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Become a Data Scientist: The Journey through Data Science and AI | Coursera Community

Last updated: 07-21-2019

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Become a Data Scientist: The Journey through Data Science and AI | Coursera Community

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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.
Coursera Community Manager
11 July 2019
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!
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.
16 July 2019
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?
16 July 2019
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,

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