Global Data Science Forum

Expand all | Collapse all

Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

  • 1.  Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri September 21, 2018 02:26 AM
    Edited by Ena Bevrnja Thu October 04, 2018 01:49 PM
    Hello everyone,

    I'm excited to announce the "Ask a Data Scientist: Careers in Data Science" - LIVE EVENT!




    Join us for this exclusive community event on Oct 4, from 11:00 AM to 12:00 PM (ET) where I will have a pleasure to chat with the great Data Scientist Caitlin Hudon live via webcast. We will discuss topics such as breaking into and transitioning to data science, some of the skills required, how to prepare for job interviews, and much more.

    We will be taking questions during the session as well as answering the questions you leave here in this forum. Feel free to drop them ahead of time and we will make sure to answer them.


    We look forward to having you at this special community event!

    Cheers,
    Gabriela de Queiroz


    Register here: http://ibm.biz/ask-a-data-scientist



    ------------------------------
    Gabriela de Queiroz
    ------------------------------


  • 2.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri September 21, 2018 02:30 AM
    I'm going to add the first question here:

    How can I best prepare for a Data Scientist interview?

    ------------------------------
    Gabriela de Queiroz
    ------------------------------



  • 3.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri September 21, 2018 01:30 PM
    I'll add a question as well!

    What are some underrated skills that new or aspiring data scientists should focus on building?

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 4.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Mon September 24, 2018 05:27 PM
    We're looking forward to chatting with both of you during our #AskaDataScientist event next week, Careers in Data Science. Thanks for taking the time to connect with our community members and offer your expertise and insights. 

    Community members, I've linked some of our speakers' recent work below - please introduce yourselves and start dropping questions into this thread ahead of time!

    YouTube Webinar: ​​​Statistics for Data Science: What You Should Know and Why, @Gabriela de Queiroz
    Presentation Slides: , ​
    Blog: OSCON2018 - TensorFlow Day, @Gabriela de Queiroz
    Blog: A Month in the Life of a Data Scientist, @Caitlin Hudon 
    ​Blog: The Coolest Things I Learned at JupyterCon, @Caitlin Hudon
    Blog: Supporting Women in Data Science, @Caitlin Hudon ​​​​​​

    ------------------------------
    Christina Howell
    ------------------------------



  • 5.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 01:49 PM
    Hi Gabriela and Caitlin, 

    Is a Udacity Nano degree sufficient to become a data analyst or data scientist?

    Thanks for sharing your feedback!

    ------------------------------
    Kitso Leshope
    ------------------------------



  • 6.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:05 PM
    Hi Kitso!

    Disclaimer: I haven't used Udacity, so I'm not 100% sure what their Nano degree covers, but my answer is: it depends. The skills needed for a data analyst and data scientist job can be very different (even within the same company), so I think the best way to know whether you'll be prepared is to read the job / application requirements, and make sure that whatever training method you choose covers most of the things that a prospective employers is interested in.

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 7.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 01:58 PM
    Edited by Ena Bevrnja Thu October 04, 2018 02:34 PM
    Hello Gabriela and Caitlin,

    Thank you so much for doing this, ladies! My question is - How do you know you are ready to interview, for someone who is transitioning from a different field into data science? How much of a portfolio should I build and how should I approach this?

    Also, do you have any resources on public data one can work on?

    Thanks in advance!

    ------------------------------
    Moyosore Ajeigbe
    ------------------------------



  • 8.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:10 PM
    Hi Moyosore!

    I'm going to answer your questions one by one: 

    How do you know you are ready to interview, for someone who is transitioning from a different field into data science?

    I would read many job postings for the type of position you're interested in, and start interviewing when you feel you have most of the skills needed. Don't wait until you feel 100% ready -- I've never met 100% of requirements on a job posting, and I've still been hired :)

    How much of a portfolio should I build and how should I approach this?

    I think one or two good projects is a nice start. My approach would be to create a GitHub repo that includes a good summary / documentation around that work you did and the process you went through to do it. Pick projects that interest you, and that you think will showcase your skills (like data munging, exploratory analysis, modeling, etc. -- or whatever combination you'd like). 

    Also, do you have any resources on public data one can work on?

     I'd suggest checking out the new Google Dataset search tool, data.world, and Kaggle as good starting places. There's a ton of free data out there. 

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 9.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 02:41 PM
    What would be the learning stages to become a Data Scientist, from the languages needed to be learn to the more complex tools or frameworks?

    ------------------------------
    Juan Lessey
    ------------------------------



  • 10.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:15 PM
    Hi Juan!

    What would be the learning stages to become a Data Scientist, from the languages needed to be learn to the more complex tools or frameworks?

    Many others have written about learning stages to becoming a data scientist, and have done a better job than I could do in a quick response. Generally, I'd suggest learning R or Python, and then diving into more complex tools / libraries as you need them. 

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 11.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 02:49 PM
    Hi Gabriela & Caitlin,

    Are there other coding languages that are complementary to Python or R, or do you do most of your work in one language? (better to be master of 1, or jack of a couple trades?)

    ------------------------------
    Erika Anderson
    ------------------------------



  • 12.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:19 PM
    Hi Erika!

    I think R and Python are both great languages, and which language I use depends on what's better suited for the task I'm working on. I typically do quick analyses and visualization in R, and machine learning and scripting in Python, but that's mostly just my personal preference based on what I'm most comfortable in. (In case it's helpful context, I learned R first and am able to clean data very quickly using tidyverse functions, while most of my Python work has been focused on the ML libraries.)

    You can do most data science stuff in either language, but it's less common to see R code in something like a machine learning pipeline, which tends to be more Python. Generally, I'd pick the one you're most comfortable using, and learn the other if and when you need to.


    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 13.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Wed October 10, 2018 05:59 AM
    Thank you! I'm starting on Python, but was wondering if you learned SQL, HTML, JavaScript in addition. The reason I asked is because I started with LinkedIn learning for Python and it listed several other things to try first (HTML, CSS, JavaScript). I dabbled in those and work with SQL regularly, and after a few days decided to go back to Python directly. As you mentioned, R seems to be better for visualization.

    ------------------------------
    Erika Anderson
    ZeroChaos | Technical Consultant
    ewurster@gmail.com
    Jacksonville, FL
    ------------------------------



  • 14.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 03:00 PM
    Gabriela-did you find your transition from academia to industry difficult or challenging? Do you have any advice on how to make this jump?

    ------------------------------
    Victoria Valencia
    ------------------------------



  • 15.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Sat October 06, 2018 06:59 PM
    Hi Victoria,

    Thank you for this great question!

    "did you find your transition from academia to industry difficult or challenging?" 

    The transition from the academia to the industry was challenging  (but totally doable). For me the major challenges were:

    1) interview - I didn't know I had to "sell" myself when talking to a recruiter for example. I thought I had to answer the questions and that was it. But as I kept doing more interviews, I understood that they needed to hear more, that I needed to elaborate a little bit more.

    2) project deadlines - Depending on which company you work for and the type of the role you have, you can't work for months in a model. You have to deliver results fast, you have to take actions quickly.

    3) good enough - In academia, we are encouraged to spend as much time as we need to find the innovative solution. In industry, we are encouraged to spend as little time as possible to get to a point where the results are good enough. For example, if you create a model and it is better than the one you have in production now, that is good enough. You will probably deploy this one and then work on something else.


    "Do you have any advice on how to make this jump?"

    My advice is practice, practice, practice. Do a few interviews to practice and to understand what companies are looking for. Also, talk to people that went through interviews recently and ask for tips. Find people that you know that have experience in recruiting and ask for some suggestions as well.


    I hope this helps! 


    ------------------------------
    Gabriela de Queiroz
    ------------------------------



  • 16.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 03:09 PM
    I've heard the title "data scientist" thrown around liberally by some, and very strictly by others. When did you consider it appropriate to use the title "data scientist" to describe yourself?

    ------------------------------
    Brendan Mahoney
    ------------------------------



  • 17.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:25 PM
    Hi Brendan!

    I've worked at some places that have used the term 'data scientist' liberally and other places that use it more strictly -- and that's totally normal, in my experience. Data science is such a new and changing field that I think the definitions of 'data scientist' and 'data analyst' vary (sometimes by a lot) from place to place. I often say that I've been doing "data science-y things", and did call myself a data scientist before getting an actual title of data scientist, which can be common, depending on the industry and the company you're working for. I think if you're doing the things that a data scientist does, you're a data scientist.

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 18.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 03:28 PM
    What are big topics, concepts or tools that you would like to see in a candidate's portfolio?

    ------------------------------
    Darryl Balderas
    ------------------------------



  • 19.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:28 PM
    Hi Darryl!

    This is a tough question because it's *so* broad. I'd look for a data science language like R or Python, use of relevant packages (like the tidyverse, pandas, and/or machine learning packages, depending on the project), for good communication / documentation (bonus points for a blog or write-up to accompany the analysis), and for the code to be generally understandable. That's super broad, so if you have more specific follow-up questions, please feel free to respond with them.

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 20.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 03:38 PM
    I acquired my Library Science Master's Degree years ago and would like to move into a career as a Data Scientist. What skills should I acquire prior to applying for a Data Science position for a firm foundation?

    ------------------------------
    Tameka Woody
    Accelerated Value Specialist
    IBM
    Durham NC
    919-543-2480
    ------------------------------



  • 21.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:31 PM
    Hi Tameka!

    I would read many job postings for the type of position you're interested in, and focus on filling in any of the gaps that your Master's program hasn't covered -- this will ensure that the skills you're learning are ones that are actually going to get you hired. 

    Hope that helps!
    Caitlin


    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 22.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Thu October 04, 2018 03:52 PM

    Hi Gabriela & Caitlin,

    I have a question that deals with your statistical knowledge. As a computer science major, what sections of statistics should a C.S. major focus on to best get a grasp of data science methodologies such as machine/deep learning? I only understand the basics of statistics from college and would love to know what I should look into to get a better grasp of the underlying fundamentals of data science.



    ------------------------------
    Matthew Nevle
    ------------------------------



  • 23.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:34 PM
    Hi Matthew!

    I'd focus particularly on basic statistics (like an intro course), including learning about the various distributions of data, probability (including Bayesian inference to get some exposure to those ideas), and linear algebra (not technically statistics, but many ML algorithms are basically giant linear algebra problems).

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 24.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 10:01 AM
    Hi,

     I am new to data science domain and want to learn it.
    I want to enroll in Udacity data science programme. How is the Udacity data science programme?
    Please let me know the review.


    Thanks,
    Kishan Patro




    ------------------------------
    Kishan Patro
    ------------------------------



  • 25.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:36 PM

    Hi Kishan, 

    I haven't taken the Udacity program, so unfortunately I'm not qualified to answer your question.

    Best,

    Caitlin



    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 26.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Mon October 08, 2018 04:45 AM
    Good Morning
    iam very happy to read All here nice to meet you Iam Lahcene from Algeria Mentor in Data Visualization withe coursera  and ihave got certificate withe IBM in Data Science Ibam provided me later a Badge for connecting withe other member iam really excited for it

    Best Regards
    lahcene 























    ------------------------------
    Lahcene Ouled Moussa
    Mentor
    Coursera
    Algiers
    +213550115977
    ------------------------------



  • 27.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 11:17 AM
    There are times in our projects that we reach a problem and no solution is available for. So, we need to come up with our own algorithm. How do you approach that? What if no solid metrics exist for this area, do you come up with a metric or modify a similar one?

    Also, more general, how can I impress on my first ML job?

    ------------------------------
    Ammar Asmro
    ------------------------------



  • 28.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 06:01 PM

    Hi Ammar, 

    Your question is tough to answer because it's so general -- so I'll give you an idea of what I would do if I thought I'd run into a problem that hasn't been solved and would need to create my own algorithm. 

    The first thing I do whenever I run into a problem I'm not sure how to solve is to research: have others run into this issue (most often, *someone has*)? If yes, how have they solved it? Googling is really helpful here. 

    The next thing I do is ask others on my team to make sure that I understand the problem fully, the caveats of the data, and the business questions we're trying to answer (or goal we're trying to achieve). If I think it will help, I sometimes reach out to other folks in my field who seem to have solved similar problems to see if they have any advice. 

    As far as creating metrics, I think about the business value and what it is we're trying to measure or move. Start with a blue sky -- if you could track or know anything, what would it be? Then look at the data you have and try to connect the dots to move towards the thing you want to know. 

    I know it's general, but I hope this helps. 

    Best,
    Caitlin



    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 29.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 12:02 PM
    Do you have any tips on connecting to employees at companies you want to work at, particularly for informational interviews? Also, any tips for contacting potential mentors? I've sent out many messages on LinkedIn and have received 0 responses.

    ------------------------------
    Jamie McNicholas
    ------------------------------



  • 30.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:55 PM
    Hi Jamie!

    I'm sorry to hear that you haven't had many responses. I'd try to connect with employees at companies you're interested in through introductions if possible (look for mutual connections on LinkedIn), or by meeting in-person at events (for example, Meetups held at the company you're interested in). This can mean attending lots of Meetups, but it doesn't hurt to simultaneously build your skillset and build your network. 

    As far as potential mentors, I'd focus on people you know already. It's *very* hard to mentor someone you don't know, and it's a big time and commitment to ask of someone you don't know. (Personally, I'd have a tough time mentoring someone I didn't know well.)

    Keep it up, and I hope things turn around for you!

    Hope that helps,
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 31.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 12:09 PM
    Hi!

    Thanks for doing this. I'm a PhD student in educational psychology and educational technology, and I'm interested in a career change. I do survey research and have done some analyses in R. How can I (1) convince employers to interview me and (2) build skills that I need?

    Thanks!!

    ------------------------------
    Emily Bovee
    ------------------------------



  • 32.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:48 PM
    Hi Emily!

    1. I'd focus on meeting people in data science, and networking to open as many doors for yourself as possible. Every job that I've ever had has come through some sort of networking (in one case, even after my resume was thrown out by their system!), and the human connection will get you a lot further than trying to stand out on paper against a number of other qualified applicants. 

    2. I'd read through the job descriptions for the types of roles you'd be interested in, and use whatever skills you don't have as a roadmap for the skills you need to build. Once you know what you need to learn, it's easier to search for courses / workshops / books etc. to start filling in the gaps. 

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 33.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 01:02 PM
    There are lots of resources online for learning data science skills like Python, R, or machine learning, but there are not as many that talk about how to actually set up and solve data science problems. How do you define a question you want to answer, and how do you design your project or solution? Are there any resources you recommend to gain more experience in this?

    ------------------------------
    Yuliya Astapova
    ------------------------------



  • 34.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Fri October 05, 2018 05:45 PM
    Hi Yuliya!

    This is a tough question, and you could write several books without answering each part fully -- I'll do my best to give a quick overview. I think the best way to gain this experience is to do it over and over. 

    Usually, the question you're trying to answer is a business one, and the process goes like this (I'm paraphrasing a slide by Renee Teate here): 
    Business question --> data question --> data answer --> business answer

    So as far as how to frame the analysis, I start by thinking about where I want to end up -- like what it is I actually want the analysis to tell me, and work backwards from there. Is it a prediction? Is it a summarization of historical data? Is it an analysis, a model, a service? Asking these kinds of questions helps me to frame how I should approach my problem, what data I should use, and what I should be working towards. Asking these questions up-front is also helpful so that you know what you *can't* accomplish. If you realize that you don't have the data to actually help with the problem you're trying to solve, you can save a lot of time by re-phrasing the question or further communicating with stakeholders to refine the question and approach(es) you could take.

    For examples, I'd suggest looking at some of the kernels on Kaggle that are focused on how a person approached a problem. It's really hard to find examples of a fully described problem space, the attempts a person made (and threw out) to solve the problem, and how they made those decisions -- although these do make for good conference talks, so it might be worth Googling those. 

    Hope that helps!
    Caitlin

    ------------------------------
    Caitlin Hudon
    ------------------------------



  • 35.  RE: Ask a Data Scientist: Careers in Data Science - LIVE SESSION (Oct 4, 2018)

    Posted Mon October 08, 2018 03:17 PM
    How can you market yourself as a data scientist with engineering/research background (eg on linkedIn)?

    ------------------------------
    Bahareh Golfar
    ------------------------------