How do I Learn Data Science?
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I am a Web Developer, looking for a career switch from being a web developer to a data scientist. Looked for various online and offline courses, didn't find an appropriate flow of studying or resources. Can anyone guide me on how to make this shift in career path?
machine-learning career
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$begingroup$
I am a Web Developer, looking for a career switch from being a web developer to a data scientist. Looked for various online and offline courses, didn't find an appropriate flow of studying or resources. Can anyone guide me on how to make this shift in career path?
machine-learning career
New contributor
$endgroup$
add a comment |
$begingroup$
I am a Web Developer, looking for a career switch from being a web developer to a data scientist. Looked for various online and offline courses, didn't find an appropriate flow of studying or resources. Can anyone guide me on how to make this shift in career path?
machine-learning career
New contributor
$endgroup$
I am a Web Developer, looking for a career switch from being a web developer to a data scientist. Looked for various online and offline courses, didn't find an appropriate flow of studying or resources. Can anyone guide me on how to make this shift in career path?
machine-learning career
machine-learning career
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New contributor
edited 2 mins ago
Ethan
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asked 11 hours ago
G Satish KumarG Satish Kumar
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2 Answers
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- Build a foundation in the basic Mathematical/Statistical concepts that you will need to further understand the algorithms/topics that you are working with.
This includes: Calculus, Linear Algebra, Probability/Statistics (Some form of optimization my also be helpful for conceptual understanding)
Learn the code. Become familiar with Python (or R) and become proficient in the Python packages Pandas, Numpy, and Matplotlib
Become familiar with ML concepts conceptually. Go through Andrew Ng's Intro to Machine Learning Course (available for free here on Coursea).
Begin to implement ML algorithms with code. Become proficient in the Python package scikit-learn.
Once you are comfortable with these concepts move to more advanced topics like Neural Networks and Deep Learning. Learn packages like Keras and Tensorflow
(Note: This is just a basic workflow. Depending on what Data Science you hope to do may require additional skills. Production level systems leveraging big data may require additional knowledge of things like Hadoop or Spark etc.)
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add a comment |
$begingroup$
Go for AnalyticVidhya, Kaggle websites for Data sets. They have got the full blogs for the begineers.
Learn basic statistics, learn graph visualization.
Learn at least Python or R.
Watching a video of an hour might not help but you need to practice it a lot more.
Best of luck
New contributor
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2 Answers
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active
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votes
2 Answers
2
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votes
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votes
$begingroup$
- Build a foundation in the basic Mathematical/Statistical concepts that you will need to further understand the algorithms/topics that you are working with.
This includes: Calculus, Linear Algebra, Probability/Statistics (Some form of optimization my also be helpful for conceptual understanding)
Learn the code. Become familiar with Python (or R) and become proficient in the Python packages Pandas, Numpy, and Matplotlib
Become familiar with ML concepts conceptually. Go through Andrew Ng's Intro to Machine Learning Course (available for free here on Coursea).
Begin to implement ML algorithms with code. Become proficient in the Python package scikit-learn.
Once you are comfortable with these concepts move to more advanced topics like Neural Networks and Deep Learning. Learn packages like Keras and Tensorflow
(Note: This is just a basic workflow. Depending on what Data Science you hope to do may require additional skills. Production level systems leveraging big data may require additional knowledge of things like Hadoop or Spark etc.)
$endgroup$
add a comment |
$begingroup$
- Build a foundation in the basic Mathematical/Statistical concepts that you will need to further understand the algorithms/topics that you are working with.
This includes: Calculus, Linear Algebra, Probability/Statistics (Some form of optimization my also be helpful for conceptual understanding)
Learn the code. Become familiar with Python (or R) and become proficient in the Python packages Pandas, Numpy, and Matplotlib
Become familiar with ML concepts conceptually. Go through Andrew Ng's Intro to Machine Learning Course (available for free here on Coursea).
Begin to implement ML algorithms with code. Become proficient in the Python package scikit-learn.
Once you are comfortable with these concepts move to more advanced topics like Neural Networks and Deep Learning. Learn packages like Keras and Tensorflow
(Note: This is just a basic workflow. Depending on what Data Science you hope to do may require additional skills. Production level systems leveraging big data may require additional knowledge of things like Hadoop or Spark etc.)
$endgroup$
add a comment |
$begingroup$
- Build a foundation in the basic Mathematical/Statistical concepts that you will need to further understand the algorithms/topics that you are working with.
This includes: Calculus, Linear Algebra, Probability/Statistics (Some form of optimization my also be helpful for conceptual understanding)
Learn the code. Become familiar with Python (or R) and become proficient in the Python packages Pandas, Numpy, and Matplotlib
Become familiar with ML concepts conceptually. Go through Andrew Ng's Intro to Machine Learning Course (available for free here on Coursea).
Begin to implement ML algorithms with code. Become proficient in the Python package scikit-learn.
Once you are comfortable with these concepts move to more advanced topics like Neural Networks and Deep Learning. Learn packages like Keras and Tensorflow
(Note: This is just a basic workflow. Depending on what Data Science you hope to do may require additional skills. Production level systems leveraging big data may require additional knowledge of things like Hadoop or Spark etc.)
$endgroup$
- Build a foundation in the basic Mathematical/Statistical concepts that you will need to further understand the algorithms/topics that you are working with.
This includes: Calculus, Linear Algebra, Probability/Statistics (Some form of optimization my also be helpful for conceptual understanding)
Learn the code. Become familiar with Python (or R) and become proficient in the Python packages Pandas, Numpy, and Matplotlib
Become familiar with ML concepts conceptually. Go through Andrew Ng's Intro to Machine Learning Course (available for free here on Coursea).
Begin to implement ML algorithms with code. Become proficient in the Python package scikit-learn.
Once you are comfortable with these concepts move to more advanced topics like Neural Networks and Deep Learning. Learn packages like Keras and Tensorflow
(Note: This is just a basic workflow. Depending on what Data Science you hope to do may require additional skills. Production level systems leveraging big data may require additional knowledge of things like Hadoop or Spark etc.)
edited 8 hours ago
answered 8 hours ago
EthanEthan
470220
470220
add a comment |
add a comment |
$begingroup$
Go for AnalyticVidhya, Kaggle websites for Data sets. They have got the full blogs for the begineers.
Learn basic statistics, learn graph visualization.
Learn at least Python or R.
Watching a video of an hour might not help but you need to practice it a lot more.
Best of luck
New contributor
$endgroup$
add a comment |
$begingroup$
Go for AnalyticVidhya, Kaggle websites for Data sets. They have got the full blogs for the begineers.
Learn basic statistics, learn graph visualization.
Learn at least Python or R.
Watching a video of an hour might not help but you need to practice it a lot more.
Best of luck
New contributor
$endgroup$
add a comment |
$begingroup$
Go for AnalyticVidhya, Kaggle websites for Data sets. They have got the full blogs for the begineers.
Learn basic statistics, learn graph visualization.
Learn at least Python or R.
Watching a video of an hour might not help but you need to practice it a lot more.
Best of luck
New contributor
$endgroup$
Go for AnalyticVidhya, Kaggle websites for Data sets. They have got the full blogs for the begineers.
Learn basic statistics, learn graph visualization.
Learn at least Python or R.
Watching a video of an hour might not help but you need to practice it a lot more.
Best of luck
New contributor
New contributor
answered 11 hours ago
BbkBbk
1
1
New contributor
New contributor
add a comment |
add a comment |
G Satish Kumar is a new contributor. Be nice, and check out our Code of Conduct.
G Satish Kumar is a new contributor. Be nice, and check out our Code of Conduct.
G Satish Kumar is a new contributor. Be nice, and check out our Code of Conduct.
G Satish Kumar is a new contributor. Be nice, and check out our Code of Conduct.
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