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Become a Data Scientist: The Journey through Data Science and AI

Become a Data Scientist: The Journey through Data Science and AI

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I became aware of Data Science and Data Science opportunities as far as five years ago. At that time there was a lot of hype about self driving cars, speech to text automated translation, fraud detection and so on. In the meantime I don’t get that much hyped about what is possible, since theoretically everything is possible once there is the data available, but more on which are the sources of data that are currently being exploited and which career paths are foreseeable in the nearby future.

Don't get me wrong. Is not that I'm no longer excited about the big data projects, but I’m rather interested in the low hanging fruit. Data Science opportunities for the few of us that will not be working for the likes of Facebook, Google and Co. with big research budgets and massive data collection capabilities. Which companies should I start following, also noteworthy conferences to attend in order to hang out with other Data Science practioners.

Final question is perhaps also in order. How much is expected to ask a data science practitioner for his first job, to be consider within the roaster. This both for people fresh coming from university and people switching careers/jobs?

Kind regards,
Omar

Hi @Omar VM

On top of what Carmen has responded I should add that I believe there are more and more use cases that are being tackled with data science everyday. There are some good courses that specifically talk about data science use cases in business on coursera, there is also a good book called "Data Science for Business" by Foster Provost and to start attending conferences and workshops, you can start by attending this free online webinar: https://www.thisismetis.com/demystifying-data-science. There are also many free Meetups that you can search for in the region you live and get involved in community. Usually when you attend couple of these events you will automatically get engaged in community and will learn about upcoming conferences, webinars,... also if you are interested to learn about startups in the area and what ideas they are working on, there are usually "start-up pitch" events, that the founders get to attend and present their ideas as well as some meetups.

Regarding your question about what is expected form a data science practitioner, it really depends on what level of job you are applying to... but I believe even for a junior role the knowledge of at least one programming language, understanding the basic concepts and ML models as well as having done some hands-on projects even on your laptop with some open source datasets that are publicly available are mandatory.
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Good morning everyone,

thanks for this Q&A opportunity. My name is Michael from Germany and my background is a Master degree in Business Information Systems. I have more than 10 years experience in (SAP) Finance/ Controlling processes, some years in Business Intelligence and also worked with IBM Watson (Artificial Intelligence).
I want to expand my knowledge to machine learning/ AI incl. Python progamming as well. As a commuter, I want to use my time to learn Python during my daily trips but it is not allowed to install any software environment for Python locally on the company notebook. Are there any web-based environments where I can code to learn Python (for free)? I didn't find any appropriate solution in the past.

Many thanks for suggestions in advance,

Michael

@MichaelM you can start by creating a free trial account at https://dataplatform.cloud.ibm.com/ then you will have access to jupyter notebooks, and many notebooks for different use cases that you can run and edit to learn from in the community tab. There is also an online IDE if you only want to write and run some python code to try it out: https://repl.it/
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There have been some questions around the webinars or training material, there are two upcoming events online that you can attend and learn for free: https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&referrer=https%3A%2F%2Fmail.notes.na.collabserv.com%2Fverse%3F&eventid=2034787&sessionid=1&key=F9B4017AFE285363E2DACB4A5EB74DA2®Tag=582091&sourcepage=register?partnerref=eventalert?cm_mmc=OSocial_Twitter-_-Data+Science_Data+Science-_-WW_WW-_-Newsletter&cm_mmca1=000024OR&cm_mmca2=10006046&

https://www.thisismetis.com/demystifying-data-science. There are many interesting talk in this two day virtual conference for example the following talk is about Deep learning:
Deep Learning for All by Gabriela de Queiroz from IBM @12 pm on 31 July
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LEGAL QUESTION: ibm badge system in data science

are they professional licenses?

https://www.ibm.com/developerworks/community/wikis/home?lang=en#!/wiki/W8877c01e0b3f_4ed5_9fa5_b8268c46f433/page/IBM%20Integrated%20Analytics%20System%20-%202018

i was planning to earn all of them.... If they are not even deemed as a professional license in the field why bothering?

Furthermore....

Am I a Data Scientist?

https://www.youracclaim.com/users/ricardo-a-vanegas/badges?sort=-state_updated_at&;page=1



Please also check out this link for IBM Professional certificates: https://www.ibm.com/certify/index
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Hello everyone, thanks to Coursera Community team and to IBM data science team for being with us here.

I'm Mo Rebaie from Lebanon, my educational background is in mathematics, and I'm have been learning ML and DL for more than 3 years, I'm currently working with Deep Learning for prototypes across industries.

My questions:

1- Do you recommend me to continue my academic learning and master Machine Learning in the university, or I should just rely on my Mathematics degree plus certifications from online courses in ML and DL?

2- I would like to know your thoughts about the intersection between AI and ethics, I read a lot about data acquisition, but I think that getting a high-quality data is more important than just collecting data, should it be an important thing to take in consideration?

Thank you!
Hi - thank you for your question.
  • Coursera offers great courses in Data Analysis, Machine Learning, Probability and Statistics - some free and some for a small fee. Browse their offerings and am sure you will find what you are looking for to continue your education if you choose to forego acquiring more college degrees.
  • As for ethics, bad practices in machine learning and AI won't slip the scrutiny of serious and responsible practitioners, and that includes collecting high-quality data.



Hi @Mo Rebaie in regarding to ethics and Regulation using Datascience and how unintended bias in data or models can effect our decision you can refer to my previous webinar on this topic: https://coursera.community/data-science-8/building-operationalizing-monitoring-ai-workloads-with-trust-transparency-4692 and a blog I've written on the same topic: https://community.ibm.com/community/user/datascience/blogs/sepideh-seifzadeh1/2019/05/29/trust-and-transparancy-in-ml-lifecycles?CommunityKey=f1c2cf2b-28bf-4b68-8570-b239473dcbbc&;tab=recentcommunityblogsdashboard I see IBM as a leader to tackle this problem with by developing and open sourcing a tool called "AI Fainess 360" which you can find the links to its community and Github page in my previous seminar as well as a production ready tool which is a more advanced version of "AI Fairness 360" and is called "IBM Open Scale" which also includes Model explainability for models to explain why they have come to this decision and which features contributed the most, ...
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Thank you @cgstefanita for your helpful answers!

I always try to collect high-quality data, AI with ethical data transform our life to a better future!

I just registered with IBM to view the events.
Hi,

Glad to hear and thank you for the kind words 😉 But events are no substitute for taking courses and/or getting certificates. So, please consider getting those. While you are at it - you may want to sign up for this:Good luck on your journey and hope you enjoy it 😉

Hello @cgstefanita, thank you for following up, I took the ML course, DL specialization, and the Tensorflow specialization, but I didn't enroll in the DS specialization by IBM, so I'll start with it by the next 2 days, I checked the 9 courses in the specialization, it sounds interesting!
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Hello everyone, thanks to Coursera Community team and to IBM data science team for being with us here.

I'm Mo Rebaie from Lebanon, my educational background is in mathematics, and I'm have been learning ML and DL for more than 3 years, I'm currently working with Deep Learning for prototypes across industries.

My questions:

1- Do you recommend me to continue my academic learning and master Machine Learning in the university, or I should just rely on my Mathematics degree plus certifications from online courses in ML and DL?

2- I would like to know your thoughts about the intersection between AI and ethics, I read a lot about data acquisition, but I think that getting a high-quality data is more important than just collecting data, should it be an important thing to take in consideration?

Thank you!
Hi - thank you for your question.
  • Coursera offers great courses in Data Analysis, Machine Learning, Probability and Statistics - some free and some for a small fee. Browse their offerings and am sure you will find what you are looking for to continue your education if you choose to forego acquiring more college degrees.
  • As for ethics, bad practices in machine learning and AI won't slip the scrutiny of serious and responsible practitioners, and that includes collecting high-quality data.

Hi @Mo Rebaie in regarding to ethics and Regulation using Datascience and how unintended bias in data or models can effect our decision you can refer to my previous webinar on this topic: https://coursera.community/data-science-8/building-operationalizing-monitoring-ai-workloads-with-trust-transparency-4692 and a blog I've written on the same topic: https://community.ibm.com/community/user/datascience/blogs/sepideh-seifzadeh1/2019/05/29/trust-and-transparancy-in-ml-lifecycles?CommunityKey=f1c2cf2b-28bf-4b68-8570-b239473dcbbc&;tab=recentcommunityblogsdashboard I see IBM as a leader to tackle this problem with by developing and open sourcing a tool called "AI Fainess 360" which you can find the links to its community and Github page in my previous seminar as well as a production ready tool which is a more advanced version of "AI Fairness 360" and is called "IBM Open Scale" which also includes Model explainability for models to explain why they have come to this decision and which features contributed the most, ...

Thank you @Sepideh for sharing valuable resources, I'll check them, and for sure I'll read your blog! I'll start write blogs about ethical AI and let you know then 🙂
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I have one more question, can you @Sepideh , @cgstefanita , @andreviolante
kindly provide us with your LinkedIn and Twitter accounts to follow you?
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I have one more question, can you @Sepideh , @cgstefanita , @andreviolante
kindly provide us with your LinkedIn and Twitter accounts to follow you?


Hi @Mo Rebaie https://www.linkedin.com/in/sepiseif/
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I have one more question, can you @Sepideh , @cgstefanita , @andreviolante
kindly provide us with your LinkedIn and Twitter accounts to follow you?


Would love to connect with anyone thanks @Mo Rebaie!

https://www.linkedin.com/in/andreviolante/
Userlevel 3
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I have one more question, can you @Sepideh , @cgstefanita , @andreviolante

kindly provide us with your LinkedIn and Twitter accounts to follow you?
Would love to connect with anyone thanks @Mo Rebaie!

https://www.linkedin.com/in/andreviolante/
I have one more question, can you @Sepideh , @cgstefanita , @andreviolante

kindly provide us with your LinkedIn and Twitter accounts to follow you?
Would love to connect with anyone thanks @Mo Rebaie!

https://www.linkedin.com/in/andreviolante/
Userlevel 3
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www.linkedin.com/in/cgstefanita