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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
Laura
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Sepideh Seifzadeh, Carmen Stefanita, and Andre Violante of the IBM Data Science Elite team are taking your questions!

With the increased need of many industries to adopt data science and machine learning techniques in their daily business, one has to wonder what it takes for an individual to become a valuable resource and fill in the skill shortages we hear so much about.

Between now and July 21, reply to this post with your questions on how to build a successful data science and AI profile. Questions are welcome on the following topics:
  • How to seek out essential experiences to prepare you for this profession – including how to find a mentor, how to seek exposure to data, and how to participate in projects/work with others
  • How to expand your analytic tool kit: Python, SPSS, SAS, R, etc.
  • Expected techniques such as basic classification methods and data visualization tools
Questions will be answered during the week of 22 July.

About the Q&A Hosts

Sepideh Seifzadeh
Sepi is a Data Scientist/Machine Learning Engineer on the IBM Data Science Elite (DSE) team, based in San Francisco. After completing her PhD at the Center for Pattern Analysis and Machine Intelligence at University of Waterloo, she started her journey as a Big Data & Analytics consultant. Sepi joined IBM as an open source solution engineer, working for IBM Canada for 2 years where she was honored to be awarded “The Best of IBM” 2018. She really enjoys being on the DSE team and applying AI in real world applications to help customers on their data science journey. She enjoys working with DSE team members who are top talent in the field.

Sepi is a speaker at conferences, meetups and summits, and she is passionate about sharing recent trends in technology with everyone. She mostly uses open source tools in Watson Studio, with her recent passion being around detecting model bias using Watson Open Scale to ensure Machine Learning models provide fair decisions with trust and transparency.

In her free time, Sepi goes hiking, scuba diving, and biking. She has a passion for technology and always tries to keep herself up to date about the recent trends and innovations in the field.

Carmen-Gabriela Stefanita, PhD
Carmen has a background in physics and advanced mathematics spanning work on three continents in different countries. After her PhD in physics from Queen’s University in Ontario, Canada, she continued to work in academia before finding her way back to industry. Her academic projects covered areas of stochastic analysis in nondestructive testing, modeling and building of sensors and devices, as well as quantum computation. In her journey to data science, Carmen has built AI models in e-commerce, ad-tech and fintech. As a senior member of the Data & AI Elite team at IBM, Carmen continues to help customers develop machine learning solutions for real life applications with models in telecommunications and manufacturing.

Carmen is also an author, inventor and entrepreneur with a passion for finding innovative solutions for today’s AI strategy. In her free time, Carmen enjoys swimming and is a devoted world traveler. You can connect with her on LinkedIn www.linkedin.com/in/cgstefanita to continue the conversation.

Andre Violante
Andre is part of the IBM Data Science Elite team and supports client engagements that involve machine learning and artificial intelligence tasks.

Andre has a Master’s degree in analytics/data science and almost 10 years of digital analytics experience. He specializes in retail and consumer analytics with experience coming from companies like Zappos, Nike, and SAS. Andre has worked with several data platforms (Oracle, Hadoop, AWS) using a variety of open source tools, primarily R and Python. Andre enjoys building relationships and is very intellectually curious with a passion for solving real world business problems that make impact.

On his off time, Andre enjoys exercising, watching sports, and spending time with his family. He is a frequent walker of various environments (outside, trails, beaches, airports, malls, etc.) and tries to be as active as possible to overcome his uncontrollable sweet tooth.
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62 replies

XinruCheng
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Hi everyone! My name is Xinru (pronounced like "zin-roo?" with a rising tone), and I'm an aspiring data scientist in Vancouver. Like Carmen, I have a background in physics. Previously I earned a thesis-based MSc in experimental quantum optics, and taught physics for a while.

I have a few questions (and will likely have more after the talk! 🙂 ):
  • For people trying to switch careers into data science, what's the best way to find a mentor?
  • When transitioning out of Academia (especially with a liberal arts background, and little relevant industry experience), how should we effectively highlight our transferrable skills and soft skills? (such as critical thinking and asking good questions!)
  • When building a portfolio and applying for jobs, is it better to go deeper in one language (have lots of projects of varying difficulties in Python), or try to dabble in everything to show versatility? (relates to the "How to expand your analytic tool kit" topic)
  • Regarding the first Data Science job (if you could have a do-over in your own career path), is location more important than salary or job function? (What I mean is perhaps relocating to a big city with more startups would bring more opportunities, even if the first job is not exactly what you are looking for?)
  • Also about the first Data Science job, is a startup with a DS team (so you're not the only "expert" in the area) better than a large company for personal growth, or should one always go for the big names when given the chance?
Thanks for reading my long list of questions! I've realized I should probably organize them and write a blog post or interview someone.

Looking forward to the Q&A!

Cheers,
Xinru
Xinru Cheng
cgstefanita
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Hi Xinru - nice meeting you and thank you for your question. Will try to answer in order, but of course, my colleagues may want to add more:
  • "For people trying to switch careers into data science, what's the best way to find a mentor?"
Best way is to try to find local meetup groups in data science and get involved in those activities, then ask some of the thought leaders in that group to mentor you.
Alternatively, there are a variety of online projects in data science where you could contribute and increase your visibility so that people start working with you - then ask someone to mentor you.
  • "When transitioning out of Academia...etc"
Work with people while still in academia that can guide you on writing a resume - and then look for similar resources outside academia. Physicists have a lot of transferable skills to data science.
  • "When building a portfolio...etc"
You will need to show proficiency in more than one programming language. Python is in high demand, but not sufficient on its own - so try to learn SQL (and related languages), Scala etc.
  • "Regarding the first Data Science job...etc"
That depends on your other priorities too, aside from getting a job (e.g. relocating a family may not be straightforward). There are great internship opportunities at various geographical locations, both in big corporations or startups. Focus on developing your skills and gain experience as these carry weight in your resume building.
  • "Also about the first Data Science job, is a startup with a DS team...etc"
You may want to look for a job that has data scientists in your group, to learn from them, to be mentored by them and in general get access to data science support.
D
Just rambling here...I have been studying data analytics for 4.5 years so merely a novice. Part and parcel to this study was the discovery of some interesting data offered by Gartner Research. Besides a subtle look at ROI calcs, the article suggested there was a 60% failure rate in Big Data Projects. So, as a result, I have been gathering research for almost a year now trying to identify the genesis of this failure rate. I know...get to the point...so, while everyone clamors for the next new language, I have to ask why isn't business purpose or process taught more (CRISP-DM as up dated by IBM) ? Further, it has been noted that data wrangling represents~80% of a data projects which to me suggests more time should be spent in SQL (and the its variants). In terms of a question, I would appreciate any insight you have regarding my observations. See source: https://www.linkedin.com/pulse/250-roi-journey-predictive-analytics-jim-conaway/
R
Userlevel 1
Badge +1
Dear all:

1.)
I got a badge from IBM as DATA SCIENTIST:

https://www.youracclaim.com/badges/27b08fa0-5fb1-4bf1-a7ae-fb778c18d18d

is this equivalent to a bachelor degree in the field ? an associate degree or what?

Can I call myself data scientist for real?

2.)
Other than data scientist or data engineer... Is it compulsory to hold a degree or professional license in : Finance, Economics, Enginerings: Chemistry, Physics, Mechanics, Electric, Aircraft, Operations, Telecommunications, Genetics, electronic, robotics. Actuarial science... I mean: a second career or another major where to apply data models


Thank you very much for your answer....
grldsndrs
Userlevel 1
Badge
Hello. As a neophyte, I am wondering if anyone has any ideas on how to engage with small businesses? Businesses that appear to be able to benefit from data science and or machine learning services, but which have not necessarily, connected the Who, Why, When and How dots.

I began my career building and deploying instrumentation, then distributing the acquired data to principles. Now, I am becoming proficient at data analysis and modeling. Together with my background in engineering and physics, my unique skill set affords me a perspective encompassing the entire data-driven analytics process, from sampling signals that measure real world physical phenomenon; to the generation of actionable insights informed by algorithms and statistical models.

From my point of view, I see opportunities everywhere. What is not as clear, is how to capitalize on these.

Can anyone here advise me on offering mutually beneficial data services, to small businesses?
Derick
Userlevel 1
Badge +1
Hello everyone, first of all let me thank you. Please i have a question regarding to build a good resume as Data science and AI for job . What the most common skills should be included?
kindly if you can share with me an example of good resume.


Thank you in advance
O
Badge
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
E
Badge
Hi,

First of all, thank you all for this unique opportunity! I am a PhD student in civil engineering at UIUC where I have utilized machine-learning algorithms to solve real life problems. Although I have taken the same classes as the CS students take and have papers published on applied ML, I feel like there is still bias against my degree in job applications for data science. What can I do to overcome this bias?.

Best,
Erman
A
Badge
Hi everyone,

hope you all are doing great.

So, I'm currently positioned as a backend developer for almost a year in Bangalore, India in a renowned enterprise data storage solution company based in Palo Alto. Most of my work involves pure distributed system fundamentals and no ML applications/usage.

So, I had finished my Masters in Computer Science from one of top tier institutions in India before I landed in this job from campus job fair (which did not have much to do with preferences/choices). The primary area of my research/thesis was Machine Learning. Have published couple of papers [1|2].

For quite some time I had been actively trying to switch/get a (first) data science role which would be a domain switch given my current area of work. The steps I thought would be optimal is to 1) revise concepts from books (Bishop/ESL/GoodFellow) or various materials, 2) restart hands-on coding, 3) maybe participate in some Kaggle Competitions, 4) keep applying to various jobs/people.

The challenges that I faced were, 1) I'm fresh grad with merely a year of experience is much less than the requirements, 2) Given my current role/job, it basically translates to no Data Science industry experience, and it would be true until the first such job, 3) In many occasions the requirements for some role includes experience in various big data frameworks.

So, could you advise on
1) How should I appoach, to compensate for the fact that I have no industrial data science experience ? Would some projects that demonstrate broad skills suffice? Does challenges like Kaggle help in this regard.
2) Along with ML/NLP core application, is it advisable to start learning other frameworks like ( Hadoop, SparkML etc.) and languages. Currently I'm okay in python but not so much in R,SQL.

Thank you so much for this initiative.
cgstefanita
Rank
Userlevel 3
Badge
Dear all:

1.)
I got a badge from IBM as DATA SCIENTIST:

https://www.youracclaim.com/badges/27b08fa0-5fb1-4bf1-a7ae-fb778c18d18d

is this equivalent to a bachelor degree in the field ? an associate degree or what?

Can I call myself data scientist for real?

2.)
Other than data scientist or data engineer... Is it compulsory to hold a degree or professional license in : Finance, Economics, Enginerings: Chemistry, Physics, Mechanics, Electric, Aircraft, Operations, Telecommunications, Genetics, electronic, robotics. Actuarial science... I mean: a second career or another major where to apply data models


Thank you very much for your answer....

Hi - congratulations on your badge.
  • A badge is not the same thing as a degree. It is just a measure of a milestone. Your badge is saying that you have accomplished this goal: being able to "explain how the IBM Integrated Analytics System architecture and parallel processing capabilities support modelling and analysis paradigms on large scale data sets.". The badge cannot be substituted for a degree and it does not make one a data scientist.
  • Degrees are not "compulsory". They are a choice one makes in their professional career. One can become a data scientist without the degrees you mention above, however just like with many career choices, there is a rigorous certification path one has to be on and complete and often that means getting some academic degrees.
cgstefanita
Rank
Userlevel 3
Badge
Hi everyone,

hope you all are doing great.

So, I'm currently positioned as a backend developer for almost a year in Bangalore, India in a renowned enterprise data storage solution company based in Palo Alto. Most of my work involves pure distributed system fundamentals and no ML applications/usage.

So, I had finished my Masters in Computer Science from one of top tier institutions in India before I landed in this job from campus job fair (which did not have much to do with preferences/choices). The primary area of my research/thesis was Machine Learning. Have published couple of papers [1|2].

For quite some time I had been actively trying to switch/get a (first) data science role which would be a domain switch given my current area of work. The steps I thought would be optimal is to 1) revise concepts from books (Bishop/ESL/GoodFellow) or various materials, 2) restart hands-on coding, 3) maybe participate in some Kaggle Competitions, 4) keep applying to various jobs/people.

The challenges that I faced were, 1) I'm fresh grad with merely a year of experience is much less than the requirements, 2) Given my current role/job, it basically translates to no Data Science industry experience, and it would be true until the first such job, 3) In many occasions the requirements for some role includes experience in various big data frameworks.

So, could you advise on
1) How should I appoach, to compensate for the fact that I have no industrial data science experience ? Would some projects that demonstrate broad skills suffice? Does challenges like Kaggle help in this regard.
2) Along with ML/NLP core application, is it advisable to start learning other frameworks like ( Hadoop, SparkML etc.) and languages. Currently I'm okay in python but not so much in R,SQL.

Thank you so much for this initiative.

Hi - all your accomplishments sound great and build a good portfolio towards becoming a data scientist. However, your journey to data science requires more structure.
One path that may be beneficial for you to take is find a mentor who could guide you through:
  • types of data science projects you should take on
  • kind of jobs or positions within your organization you should seek
  • get certifications or degrees that could help you become a data scientist
If your current organization supports data science, then seeking career advice from within may help. If not, join meetups or online groups to find a mentor so that together you can structure a path to further data science education.
cgstefanita
Rank
Userlevel 3
Badge
Hello. As a neophyte, I am wondering if anyone has any ideas on how to engage with small businesses? Businesses that appear to be able to benefit from data science and or machine learning services, but which have not necessarily, connected the Who, Why, When and How dots.

I began my career building and deploying instrumentation, then distributing the acquired data to principles. Now, I am becoming proficient at data analysis and modeling. Together with my background in engineering and physics, my unique skill set affords me a perspective encompassing the entire data-driven analytics process, from sampling signals that measure real world physical phenomenon; to the generation of actionable insights informed by algorithms and statistical models.

From my point of view, I see opportunities everywhere. What is not as clear, is how to capitalize on these.

Can anyone here advise me on offering mutually beneficial data services, to small businesses?

Hi - is your question about how to become gainfully employed to help small businesses with data science and machine learning?
If so, two options:
  • Apply for a data science job at the small business you wish to help -- assuming they have such a position
  • If the small business does not have a data science position, become a data science consultant, assuming you get the necessary credibility through degrees, certifications and experience so that people would consider using your services.
cgstefanita
Rank
Userlevel 3
Badge
Hello everyone, first of all let me thank you. Please i have a question regarding to build a good resume as Data science and AI for job . What the most common skills should be included?
kindly if you can share with me an example of good resume.


Thank you in advance

Hi - thank you for your question. Your resume should include:
  • essential data science experiences (having a mentor, project participation, exposure to different data types etc)
  • diversified analytics tool kit
  • business domain knowledge
  • gained experience as a data scientist in real life environments
cgstefanita
Rank
Userlevel 3
Badge
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 -

Thank you for your insights.
One recommendation is to join data science meetups in your area - they are organized by companies in your geographical area. This way you get first hand experience as to how the field is evolving and many of your questions will be answered as you understand the techniques, the problems thay are trying to solve and overall innovations that are being done.

Also get a mentor from within your local data science community - or - find such communities online where you can contribute and make your presence felt. You have to give something to the community before you can receive.😉
cgstefanita
Rank
Userlevel 3
Badge
Hi,

First of all, thank you all for this unique opportunity! I am a PhD student in civil engineering at UIUC where I have utilized machine-learning algorithms to solve real life problems. Although I have taken the same classes as the CS students take and have papers published on applied ML, I feel like there is still bias against my degree in job applications for data science. What can I do to overcome this bias?.

Best,
Erman

Hi Erman,

Thank you for your question. To make other people believe you are a data scientist, you have to first believe it yourself. Answer first to yourself what you think makes you a data scientist based on your accomplishments so far - and - find gaps that you need to fill to be a data scientist. Once you have those answers and solutions, then you can start writing your resume and presenting yourself in such a way that people can see you in a data science light. You still have a way to go from civil engineering to data science, so it is not bias against your degree.
R
Userlevel 1
Badge +1

Dear all:

1.)
I got a badge from IBM as DATA SCIENTIST:

https://www.youracclaim.com/badges/27b08fa0-5fb1-4bf1-a7ae-fb778c18d18d

is this equivalent to a bachelor degree in the field ? an associate degree or what?

Can I call myself data scientist for real?

2.)
Other than data scientist or data engineer... Is it compulsory to hold a degree or professional license in : Finance, Economics, Enginerings: Chemistry, Physics, Mechanics, Electric, Aircraft, Operations, Telecommunications, Genetics, electronic, robotics. Actuarial science... I mean: a second career or another major where to apply data models


Thank you very much for your answer....Hi - congratulations on your badge.
  • A badge is not the same thing as a degree. It is just a measure of a milestone. Your badge is saying that you have accomplished this goal: being able to "explain how the IBM Integrated Analytics System architecture and parallel processing capabilities support modelling and analysis paradigms on large scale data sets.". The badge cannot be substituted for a degree and it does not make one a data scientist.
  • Degrees are not "compulsory". They are a choice one makes in their professional career. One can become a data scientist without the degrees you mention above, however just like with many career choices, there is a rigorous certification path one has to be on and complete and often that means getting some academic degrees.
R
Userlevel 1
Badge +1
HI THERE!!!

yes! i asked you those questions because I have always found laughable you must go to a bureaucratic, brick & mortar campus to get a degree that should be granted by IBM, MICROSOFT, CISCO, INTEL, AMD, AMAZON, accenture, google, SAS ... A manufacturer!! The real deal!!

Are there any colleges out there, granting bachelor or associate degrees in either Data science or Data engineering???

I majored back in 1999 in Financial Engineering and last year I got a Master badge from WORLD BANK GROUP...

Again, I find more useful world bank group education , nyif or any stock/derivatives exchange education than the Finance you learn on campus....

Professional guilds, regulators and service providers are handling with the real deal ... While colleges are not?

Or why do hardware and software manufacturers prefer candidates with manufacturers' professional certificates rather than college diplomas??
Sepi
Rank
Badge

Hi,

First of all, thank you all for this unique opportunity! I am a PhD student in civil engineering at UIUC where I have utilized machine-learning algorithms to solve real life problems. Although I have taken the same classes as the CS students take and have papers published on applied ML, I feel like there is still bias against my degree in job applications for data science. What can I do to overcome this bias?.

Best,
ErmanHi Erman,

Thank you for your question. To make other people believe you are a data scientist, you have to first believe it yourself. Answer first to yourself what you think makes you a data scientist based on your accomplishments so far - and - find gaps that you need to fill to be a data scientist. Once you have those answers and solutions, then you can start writing your resume and presenting yourself in such a way that people can see you in a data science light. You still have a way to go from civil engineering to data science, so it is not bias against your degree.


Hi Erman,

Just echoing on what Carmen mentioned above, there are a lot of great courses and material you can take to improve your skills in the fields and add to your resume, for example Coursera has many great courses, some of these courses even offer a certificate at the end. Also try to attend the meetups and be in community to keep yourself updates about what are the trends and skills needed in this area, what others are working on. Participating in some online coding competitions like Kaggle might help as well, or looking at some of the previous competitions and learn from the approach and code that others have posted.
andreviolante
Rank
Userlevel 2
Badge
Just rambling here...I have been studying data analytics for 4.5 years so merely a novice. Part and parcel to this study was the discovery of some interesting data offered by Gartner Research. Besides a subtle look at ROI calcs, the article suggested there was a 60% failure rate in Big Data Projects. So, as a result, I have been gathering research for almost a year now trying to identify the genesis of this failure rate. I know...get to the point...so, while everyone clamors for the next new language, I have to ask why isn't business purpose or process taught more (CRISP-DM as up dated by IBM) ? Further, it has been noted that data wrangling represents~80% of a data projects which to me suggests more time should be spent in SQL (and the its variants). In terms of a question, I would appreciate any insight you have regarding my observations. See source: https://www.linkedin.com/pulse/250-roi-journey-predictive-analytics-jim-conaway/

Hey Dr. J,

Thanks for the very relevant question! Let me provide some of my thoughts around the questions you posed and I'll try and bullet them out as I see them.

a) ...why isn't business purpose or process taught more?

I completely agree with this line of questioning. I've almost always used a loose-form CRISP-DM model when scoping my analytics projects. However, no single person in my organizations told me explicitly that "we're using the CRISP-DM model". The model was a process that worked well for us in a hub-and-spoke type model where a data science team is in the center (hub) and connects to each of the different business functions (spokes). We worked a lot with business stakeholders upfront to understand THEIR needs and the problem. It always came back full circle when presenting back the completed work. So long answer short, I completely agree that some curriculum around process and working with the business to increase deliverable value would be very beneficial!

b) it has been noted that data wrangling represents~80% of a data projects which to me suggests more time should be spent in SQL...

I love that you brought this up! I've helped dozens of colleagues with interviews and I'm always shocked when they're surprised about SQL questions showing up early in the interview. SQL is still a very necessary skill and collecting and dealing with data where the data resides is efficient and critical. I've taken several SQL courses in my grad programs but early on in my data science career I spent most of my 'coding' days writing SQL queries for my projects and the projects of others. I'm very grateful for that experience as its helped me tremendously with in my data science creativity as well as collecting data and visualizing data before its processed (aggregate tables, feature engineering, etc).

Hopefully this is somewhat helpful even though I agree with your entire post 🙂
Sepi
Rank
Badge

Hello everyone, first of all let me thank you. Please i have a question regarding to build a good resume as Data science and AI for job . What the most common skills should be included?
kindly if you can share with me an example of good resume.


Thank you in advanceHi - thank you for your question. Your resume should include:
  • essential data science experiences (having a mentor, project participation, exposure to different data types etc)
  • diversified analytics tool kit
  • business domain knowledge
  • gained experience as a data scientist in real life environments


Hi,

On top of what Carmen has mentioned, often programming languages and different softwares that you have used in the past are important too, for example, knowing Python, R, Scala, Spark ,... could be a huge plus. also adding related projects that you have worked on can be very effective.
R
Userlevel 1
Badge +1


Hi,

First of all, thank you all for this unique opportunity! I am a PhD student in civil engineering at UIUC where I have utilized machine-learning algorithms to solve real life problems. Although I have taken the same classes as the CS students take and have papers published on applied ML, I feel like there is still bias against my degree in job applications for data science. What can I do to overcome this bias?.

Best,
ErmanHi Erman,

Thank you for your question. To make other people believe you are a data scientist, you have to first believe it yourself. Answer first to yourself what you think makes you a data scientist based on your accomplishments so far - and - find gaps that you need to fill to be a data scientist. Once you have those answers and solutions, then you can start writing your resume and presenting yourself in such a way that people can see you in a data science light. You still have a way to go from civil engineering to data science, so it is not bias against your degree.
Hi Erman,

Just echoing on what Carmen mentioned above, there are a lot of great courses and material you can take to improve your skills in the fields and add to your resume, for example Coursera has many great courses, some of these courses even offer a certificate at the end. Also try to attend the meetups and be in community to keep yourself updates about what are the trends and skills needed in this area, what others are working on. Participating in some online coding competitions like Kaggle might help as well, or looking at some of the previous competitions and learn from the approach and code that others have posted.
i am finding itchy and irritating the fact that IBM geniuses do not acknowledge the badges and the badge system they created as a for real DEAL ... Why bothering with all the fuss? pure marketing only??
M
Userlevel 4
Badge +4
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!
M.R
grldsndrs
Userlevel 1
Badge
@cgstefanita
Thank you for replying. You asked, if my question is about "how to become gainfully employed to help small businesses with data science and machine learning?"

In a word, no. The question attempts to address the fact that many of these small businesses don't realize the opportunities before them. Opportunities to benefit from the data science revolution, as big businesses have benefited.
I think there might be a perception, that data collection and analytics ... etc, are only for the Google's and the Microsoft's and IBM's and Amazon's of this world. I think small business owners, don't readily see the opportunities data science can provide in their small data environment. I think they have data driven questions, as any owner would, but often lack the vision, time or resource to pursue them profitably.

As newly minted and hungry data scientists, I think, here there exists a synergy. We want and need meaningful projects to build our portfolios. They have data and proofing grounds, on which we can hone our skills. It presents, as sort of a minor league-major league scenario.

So my question really is, what kind of strategies can we employ to overcome, what I see as the two main obstacles for engagement: namely the lack of vision on the part of the small businesses and the lack of confidence in a newly minted data scientist?
We want to get them to see playing ball with us, as a prelude, to playing ball in the Majors.
M
Badge
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
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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
@MichaelM
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