineering-computer-engineering.html
Electronics & Digital Principles
This module provides students with basic knowledge of electronics. Fundamental concepts in digital electronics and electronics devices are taught through analysis of basic functional circuits and their applications. With the knowledge, students would be able to understand the functionality of the devices in common applications. This subject also aims to develop in them proficiency in checking and testing the parameters of these devices, as well as in the analysis of their application in simple digital and analog electronic circuits.

Computing & Programming
It encompasses the process of decomposing a problem into a sequence of smaller abstractions which are implemented in software in a structured top-down approach. Software implementation includes the process of designing, writing, testing, and debugging program code.


Data Structures & Algorithms
This subject introduces the fundamentals of recursion and data structures in solving problems using a programming language. Topics covered include stacks, queues and linked lists. Searching techniques and sorting algorithms are also covered.


https://www.tp.edu.sg/schools-and-c...al-media-applied-artificial-intelligence.html

Computational Thinking
This subject introduces students to the fundamentals of computational thinking and their application in developing programming solutions to problems. Topics covered include programming concepts, simple data structures and programming techniques.
Coding & Development Project
This subject introduces students to coding principles and practices using an object-oriented approach. The subject also introduces the development of an IT application using the latest technologies. Topics covered include object and classes, composition, simple data structures, application architecture, design and development

Database Application Development
This subject will introduce the fundamental concepts of relational database systems, the design methods specific to relational database, database manipulation using a database query language, and the techniques of implementing relational databases. It will also cover implementation of simple application to access relational database.
Hi,

Can I just better understand what your goals is, because these 2 courses will drive you in different career directions. And honestly, I really am not a fan of any tech courses in Poly. They are usually taught by lecturers who are too out of touch with the industry to impart anything relevant to you. And most of the learning is done through rote memorisation
 

paper82

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Hi,

Can I just better understand what your goals is, because these 2 courses will drive you in different career directions. And honestly, I really am not a fan of any tech courses in Poly. They are usually taught by lecturers who are too out of touch with the industry to impart anything relevant to you. And most of the learning is done through rote memorisation
Hi Data Science , my goal is improve my scripting , logic , database knowledge and automation skillset.
 

GoodBetterBest

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wanted to ask something about Kaggle datasets that DA usually practices with, how do you all actually store these datasets in actual working environment? cause downloading 10mb to 1gb+ worth of datasets into local repository is quite messy even though I can easily place "data/*" in the .gitignore file.

https://dvc.org/
Version control for ML models. It uses Github for versioning but store the data in other cloud such as Google. But I guess you still store it locally to use it. Github will keep track for you. GH is like a pointer to the could versioning. Hope it's useful for you. I've not tried yet.
 

GoodBetterBest

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Do Data Science experts actually read the sklearn or tensorflow manuals cover to cover ? They seems to know so much. And they can point back to the manual when there is a question.
 

DataScience

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Hi Data Science , my goal is improve my scripting , logic , database knowledge and automation skillset.
Hi in that case I don't think both of those courses achieve those. You are better off just looking for free online resources because you already have some basic knowledge of programming.
 

DataScience

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Do Data Science experts actually read the sklearn or tensorflow manuals cover to cover ? They seems to know so much. And they can point back to the manual when there is a question.
Narh, its too boring to learn from cover to cover. Personally, what I do is find some videos on the topic and watch it to get a broad understanding. Then I read the documentations broadly just to understand how they index the content.

Then I use the library for projects, and that is when you actually learn and consolidate your knowledge. Programming is something where you have to learn through doing.
 
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