So, you’ve decided you want to be #datascientist.
If you’re 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 I’ve 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
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
If you’re 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 I’ve 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
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
How to fail the Data Science business case
2. Recruiting as quick-fix — “I am looking to recruit 150 Data Scientists in the next 12 months”. I am not kidding: I did get that phone call, and it was about recruiting ‘Data-Scientists-as-consultants’. 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 ‘unicorn’ wishlist or because the case couldn’t 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
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
2. Recruiting as quick-fix — “I am looking to recruit 150 Data Scientists in the next 12 months”. I am not kidding: I did get that phone call, and it was about recruiting ‘Data-Scientists-as-consultants’. 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 ‘unicorn’ wishlist or because the case couldn’t 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
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN
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
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @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
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
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
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @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
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
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
🌎 http://bit.ly/2U4ZSAf
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
🌎 http://bit.ly/2U4ZSAf
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
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 -
➡️ 1. Choose a problem (and dataset) that you find interesting
➡️ 2. Begin trying to solve the problem
➡️ 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
👉 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.
👉 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
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
I've had a lot of people ask me that recently, but it's really the wrong question.
Try this instead -
➡️ 1. Choose a problem (and dataset) that you find interesting
➡️ 2. Begin trying to solve the problem
➡️ 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
👉 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.
👉 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
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
“If you only read the books that everyone else is reading, you can only think what everyone else is thinking.”
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 there’s no one-shoe-fits-all answer to this, I have done my best to cut down the list to these 27 books we’ll 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
✴️ @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 there’s no one-shoe-fits-all answer to this, I have done my best to cut down the list to these 27 books we’ll 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
✴️ @AI_Python_EN
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
🌎 https://bit.ly/2U2J5BX
✴️ @AI_Python_EN
🌎 https://bit.ly/2U2J5BX
✴️ @AI_Python_EN
I wanna be a data scientist, but… how!?
https://link.medium.com/CUDoPvMOEV
#DataScience #ArtificialIntelligence #MachineLearning #DeepLearning #DataScientist
✴️ @AI_Python_EN
https://link.medium.com/CUDoPvMOEV
#DataScience #ArtificialIntelligence #MachineLearning #DeepLearning #DataScientist
✴️ @AI_Python_EN