Data Science Courses/Degress/Work

miyaying

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Tbh I am not a fan of both programs. The reason is because from what I am understand, for the machine learning part of the course, you perform machine learning using their company's software (IBM watson studio / Microsoft Azure). So you are not really going to have as much hands-on coding as you think. And this is an issue, imagine telling potential employers that you don't know how to use SKLearn / Tensorflow (the libraries which 99% of data scientists in the world use) to write your Machine learning models, but you only know azure/ibm watson. And the worst part, many companies don't exactly subscribe to the software of both companies.

But if you want me to choose between the two, i'd say the IBM curriculum is better!
Thanks so much for the reply!

Seems like both courses may not be worth the time...

Am currently unemployed right now, so am really looking to spend my time wisely as I upskill myself and switch career paths. I've noted your recommendation on Heicoders Academy and am definitely keen to take the A100 course.

Are there any other advice/ tips that you could provide me with as I start my self-learn journey? Would really appreciate any form of guidance! :)
 

miyaying

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I did the IBM course. Strongly recommend against it if you intend to start a career in Data Analytics. Like what DataScience said, you'll be using IBM Watson. It's a no code environment, so you won't have any transferable technical skills outside the IBM ecosystem.

The course also doesn't do much beyond theory. The syllabus could probably be completed in half the time set by the company.
Thanks for your reply!!

This was honestly one of my concerns... that it was going to be all theory and no application/coding. While I know you mentioned that there's no transferable skills, did you feel that potential employers recognised this certification (if you went job hunting thereafter)?
 

PotatoMeister

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Thanks for your reply!!

This was honestly one of my concerns... that it was going to be all theory and no application/coding. While I know you mentioned that there's no transferable skills, did you feel that potential employers recognised this certification (if you went job hunting thereafter)?
Completely not applicable. IBM themselves said that we should only expect to function at sales or consultant level.

Thought I could go with consulting, but no replies from my applications.

Sort of paused job hunting for a while to settle other family matters. But last I heard from my classmates, many still unemployed.
 

miyaying

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Completely not applicable. IBM themselves said that we should only expect to function at sales or consultant level.

Thought I could go with consulting, but no replies from my applications.

Sort of paused job hunting for a while to settle other family matters. But last I heard from my classmates, many still unemployed.
I see!! Thanks for letting me know.

All the best in job hunting!!
 

DataScience

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Thanks so much for the reply!

Seems like both courses may not be worth the time...

Am currently unemployed right now, so am really looking to spend my time wisely as I upskill myself and switch career paths. I've noted your recommendation on Heicoders Academy and am definitely keen to take the A100 course.

Are there any other advice/ tips that you could provide me with as I start my self-learn journey? Would really appreciate any form of guidance! :)
In an ideal world where you have a lot of time and money, then perhaps it might be worth it!

AI100 is suitable if you don't have programming background. If you already have programming background, just go to AI200 straight. AI100 is a very good fundamental course, but I'd say their true game-changer and flagship course is AI200.

As for tips, I'd say, even after taking paid courses, you should check with your instructors for free youtube videos recommendations to continue honing your skills. Even in university, there is no way a degree can finish teaching all that you need to know. I have a degree in software engineering, but even then only 10-20% of what I know came from the university. So in the same vein, do squeeze your instructors dry and seek their advice on other free youtube courses you can take to continue learning. Any instructor worth their salt should be able to give you decent recommendations.
 
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miyaying

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In an ideal world where you have a lot of time and money, then perhaps it might be worth it!

AI100 is suitable if you don't have programming background. If you already have programming background, just go to AI200 straight. AI100 is a very good fundamental course, but I'd say their true game-changer and flagship course is AI200.

As for tips, I'd say, even after taking paid courses, you should check with your instructors for free youtube videos recommendations to continue honing your skills. Even in university, there is no way a degree can finish teaching all that you need to know. I have a degree in software engineering, but even then only 10-20% of what I know came from the university. So in the same vein, do squeeze your instructors dry and seek their advice on other free youtube courses you can take to continue learning. Any instructor worth their salt should be able to give you decent recommendations.
I'm currently in my mid-20s, so perhaps I have time (and youth?!) but no money!! T.T

Thank you for sharing your tips! Will check out the AI200 course and decide if I should take it straight based on what I've managed to learn on my own with Python.
 

Shaneee

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what do you think of upenn mcit masters online and illinois mcs-ds?

Whats the ideal modules combination for gatech omscs? theres alot of modules even if i want to specialise in machine learning for DS
 
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DataScience

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what do you think of upenn mcit masters online and illinois mcs-ds?

Whats the ideal modules combination for gatech omscs? theres alot of modules even if i want to specialise in machine learning for DS
Well Upenn is nice in that it is an ivy league. But the slight problem with Upenn and illinois is that you are paying several times more than the Gatech OMCS. If cost isn't an issue for you, then I think the Upenn choice is quite neat, especially if you want to associated with the ivy leagues. The ivy leagues has a very strong alumni network. For instance when i wanted to raise cash for my startup, I was able to do so easily by just circulating around in the alum network of the ivy league i came from.

As for ideal modules, you should just someone who is taking/or has taken OMCSC. Because sometimes, even if a module's curriculum sound perfect on paper, the delivery may suck. I heard from colleagues that there are some courses in OMCSC that are bad. And I had the same experience in my own university as well.

If I don't remember wrongly you were taking AI200 or something right, you should ask your instructors, I know they have several ppl who have taken/or are taking that right know,

But generally you should learn: optimisation, supervised and unsupervised learning, deep learning, and possibly reinforcement learning.
 

Shaneee

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Well Upenn is nice in that it is an ivy league. But the slight problem with Upenn and illinois is that you are paying several times more than the Gatech OMCS. If cost isn't an issue for you, then I think the Upenn choice is quite neat, especially if you want to associated with the ivy leagues. The ivy leagues has a very strong alumni network. For instance when i wanted to raise cash for my startup, I was able to do so easily by just circulating around in the alum network of the ivy league i came from.

As for ideal modules, you should just someone who is taking/or has taken OMCSC. Because sometimes, even if a module's curriculum sound perfect on paper, the delivery may suck. I heard from colleagues that there are some courses in OMCSC that are bad. And I had the same experience in my own university as well.

If I don't remember wrongly you were taking AI200 or something right, you should ask your instructors, I know they have several ppl who have taken/or are taking that right know,

But generally you should learn: optimisation, supervised and unsupervised learning, deep learning, and possibly reinforcement learning.
The upenn master of computer and information technology seems to cater for non tech bachelor , i guess its curriculum should be fine for a master cs?

Im still exploring options for online master and also noting down what modules to take it i were to enroll.
 

nwstbz23

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As for ideal modules, you should just someone who is taking/or has taken OMCSC. Because sometimes, even if a module's curriculum sound perfect on paper, the delivery may suck. I heard from colleagues that there are some courses in OMCSC that are bad. And I had the same experience in my own university as well.

If I don't remember wrongly you were taking AI200 or something right, you should ask your instructors, I know they have several ppl who have taken/or are taking that right know,

But generally you should learn: optimisation, supervised and unsupervised learning, deep learning, and possibly reinforcement learning.
Thanks for sharing the suggested topics to learn. A review website for OMSCS modules is available at https://omscentral.com/courses

I also read a recent KDNuggets article for the resources to cover data science subject areas for beginners. https://www.kdnuggets.com/2021/09/path-full-stack-data-science.html
@DataScience Is the suggested list of data science resources a good start for beginners here? Will you have any other topics/areas/subjects to add on?

Thanks for your time to contribute to this forum!
 

Jpeaches

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Hi DS,

I am a beginner at Python, I have taken some Python courses in the past at Ngee Ann Poly but I am in need of a refresher. I am keen to take up a Data analyst role in the future. My background is in Social sciences, I have some prior knowledge of statistics from college as well.

I am wondering if I should take up Georgia Tech’s Professional Certificate in Introduction to Python Programming. Would you recommend this course? Part of the reason why I am interested to take up this course was because I am prepping myself to apply for the OMSA at Georgia Tech.

Any other suggestions/ programs that you think might help would be very useful!
 
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-Wanderer-

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Guys, wanted to just gauge the interest of those interested in taking the OMSA at Georgia Tech over here. If yes, maybe we can form a study group if we do take it up...
 
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Eririn

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Guys, wanted to just gauge the interest of those interested in taking the OMSA at Georgia Tech over here. If yes, maybe we can form a study group if we do take it up...
I'm interested in Georgia Tech's online masters! But am interested in OMSCS instead of OMSA
 

DataScience

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Thanks for sharing the suggested topics to learn. A review website for OMSCS modules is available at https://omscentral.com/courses

I also read a recent KDNuggets article for the resources to cover data science subject areas for beginners. https://www.kdnuggets.com/2021/09/path-full-stack-data-science.html
@DataScience Is the suggested list of data science resources a good start for beginners here? Will you have any other topics/areas/subjects to add on?

Thanks for your time to contribute to this forum!
To be honest, I don't quite like this list of resource. It literally just lists everything under the sun in the world of programming. For example, things like C/C++ is totally irrelevant to data science. IMO, just focus on the key things that matter:

- database
- python programming
- data manipulation with pandas
- regression/classification/clustering models with SK-learn (if possible learn a bit of the math behind these)
- ensemble learning
- recommenders systems
- deep learning in computer vision (with tensorflow)
- OOP in python
- deploying machine learning models
- Github

Stretch goals:
- Distributed computing (hadoop)
- Natural language processing

Good to know:
- Optimisation

With this, you'd make a very holistic and attractive employee! In fact, there are many data scientists that don't actually possess all of the skills I mentioned in this list. Additionally, the best data scientists in the field should have knowledge of all of these, but specialise in one / two areas. For example, I personally specialise in computer vision & recommenders systems, since my company builds these engines for other clients.
 
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DataScience

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Hi DS,

I am a beginner at Python, I have taken some Python courses in the past at Ngee Ann Poly but I am in need of a refresher. I am keen to take up a Data analyst role in the future. My background is in Social sciences, I have some prior knowledge of statistics from college as well.

I am wondering if I should take up Georgia Tech’s Professional Certificate in Introduction to Python Programming. Would you recommend this course? Part of the reason why I am interested to take up this course was because I am prepping myself to apply for the OMSA at Georgia Tech.

Any other suggestions/ programs that you think might help would be very useful!
For those that have zero programming background I would usually recommend Heicoders Academy's AI100. But If you already have some python background, then don't waste money on courses. What you can do is:

1) First go youtube and find some python programming video (plenty of good and free ones out there)
2) Go hackerrank / leetcode and start practicing different types of python questions (good training for technical interviews)

If you are attending OMSA, it would be beneficial to be acquainted with some OOP concepts + using sklearn to generate machine learning models. This will greatly reduce your learning curve later on.
 

DataScience

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The upenn master of computer and information technology seems to cater for non tech bachelor , i guess its curriculum should be fine for a master cs?

Im still exploring options for online master and also noting down what modules to take it i were to enroll.
I am personally more inclined to Georgia tech OMSCS because (1) cheaper, and (2) the technical depth and breadth is much more. This particular program of Upenn's is only worth considering because it is an ivy league. But for the cost, I honestly don't think it is worth it. For the same cost, you can go for even better schools like berkeley.
 

nwstbz23

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To be honest, I don't quite like this list of resource. It literally just lists everything under the sun in the world of programming. For example, things like C/C++ is totally irrelevant to data science. IMO, just focus on the key things that matter:

- database
- python programming
- data manipulation with pandas
- regression/classification/clustering models with SK-learn (if possible learn a bit of the math behind these)
- ensemble learning
- recommenders systems
- deep learning in computer vision (with tensorflow)
- OOP in python
- deploying machine learning models
- Github

Stretch goals:
- Distributed computing (hadoop)
- Natural language processing

Good to know:
- Optimisation

With this, you'd make a very holistic and attractive employee! In fact, there are many data scientists that don't actually possess all of the skills I mentioned in this list. Additionally, the best data scientists in the field should have knowledge of all of these, but specialise in one / two areas. For example, I personally specialise in computer vision & recommenders systems, since my company builds these engines for other clients.
Thanks @DataScience for sharing your views using your work experience to confirm the key concepts needed for the industry. It appears that data science people are skilled generalists with a few specialities/domains (correct me if I am wrong) from my own experience working with data science people.

Here is one Harvard Business Review article advocating data science generalists at https://hbr.org/2019/03/why-data-science-teams-need-generalists-not-specialists
 

KingJedi

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Tbh I am not a fan of both programs. The reason is because from what I am understand, for the machine learning part of the course, you perform machine learning using their company's software (IBM watson studio / Microsoft Azure). So you are not really going to have as much hands-on coding as you think. And this is an issue, imagine telling potential employers that you don't know how to use SKLearn / Tensorflow (the libraries which 99% of data scientists in the world use) to write your Machine learning models, but you only know azure/ibm watson. And the worst part, many companies don't exactly subscribe to the software of both companies.

But if you want me to choose between the two, i'd say the IBM curriculum is better!
Hi DataScince,

Thank you for sharing your valuble advises!

Currently there are some courses as part of Skillsfuture and IMDA separately. What are you views on the below courses and which one is worth taking?

1) Skills ignition -Google: Data Engineering with Machine Learning Fundamentals (taught by third party company Agilitics.)
2) Microsoft - SGUP-CT Jr Full stack Devloper

Also, for cloud courses below, what are your views?
1) Skills ignition -Google: Cloudy Technology (taught by third party company Agilitics.)
2) Microsoft - SGUP-CT Jr Cloud Support & Devops

Is IBM i.am-vitalize (AI Track) still the better choice compare to above courses?

Would really appreciate your thoughts!
Thanks !!!
 

Carlini

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Hi DataScience,
Riding on the earlier qn by KingJedi, how abt NTU PACE SGUS 6-mth Software Engineer course? This course actually teaches JavaScript extensively.. so newbie can master at least 1 programming language to launch a tech career….
 

DataScience

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Thanks @DataScience for sharing your views using your work experience to confirm the key concepts needed for the industry. It appears that data science people are skilled generalists with a few specialities/domains (correct me if I am wrong) from my own experience working with data science people.

Here is one Harvard Business Review article advocating data science generalists at https://hbr.org/2019/03/why-data-science-teams-need-generalists-not-specialists
Yes it is true. It is beneficial for data scientists to be generalist in the sense that you are exposed to different types of techniques and domains in data science. Some domain includes: recommender systems, NLP, computer vision etc. One benefit of this is that sometimes, being strong in one domain can lead to inspiration in another domain. For example, the recent advancements in NLP has been largely attributed to prior advancements of deep learning in computer vision.

However, at the end of the day, you need to be particularly strong in one of the domain. You should be the first person that others think of if they want to consult an "NLP expert" for instance. This will then ensure that you are highly sought after.
 
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