So, youβve decided you want to be #datascientist.
If youβre like me, navigating the #datascience landscape of different terminology, courses, and misconceptions about the discipline, itself, can be overwhelming. When you get distracted or worried about trying to keep up with everyone else, it becomes even more daunting.
We all come from unique backgrounds and are in different places on our #datasciencejourney. If you find yourself getting frustrated, pause, take a deep breath, and:
1. Level-set on your goals. Remind yourself what you really want to accomplish and / or where you hope to be in the future.
2. Take stock of where you are at right now and where you have been. Determine the gaps, and come up with a plan to fill them.
3. Stop worrying about how many / what types of certifications, degrees, courses, skills, and / or jobs everyone else has. Focus on you!
4. Remember life is a marathon, NOT a sprint. Celebrate quick-wins, but be patient with yourself. Enjoy the journey.
As a natural competitor, this has been one of the hardest lessons Iβve had to learn, and I still struggle.
What other advice would you give to those that are new to #datascience and / or people who have had some experience with data, but want to make the leap to #datascientist?
I'd like to add 'Specify where you want to go'.
There are so many different 'data scientists' from data analyst to deep learning engineers. So Be more specific in what field you want to go in, not being a generalist. Highly recommend this article by Jeremie Harris
https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c
If you like our channel, i invite you to share it with your friends
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
If youβre like me, navigating the #datascience landscape of different terminology, courses, and misconceptions about the discipline, itself, can be overwhelming. When you get distracted or worried about trying to keep up with everyone else, it becomes even more daunting.
We all come from unique backgrounds and are in different places on our #datasciencejourney. If you find yourself getting frustrated, pause, take a deep breath, and:
1. Level-set on your goals. Remind yourself what you really want to accomplish and / or where you hope to be in the future.
2. Take stock of where you are at right now and where you have been. Determine the gaps, and come up with a plan to fill them.
3. Stop worrying about how many / what types of certifications, degrees, courses, skills, and / or jobs everyone else has. Focus on you!
4. Remember life is a marathon, NOT a sprint. Celebrate quick-wins, but be patient with yourself. Enjoy the journey.
As a natural competitor, this has been one of the hardest lessons Iβve had to learn, and I still struggle.
What other advice would you give to those that are new to #datascience and / or people who have had some experience with data, but want to make the leap to #datascientist?
I'd like to add 'Specify where you want to go'.
There are so many different 'data scientists' from data analyst to deep learning engineers. So Be more specific in what field you want to go in, not being a generalist. Highly recommend this article by Jeremie Harris
https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c
If you like our channel, i invite you to share it with your friends
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
January 5, 2019
How to fail the Data Science business case
2. Recruiting as quick-fixββββI am looking to recruit 150 Data Scientists in the next 12 monthsβ. I am not kidding: I did get that phone call, and it was about recruiting βData-Scientists-as-consultantsβ. Yes, expertise matters. Yes, there is a shortage of talent. And, yes, as companies struggle to build up data science capabilities they likely will be keen on consultancy services. However, the shortage of experts is real. Moreover, a senior data scientist likely prefers building products over project work, and impact with customers over project management meetings. Overall, I have seen quite a few attempts at using recruiting-as-a-fix, often failing at implementation already, either because of an unrealistic βunicornβ wishlist or because the case couldnβt be made as to why an experienced Data Scientists should join the company. Moreover, Data Scientists frequently report that they are interviewed by non-experts.
#interviews #datascientist #recruiting #machinelearning
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
2. Recruiting as quick-fixββββI am looking to recruit 150 Data Scientists in the next 12 monthsβ. I am not kidding: I did get that phone call, and it was about recruiting βData-Scientists-as-consultantsβ. Yes, expertise matters. Yes, there is a shortage of talent. And, yes, as companies struggle to build up data science capabilities they likely will be keen on consultancy services. However, the shortage of experts is real. Moreover, a senior data scientist likely prefers building products over project work, and impact with customers over project management meetings. Overall, I have seen quite a few attempts at using recruiting-as-a-fix, often failing at implementation already, either because of an unrealistic βunicornβ wishlist or because the case couldnβt be made as to why an experienced Data Scientists should join the company. Moreover, Data Scientists frequently report that they are interviewed by non-experts.
#interviews #datascientist #recruiting #machinelearning
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
January 19, 2019
DASK CHEATSHEET - FOR PARALLEL COMPUTING IN DATA SCIENCE
You will need Dask when the data is too big
This is the guide from Analytics Vidhya https://lnkd.in/fKVBFhE
#datascience #pydata #pandas
#datascientist
βοΈ @AI_Python
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
You will need Dask when the data is too big
This is the guide from Analytics Vidhya https://lnkd.in/fKVBFhE
#datascience #pydata #pandas
#datascientist
βοΈ @AI_Python
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
February 2, 2019
Presenting 4 more true stories of professionals in our community transitioning into #DataScience from a variety of backgrounds:
How I became a Data Science Hacker from being a Delivery Head - https://lnkd.in/faQDP2p
How I became a Data Science #Analyst from a Software Developer - https://lnkd.in/fYueNbn
Becoming a #DataScientist after 8 Years as a Software Test Engineer - https://lnkd.in/fjihReg
I became a Data Scientist after working for 10 years in IT Industry - https://lnkd.in/fibY7iB
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
How I became a Data Science Hacker from being a Delivery Head - https://lnkd.in/faQDP2p
How I became a Data Science #Analyst from a Software Developer - https://lnkd.in/fYueNbn
Becoming a #DataScientist after 8 Years as a Software Test Engineer - https://lnkd.in/fjihReg
I became a Data Scientist after working for 10 years in IT Industry - https://lnkd.in/fibY7iB
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
February 13, 2019
Google Doubles Down On Spammers With #TensorFlow. #BigData #Analytics #MachineLearning #DataScience #AI #NLProc #IoT #IIoT #PyTorch #Python #RStats #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #TransferLearning
π http://bit.ly/2U4ZSAf
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
π http://bit.ly/2U4ZSAf
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
February 24, 2019
What skills should you start learning to become a data scientist?
I've had a lot of people ask me that recently, but it's really the wrong question.
Try this instead -
β‘οΈ 1. Choose a problem (and dataset) that you find interesting
β‘οΈ 2. Begin trying to solve the problem
β‘οΈ 3. When you get stuck because your skill set is limited, go learn that skill
For example, if you get stuck...
- loading the data into a dataframe/table, then learn pandas or SQL
- identifying outliers, then study stats
- figuring out what to do with missing data, then learn when to remove data, replace values, impute values, etc
- building a regression model that makes good predictions, then learn about regression techniques
π There are 2 big differences when you take this approach:
1. You don't waste time guessing what you need to know
2. You're highly motivated to learn at every step of the way
This eliminates the scenario where you're learning a subject because someone else told you it's a good idea.
π Now you're learning because you NEED that knowledge for something useful.
Start using this approach in your studies and you'll also see that you're learning more quickly and completely.
#datascience #aspiring #datascientist
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
I've had a lot of people ask me that recently, but it's really the wrong question.
Try this instead -
β‘οΈ 1. Choose a problem (and dataset) that you find interesting
β‘οΈ 2. Begin trying to solve the problem
β‘οΈ 3. When you get stuck because your skill set is limited, go learn that skill
For example, if you get stuck...
- loading the data into a dataframe/table, then learn pandas or SQL
- identifying outliers, then study stats
- figuring out what to do with missing data, then learn when to remove data, replace values, impute values, etc
- building a regression model that makes good predictions, then learn about regression techniques
π There are 2 big differences when you take this approach:
1. You don't waste time guessing what you need to know
2. You're highly motivated to learn at every step of the way
This eliminates the scenario where you're learning a subject because someone else told you it's a good idea.
π Now you're learning because you NEED that knowledge for something useful.
Start using this approach in your studies and you'll also see that you're learning more quickly and completely.
#datascience #aspiring #datascientist
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
February 27, 2019
βIf you only read the books that everyone else is reading, you can only think what everyone else is thinking.β
Every person has their own way of learning. What helped me break into data science was books. There is nothing like opening your mind to a world of knowledge condensed into a few hundred pages. There is a magic and allure to books that I have never found in any other medium of learning.
There are hundreds of books out there about data science. How do you choose where to start? Which books are ideal for learning a certain technique or domain? While thereβs no one-shoe-fits-all answer to this, I have done my best to cut down the list to these 27 books weβll see shortly.
I have divided the books into different domains to make things easier for you:
1. Books on Statistics
2. Books on Probability
3. Books on Machine Learning
4. Books on Deep Learning
5. Books on Natural Language Processing (NLP)
6. Books on Computer Vision
7. Books on Artificial Intelligence
8. Books on Tools/Languages
- Python
- R
Link : https://bit.ly/2IOPV8T
#python #books #artificialintelligence #datascience
#machinelearning #statistics #datascientist #deeplearning
β΄οΈ @AI_Python_EN
Every person has their own way of learning. What helped me break into data science was books. There is nothing like opening your mind to a world of knowledge condensed into a few hundred pages. There is a magic and allure to books that I have never found in any other medium of learning.
There are hundreds of books out there about data science. How do you choose where to start? Which books are ideal for learning a certain technique or domain? While thereβs no one-shoe-fits-all answer to this, I have done my best to cut down the list to these 27 books weβll see shortly.
I have divided the books into different domains to make things easier for you:
1. Books on Statistics
2. Books on Probability
3. Books on Machine Learning
4. Books on Deep Learning
5. Books on Natural Language Processing (NLP)
6. Books on Computer Vision
7. Books on Artificial Intelligence
8. Books on Tools/Languages
- Python
- R
Link : https://bit.ly/2IOPV8T
#python #books #artificialintelligence #datascience
#machinelearning #statistics #datascientist #deeplearning
β΄οΈ @AI_Python_EN
March 17, 2019
How This Researcher Is Using #DeepLearning To Shut Down Trolls And Fake Reviews. #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #JavaScript #ReactJS #GoLang #Serverless #DataScientist #Linux
π https://bit.ly/2U2J5BX
β΄οΈ @AI_Python_EN
π https://bit.ly/2U2J5BX
β΄οΈ @AI_Python_EN
March 29, 2019
I wanna be a data scientist, but⦠how!?
https://link.medium.com/CUDoPvMOEV
#DataScience #ArtificialIntelligence #MachineLearning #DeepLearning #DataScientist
β΄οΈ @AI_Python_EN
https://link.medium.com/CUDoPvMOEV
#DataScience #ArtificialIntelligence #MachineLearning #DeepLearning #DataScientist
β΄οΈ @AI_Python_EN
April 17, 2019
The ability to deal with imbalanced datasets is a must-have for any #datascientist. Here are 4 tutorials to learn the different techniques of handling imbalanced data:
How to handle Imbalanced #Classification Problems in #MachineLearning? - https://buff.ly/2sIsR0M
Investigation on Handling Structured & Imbalanced Datasets with #DeepLearning - https://buff.ly/2MpxuG1
This Machine Learning Project on Imbalanced Data Can Add Value to Your #DataScience #Resume - https://buff.ly/2Mpr2i0
Practical Guide to deal with Imbalanced Classification Problems in #R - https://buff.ly/2MrS8Fr
β΄οΈ @AI_Python_EN
How to handle Imbalanced #Classification Problems in #MachineLearning? - https://buff.ly/2sIsR0M
Investigation on Handling Structured & Imbalanced Datasets with #DeepLearning - https://buff.ly/2MpxuG1
This Machine Learning Project on Imbalanced Data Can Add Value to Your #DataScience #Resume - https://buff.ly/2Mpr2i0
Practical Guide to deal with Imbalanced Classification Problems in #R - https://buff.ly/2MrS8Fr
β΄οΈ @AI_Python_EN
April 25, 2019
DeepAMD: Detect Early Age-Related Macular Degeneration.
#BigData #Analytics #DataScience #AI #MachineLearning #DeepLearning #IoT #IIoT #PyTorch #Python #CloudComputing #DataScientist #Linux
https://link.springer.com/chapter/10.1007%2F978-3-030-20873-8_40
β΄οΈ @AI_Python_EN
#BigData #Analytics #DataScience #AI #MachineLearning #DeepLearning #IoT #IIoT #PyTorch #Python #CloudComputing #DataScientist #Linux
https://link.springer.com/chapter/10.1007%2F978-3-030-20873-8_40
β΄οΈ @AI_Python_EN
May 26, 2019
One of the BEST #MachineLearning Glossary by Google
It will definitely come in handy - https://lnkd.in/gNiE9JT
Link to learn more about Machine Learning:
β Course 1 : A comprehensive Learning Path to become Data Scientist in 2019
Link : https://bit.ly/2HOthei
β Course 2 : Experiments with Data
Link : https://bit.ly/2HQuQbw
β Course 3 : Python for Data Science
Link : https://bit.ly/2HOG5RG
β Course 4 : Twitter Sentiments Analysis
Link : https://bit.ly/2HR8O8A
β Course 5 : Creating Time Series Forecast with Python
Link : https://bit.ly/2XniU6r
β Course 6 : A comprehensive path for learning Deep Learning in 2019
Link : https://bit.ly/2HO1VVJ
β Course 7 : Loan Prediction Practice problem
Link : https://bit.ly/2IcynQl
β Course 8 : Big mart Sales Problem using R
Link : https://bit.ly/2JUlZIb
#announcements #datascientist #machinelearning #datascience #artificialintelligence
β΄οΈ @AI_Python_EN
It will definitely come in handy - https://lnkd.in/gNiE9JT
Link to learn more about Machine Learning:
β Course 1 : A comprehensive Learning Path to become Data Scientist in 2019
Link : https://bit.ly/2HOthei
β Course 2 : Experiments with Data
Link : https://bit.ly/2HQuQbw
β Course 3 : Python for Data Science
Link : https://bit.ly/2HOG5RG
β Course 4 : Twitter Sentiments Analysis
Link : https://bit.ly/2HR8O8A
β Course 5 : Creating Time Series Forecast with Python
Link : https://bit.ly/2XniU6r
β Course 6 : A comprehensive path for learning Deep Learning in 2019
Link : https://bit.ly/2HO1VVJ
β Course 7 : Loan Prediction Practice problem
Link : https://bit.ly/2IcynQl
β Course 8 : Big mart Sales Problem using R
Link : https://bit.ly/2JUlZIb
#announcements #datascientist #machinelearning #datascience #artificialintelligence
β΄οΈ @AI_Python_EN
June 21, 2019
Getting System Information in Linux using Python Script.
#BigData #Analytics #DataScience #IoT #PyTorch #Python #RStats #TensorFlow #DataScientist #Linux
http://bit.ly/2X56cZa
β΄οΈ @AI_Python_EN
#BigData #Analytics #DataScience #IoT #PyTorch #Python #RStats #TensorFlow #DataScientist #Linux
http://bit.ly/2X56cZa
β΄οΈ @AI_Python_EN
June 29, 2019
Here is an exclusive video that highlights the 5 things that any aspirant should consider before choosing a Machine Learning course. Watch the full video here:
https://lnkd.in/fVyW5Uq
#machinelearning #artificialintelligence #datascience #deeplearning #datascientist #ai
β΄οΈ @AI_Python_EN
https://lnkd.in/fVyW5Uq
#machinelearning #artificialintelligence #datascience #deeplearning #datascientist #ai
β΄οΈ @AI_Python_EN
July 1, 2019
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Pytorch Implementation of Deep Flow.
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #DataScientist #Linux
https://arxiv.org/pdf/1905.02884.pdf
β΄οΈ @AI_Python_EN
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #DataScientist #Linux
https://arxiv.org/pdf/1905.02884.pdf
β΄οΈ @AI_Python_EN
July 17, 2019
ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....
PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.
It allows you to go from preparing data to deploying a model within SECONDS.
PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:
- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!
Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.
The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.
To learn more about PyCaret, please visit the official website https://www.pycaret.org
#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret
βοΈ @AI_Python_EN
PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.
It allows you to go from preparing data to deploying a model within SECONDS.
PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:
- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!
Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.
The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.
To learn more about PyCaret, please visit the official website https://www.pycaret.org
#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret
βοΈ @AI_Python_EN
April 9, 2020