The Data Science 101 blog dates back to its very first post in February of 2012. The author, Ryan Swanstrom, was transitioning from a career in software engineering to a career in data science. Thus, he decided to create a blog to share some of the things he has learned along the journey.

Hopefully, you find the Data Science 101 blog useful.

Since starting the blog, Ryan has become a data scientist, an educator for future data scientist, a director of data science, and an entrepreneur. You can read more about Ryan Swanstrom on his website.

Recognition for The Data Science 101 Blog

The Data Science 101 Blog is consistently recognized as one of the top blogs for data science.

42 Comments on “About”

  1. Ryan, Thanks for this awesome experience I had with this blog. I start my day with it. Keep this flowing!

  2. Hi Ryan,
    I was searching about data science and came to your website. It is an awesome blog. Thanks for maintaining it!
    I have few queries about data scientists/science. I would be glad if you provide your opinion.

    I have seen that most of the data scientists are PhD holders. Do you believe PhD is must for data scientist?

    I am a new entrant in the field of analytics. I started as a sales person in an analytics firm a year back (I hold bachelors degree in computer science and MBA in IT/Strategy). I love technology, advanced analytics and I believe I should go back and become a data scientist. What do you think? Is it a crazy idea? Will I be shooed away by PhDs ? 🙂

    1. First, thanks for commenting. Strange you should bring this up, I am currently working on a blog post on this topic. You are not the first to ask about getting a PhD. Anyhow, it should be out early next week. To provide a little teaser, no I do not think a PhD is required to do data science.


      1. PhD is mainly for exploration of new field and the main activity should be paper publishing. While I believe data science focus on applying inter-desciplinary knowledge to real problems.

  3. This site and Listudy are both tremendously helpful. Just incredible. Your hard work has not gone in vain! Thank you so much.

  4. Thank you for this blog!
    I am an undergraduate Italian student in statistics, and I’m going to start reading and studying something of this new field.

  5. Hi, Ryan.

    I’m pettinorang(nickname) living in Seoul and I’m also learning Data Science to be a Data Scientist. I just started,,,sort of newbie 🙂

    I’m enjoying your posts regarding Data Science and I think your posts are very useful and worth to share. I’d like to translate your post into Korean and have them up on my blog for students or people speaking Korean and preparing to be Data Science.

    I’d like to hear your thought on it and it would be very appreciated for me If I can get your permission.

    Looking forward to hearing from you.

    Thank you.

  6. Hey hi ! I was wondering if you could blog about the methods of formal education towards being a data scientist. I am an undergraduate Comp Sci student and wish to be a data scientist. I couldnt decide whether to pursue a MS in Business analytics which is a 9 months course offered by many universities or go in for MS in Comp Sci while specializing in data sciences which is a 2 year course. I personally feel the latter is a better option. Please shed some light on this

  7. Hello,
    I recently came across your blog and noticed your interest and immense knowledge in data science.
    I work for Cybera, Alberta’s not-for-profit technical agency that helps the province advance its IT frontiers. I am in charge of our website’s blog, http://www.cybera.ca/news-and-events/tech-radar/, and thought of you as a possible guest blogger.

    Our organization is actually having a summit late September about data science, so finding your blog was very interesting for me. I was thinking perhaps you would be interested in writing a blog post for us about data science (it could even be a re-write of a blog post you already have… or why data science/scientists is awesome and what we can all learn from it/them.. etc). This could help build your audience grow also. Cybera’s Tech Radar blog reaches a variety of audience from the educational and technical community.

    Let me know what you think!


  8. Hi Ryan This is one of the best blogs I’ve seen so far on the subject of data science and its innovation. I’d like to invite you and your community to attend the inaugural NFL hackathon, where attendees can get access to previously closed APIs and datasets.

    I think one of the best applications in which new insights and manipulation of data can be used is to uncover a deeper understanding on athletic performance (like the NBA) and I was wondering if you would be interested in having a custom code for free tickets for your community?

    Let me know 🙂

  9. Hi Ryan

    I have an article that you might be interested in taking a look at regarding the job market that faces budding Data Scientists worldwide. Would you be interested in taking a look at it?

    Great articles by the way, we would love your feedback!

  10. Only simple things work in life & Ryan is making data science simple to understand from his blogs. I like your contents & hard work Ryan sir.

    I want to pursue my career as data scientist for analysing sports & in the field of medicine. Any suggestion will be helpful.

    Thank you for your blogs. We love it so much.


    1. Mustafa,
      Thank you very much. Sports and Medicine are going to be great fields for data science. They already are. The White House is really pushing precision medicine, and funding a lot of research there. Just try to learn all you can about the sport you are interested in, medicine and data science. That is a lot of knowledge, so best of luck.


  11. Hello Dr. Swanstrom,

    My name is Matt Stauner and I’m with the UW-River Falls College of Business & Economics. I’m reaching out to you because we are interested in having our Data Science program added to your list of available programs.

    UW-River Falls, located east of the Twin Cities, is the only school in the University of Wisconsin System to offer this course of study as an in-person, undergraduate degree. The program involves coursework in areas such as machine learning, data visualization, data storage and statistics across the disciplines of computer science and information systems and math.

    For more information on our program, please visit: https://www.uwrf.edu/CBE/Programs/DataScience.cfm

    If you have any further questions, please don’t hesitate to reply to this comment or give us a call at (715)-425-3335.

    Thank you,

    Matt Stauner
    UW-River Falls College of Business & Economics

  12. Hi Ryan, I am surveying all universities (data curriculum) in all countries to predict the global shortfall in data skills (I have starting list of 9,364 universities in 208 countries). Can you take a look at the project and if you would like to help, let your community know the research work is underway… you can read about it, and nominate your uni/faculty at: http://www.cleardq.com/?page_id=321. Also I am looking for collaborators, sponsors and any other community help for this really important project. Thanks. Call me for more info.

  13. I really like your blog. It has inspired me to apply to Master’s programs in Data Science. I have a dilemma though: say you have two equally reputable MS programs but one is called Data Science and spawned from math/cs departments and another is called Business Analytics and offered at a prestigious business school. The former is mostly data science and all the stats and CS courses needed to make that work and the latter is about 40% business and how it relates to analytics and the rest is data science. Both programs have practicums but the one called Data Science has a longer one. My aim is to stand where data science and decision-making converge thus I am interested in a program with some business applications which is why I applied to this Business Analytics program as well. A Data Science program with a strong practicum might be enough to gain this component and especially for me having some of this experience already as the Computer Science Engineer with 15+ experience that I am. The only concern with a program that is mostly data science is that it might go to deeper in the math than necessary to be able to understand the tools of the trade and perform my job which I could probably do with either degree. I guess what I need to know is how is an MS Business Analytics degree perceived in the marketplace versus an MS Data Science degree? Is it more managerial? Does it have more earning potential? is it less versatile? Is it less sought after? Is it better for a mid-career change? Any thoughts on this would be greatly appreciated.

    1. I would say the data science degree would make you more prepared to be a hard-core data scientist, a person really doing the data analysis/gathering/modeling. The Business Analytics degree would better prepare you for a managerial role. I think they both have excellent earning potential and are highly sought after. Given your interests, I think either program could help you reach your goals. Thus, I always say, “choose the program where you will be most successful.” If you enjoy small classes, go to the school with smaller class sizes. If you like warm weather, go to the warmer school. There are many factors to consider, so choose the program most successful for you.

      I hope that helps, and thanks for reading the blog.


  14. Hi Ryan,

    Stanford University will host one of the fastest growing data science conferences, attracting more than 100,000 participants, on March 2. The Women in Data Science (WiDS) Conference features some of the most successful data scientists in the world talking about data science technology, ethics, opportunities, education and other topics.

    Please let me know if you are interested in live streaming the one day conference on your site and talking with a conference speaker prior to the conference. Sample speakers include:

    Margot Gerritsen, Senior Associate Dean, Stanford University
    Rama ​Akkiraju, IBM Fellow and Director of A.I. Operations
    Been Kim​, Research Scientist, Google Brain

    Relevant topics from 2019 include:

    Machine Learning and the Evaluation of Criminal Evidence
    Understanding the Limitations of AI: When Algorithms Fail
    Building Trust in the Digital Age

    The conference format is intended to engage and inspire women to pursue careers and do more with data science. Stanford University partners with the leading data science programs in academia and the private sector to bring together the top data scientists.

    Please let me know if you’re interested in talking with the WiDS speakers before the conference and posting the live stream on your site.

    Thank you

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