How long would it take to become proficient in machine learning for someone with a non-statistical mathematical background? The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhat skills do I need to become a data scientist? And how to show them?Research in high-dimensional statistics vs. machine learning?Do Data Miners realise Machine Learning cannot establish Causality?Hidden Markov Models: Linking states to labels after EM trainingApplied statistics in Machine Learning, AI, Neural NetworksHow can learn machine learning with Statistics background?Why positive-unlabeled learning?GANs and grayscale imagery colorizationStatistical test for machine learningIs there a possible intersection of mathematical logic and machine learning?How do I approach learning Data Science/ML the 'rightest' way?
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How long would it take to become proficient in machine learning for someone with a non-statistical mathematical background?
The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhat skills do I need to become a data scientist? And how to show them?Research in high-dimensional statistics vs. machine learning?Do Data Miners realise Machine Learning cannot establish Causality?Hidden Markov Models: Linking states to labels after EM trainingApplied statistics in Machine Learning, AI, Neural NetworksHow can learn machine learning with Statistics background?Why positive-unlabeled learning?GANs and grayscale imagery colorizationStatistical test for machine learningIs there a possible intersection of mathematical logic and machine learning?How do I approach learning Data Science/ML the 'rightest' way?
$begingroup$
I am currently a postdoc and my PhD was in applied mathematics in the area of numerical analysis and electromagnetic/acoustic wave propagation. There was no statistical element to my PhD, it was completely deterministic. I took several probability/statistics and one machine learning module 5-6 years ago during my BSc, and a stochastic ODE module during my MSc but that's about it..its been all applied mathematics since then.
I am considering leaving academia and entering industry and it seems like there are far more jobs in the area of data science/machine learning than there are for my skillset.
- If I left academia and began 'studying up', how long do you think it could take me to gain the skills required for a data science/machine learning position in industry?
- It seems like there is a very wide variety of science/machine learning techniques and obviously there isn't time to learn all or even most of them. So what approaches are absolutely essential for data science/machine learning in industry these days and what is the most efficient route to gaining these skills?
machine-learning classification clustering statistics
asked Mar 31 at 12:57
electroscienceelectroscience
132
$endgroup$
add a comment |
$begingroup$
I am currently a postdoc and my PhD was in applied mathematics in the area of numerical analysis and electromagnetic/acoustic wave propagation. There was no statistical element to my PhD, it was completely deterministic. I took several probability/statistics and one machine learning module 5-6 years ago during my BSc, and a stochastic ODE module during my MSc but that's about it..its been all applied mathematics since then.
I am considering leaving academia and entering industry and it seems like there are far more jobs in the area of data science/machine learning than there are for my skillset.
- If I left academia and began 'studying up', how long do you think it could take me to gain the skills required for a data science/machine learning position in industry?
- It seems like there is a very wide variety of science/machine learning techniques and obviously there isn't time to learn all or even most of them. So what approaches are absolutely essential for data science/machine learning in industry these days and what is the most efficient route to gaining these skills?
machine-learning classification clustering statistics
asked Mar 31 at 12:57
electroscienceelectroscience
132
$endgroup$
1
$begingroup$
datascience.stackexchange.com/questions/47854/… give this a check for more courses. You have a strong mathematical background and should be ready is few months. Also you have strong signal processing background that can be really useful for computer vision and audio processing
$endgroup$
– Pedro Henrique Monforte
Apr 1 at 12:36
add a comment |
$begingroup$
I am currently a postdoc and my PhD was in applied mathematics in the area of numerical analysis and electromagnetic/acoustic wave propagation. There was no statistical element to my PhD, it was completely deterministic. I took several probability/statistics and one machine learning module 5-6 years ago during my BSc, and a stochastic ODE module during my MSc but that's about it..its been all applied mathematics since then.
I am considering leaving academia and entering industry and it seems like there are far more jobs in the area of data science/machine learning than there are for my skillset.
- If I left academia and began 'studying up', how long do you think it could take me to gain the skills required for a data science/machine learning position in industry?
- It seems like there is a very wide variety of science/machine learning techniques and obviously there isn't time to learn all or even most of them. So what approaches are absolutely essential for data science/machine learning in industry these days and what is the most efficient route to gaining these skills?
machine-learning classification clustering statistics
asked Mar 31 at 12:57
electroscienceelectroscience
132
$endgroup$
I am currently a postdoc and my PhD was in applied mathematics in the area of numerical analysis and electromagnetic/acoustic wave propagation. There was no statistical element to my PhD, it was completely deterministic. I took several probability/statistics and one machine learning module 5-6 years ago during my BSc, and a stochastic ODE module during my MSc but that's about it..its been all applied mathematics since then.
I am considering leaving academia and entering industry and it seems like there are far more jobs in the area of data science/machine learning than there are for my skillset.
- If I left academia and began 'studying up', how long do you think it could take me to gain the skills required for a data science/machine learning position in industry?
- It seems like there is a very wide variety of science/machine learning techniques and obviously there isn't time to learn all or even most of them. So what approaches are absolutely essential for data science/machine learning in industry these days and what is the most efficient route to gaining these skills?
machine-learning classification clustering statistics
machine-learning classification clustering statistics
asked Mar 31 at 12:57
electroscienceelectroscience
132
asked Mar 31 at 12:57
electroscienceelectroscience
132
asked Mar 31 at 12:57
electroscienceelectroscience
132
asked Mar 31 at 12:57
electroscienceelectroscience
132
asked Mar 31 at 12:57
electroscienceelectroscience
132
132
1
$begingroup$
datascience.stackexchange.com/questions/47854/… give this a check for more courses. You have a strong mathematical background and should be ready is few months. Also you have strong signal processing background that can be really useful for computer vision and audio processing
$endgroup$
– Pedro Henrique Monforte
Apr 1 at 12:36
add a comment |
1
$begingroup$
datascience.stackexchange.com/questions/47854/… give this a check for more courses. You have a strong mathematical background and should be ready is few months. Also you have strong signal processing background that can be really useful for computer vision and audio processing
$endgroup$
– Pedro Henrique Monforte
Apr 1 at 12:36
1
1
$begingroup$
datascience.stackexchange.com/questions/47854/… give this a check for more courses. You have a strong mathematical background and should be ready is few months. Also you have strong signal processing background that can be really useful for computer vision and audio processing
$endgroup$
– Pedro Henrique Monforte
Apr 1 at 12:36
$begingroup$
datascience.stackexchange.com/questions/47854/… give this a check for more courses. You have a strong mathematical background and should be ready is few months. Also you have strong signal processing background that can be really useful for computer vision and audio processing
$endgroup$
– Pedro Henrique Monforte
Apr 1 at 12:36
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
As the market is in desperate need of people, and there are plenty of people with absolutely no formal training and no background in statistics, you are already perfectly qualified to spin this hype wheel and call yourself a "data scientist", too.
I'm not kidding. Just do some free online courses and you'll likely see that you can do all they ask for. Data science is about buzzword bingo, not about being smart at statistics not good at coding (unfortunately).
If you don't want to feel like an impostor, I suggest the following: find some important algorithm still missing from the big toolkits such as sklearn, R, Weka, ELKI. Implement it, and contribute it to some open-source toolkit. Then you can call yourself an "sklearn contributor" in your resume, which puts you ahead of 90% of self-proclaimed data scientists. What could make you a more proven data scientist / machine learner than having written code used by other data scientists / machine learners?
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
$endgroup$
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
add a comment |
$begingroup$
I think you already know enough applied mathematics to begin with. You can pick-up rest of it as required.
One option is :
- Start with an online course that provides high level overview of machine learning and types of algorithms (E.g.: https://www.coursera.org/learn/machine-learning)
- Start applying the knowledge in real world problems as soon as possible.
- Learn various types of neural networks (deeplearning.ai is one place to start)
- Apply knowledge to real world problems (Such as Audio/Video classification , Natural Language)
- Get an internship
This will take 5 - 6 months.
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
$endgroup$
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
As the market is in desperate need of people, and there are plenty of people with absolutely no formal training and no background in statistics, you are already perfectly qualified to spin this hype wheel and call yourself a "data scientist", too.
I'm not kidding. Just do some free online courses and you'll likely see that you can do all they ask for. Data science is about buzzword bingo, not about being smart at statistics not good at coding (unfortunately).
If you don't want to feel like an impostor, I suggest the following: find some important algorithm still missing from the big toolkits such as sklearn, R, Weka, ELKI. Implement it, and contribute it to some open-source toolkit. Then you can call yourself an "sklearn contributor" in your resume, which puts you ahead of 90% of self-proclaimed data scientists. What could make you a more proven data scientist / machine learner than having written code used by other data scientists / machine learners?
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
$endgroup$
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
add a comment |
$begingroup$
As the market is in desperate need of people, and there are plenty of people with absolutely no formal training and no background in statistics, you are already perfectly qualified to spin this hype wheel and call yourself a "data scientist", too.
I'm not kidding. Just do some free online courses and you'll likely see that you can do all they ask for. Data science is about buzzword bingo, not about being smart at statistics not good at coding (unfortunately).
If you don't want to feel like an impostor, I suggest the following: find some important algorithm still missing from the big toolkits such as sklearn, R, Weka, ELKI. Implement it, and contribute it to some open-source toolkit. Then you can call yourself an "sklearn contributor" in your resume, which puts you ahead of 90% of self-proclaimed data scientists. What could make you a more proven data scientist / machine learner than having written code used by other data scientists / machine learners?
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
$endgroup$
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
add a comment |
$begingroup$
As the market is in desperate need of people, and there are plenty of people with absolutely no formal training and no background in statistics, you are already perfectly qualified to spin this hype wheel and call yourself a "data scientist", too.
I'm not kidding. Just do some free online courses and you'll likely see that you can do all they ask for. Data science is about buzzword bingo, not about being smart at statistics not good at coding (unfortunately).
If you don't want to feel like an impostor, I suggest the following: find some important algorithm still missing from the big toolkits such as sklearn, R, Weka, ELKI. Implement it, and contribute it to some open-source toolkit. Then you can call yourself an "sklearn contributor" in your resume, which puts you ahead of 90% of self-proclaimed data scientists. What could make you a more proven data scientist / machine learner than having written code used by other data scientists / machine learners?
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
$endgroup$
As the market is in desperate need of people, and there are plenty of people with absolutely no formal training and no background in statistics, you are already perfectly qualified to spin this hype wheel and call yourself a "data scientist", too.
I'm not kidding. Just do some free online courses and you'll likely see that you can do all they ask for. Data science is about buzzword bingo, not about being smart at statistics not good at coding (unfortunately).
If you don't want to feel like an impostor, I suggest the following: find some important algorithm still missing from the big toolkits such as sklearn, R, Weka, ELKI. Implement it, and contribute it to some open-source toolkit. Then you can call yourself an "sklearn contributor" in your resume, which puts you ahead of 90% of self-proclaimed data scientists. What could make you a more proven data scientist / machine learner than having written code used by other data scientists / machine learners?
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
edited Mar 31 at 15:06
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
answered Mar 31 at 15:01
Anony-MousseAnony-Mousse
5,165625
5,165625
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
add a comment |
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
$begingroup$
Contributing to an open-source toolkit sounds like a good idea alright, I will certainly look into that.
$endgroup$
– electroscience
Apr 1 at 7:04
add a comment |
$begingroup$
I think you already know enough applied mathematics to begin with. You can pick-up rest of it as required.
One option is :
- Start with an online course that provides high level overview of machine learning and types of algorithms (E.g.: https://www.coursera.org/learn/machine-learning)
- Start applying the knowledge in real world problems as soon as possible.
- Learn various types of neural networks (deeplearning.ai is one place to start)
- Apply knowledge to real world problems (Such as Audio/Video classification , Natural Language)
- Get an internship
This will take 5 - 6 months.
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
$endgroup$
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
add a comment |
$begingroup$
I think you already know enough applied mathematics to begin with. You can pick-up rest of it as required.
One option is :
- Start with an online course that provides high level overview of machine learning and types of algorithms (E.g.: https://www.coursera.org/learn/machine-learning)
- Start applying the knowledge in real world problems as soon as possible.
- Learn various types of neural networks (deeplearning.ai is one place to start)
- Apply knowledge to real world problems (Such as Audio/Video classification , Natural Language)
- Get an internship
This will take 5 - 6 months.
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
$endgroup$
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
add a comment |
$begingroup$
I think you already know enough applied mathematics to begin with. You can pick-up rest of it as required.
One option is :
- Start with an online course that provides high level overview of machine learning and types of algorithms (E.g.: https://www.coursera.org/learn/machine-learning)
- Start applying the knowledge in real world problems as soon as possible.
- Learn various types of neural networks (deeplearning.ai is one place to start)
- Apply knowledge to real world problems (Such as Audio/Video classification , Natural Language)
- Get an internship
This will take 5 - 6 months.
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
$endgroup$
I think you already know enough applied mathematics to begin with. You can pick-up rest of it as required.
One option is :
- Start with an online course that provides high level overview of machine learning and types of algorithms (E.g.: https://www.coursera.org/learn/machine-learning)
- Start applying the knowledge in real world problems as soon as possible.
- Learn various types of neural networks (deeplearning.ai is one place to start)
- Apply knowledge to real world problems (Such as Audio/Video classification , Natural Language)
- Get an internship
This will take 5 - 6 months.
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
answered Mar 31 at 15:57
Shamit VermaShamit Verma
1,5941314
1,5941314
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
add a comment |
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
$begingroup$
Thanks, I've just enrolled in some of those courses.
$endgroup$
– electroscience
Apr 1 at 7:25
add a comment |
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$begingroup$
datascience.stackexchange.com/questions/47854/… give this a check for more courses. You have a strong mathematical background and should be ready is few months. Also you have strong signal processing background that can be really useful for computer vision and audio processing
$endgroup$
– Pedro Henrique Monforte
Apr 1 at 12:36