How to Get Ahead of 99% of Data Analysts

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  • Опубликовано: 22 янв 2025

Комментарии • 34

  • @MattMike
    @MattMike  5 месяцев назад +2

    You can also find me on LinkedIn where I post (almost) daily about how to advance a career in data analytics: www.linkedin.com/in/matthewmike/

  • @nileshhazra
    @nileshhazra 5 месяцев назад +9

    Thank you Matt. Great video, really loved the content.
    1. Domain knowledge
    2. Storytelling
    3. Product sense (Problem Solving)

  • @yp8666
    @yp8666 5 месяцев назад +2

    Hi Matt, I really love this content that you put out. I love the fact that you emphasize domain knowledge - very few content creators emphasize this! I would really like to know more about product sense/problem solving as this is crucial in today's competitive job market. Any resources that you can share or upcoming videos on this will be really helpful. Thank you.

    • @MattMike
      @MattMike  5 месяцев назад +1

      Thank you! Planning to maybe break each of these 3 topics into their own in-depth videos :)

  • @wadealexander2478
    @wadealexander2478 5 месяцев назад +1

    Excellent presentation with actionable material. Having served as a daily intelligence briefer for senior executives, you are spot on for delivering analytical results.

    • @MattMike
      @MattMike  5 месяцев назад

      Thank you! So glad to hear that!

  • @Lg_Percent
    @Lg_Percent 5 месяцев назад +1

    Good stuff thanks
    Domain knowledge
    Storytelling, understanding context, know who you're speaking to
    Product sense, understanding a product, knowing what makes it great and how to improve it

    • @MattMike
      @MattMike  5 месяцев назад

      Glad it was helpful!

  • @SergioTinoco
    @SergioTinoco 5 месяцев назад +1

    Great video, it was like having a conversation with a Mentor!

    • @MattMike
      @MattMike  5 месяцев назад

      Thank you!!

  • @vatsalpatil6844
    @vatsalpatil6844 5 месяцев назад +2

    Great all of your knowledge make sense it is very much useful

    • @MattMike
      @MattMike  5 месяцев назад

      Glad to hear that!

  • @irinamicov
    @irinamicov 5 месяцев назад +1

    Really informative and well structured video. I think that in terms of domain knowledge it’s very hard to stand out unless you have specific experience. The other two skills can be developed, but it might be harder and take longer than acquiring tech skills.

    • @MattMike
      @MattMike  5 месяцев назад +3

      Domain knowledge is definitely one of those skills that mainly comes from experience. Easier for later career professionals to leverage when transitioning to data. I'd say the way to develop it without actual experience is through industry specific projects.

    • @irinamicov
      @irinamicov 5 месяцев назад

      @@MattMike Completely agree!

  • @muhammadomer1654
    @muhammadomer1654 5 месяцев назад +1

    Thanks Matt for sharing this undiscussed but precious information. Could you pls tell us how a beginner gain these soft skills for a better data career?

    • @MattMike
      @MattMike  5 месяцев назад +1

      I may do a few follow up videos to address each of these points more in-depth :)

  • @chrischris5715
    @chrischris5715 5 месяцев назад +1

    Thanks man, very good video 🙏

    • @MattMike
      @MattMike  5 месяцев назад

      Thank you!

  • @LuisFernandez-nm8qq
    @LuisFernandez-nm8qq 5 месяцев назад +1

    Great video!

    • @MattMike
      @MattMike  5 месяцев назад

      Thank you!

  • @snehashekar1839
    @snehashekar1839 4 месяца назад +1

    Can you suggest any resources to gain domain knowledge , thanks for the video it's so useful

    • @MattMike
      @MattMike  4 месяца назад

      My next video will be on this exact topic.

  • @shaikazar2436
    @shaikazar2436 5 месяцев назад +2

    Thanks for sharing, Matt. Could you share a few resources for building domain knowledge and product sense?

    • @MattMike
      @MattMike  5 месяцев назад

      I'm thinking I may address those points more in-depth in a couple of follow up videos :)

  • @Datacentriq-nz9iq
    @Datacentriq-nz9iq 5 месяцев назад +1

    Thanks for sharing Matt Mike. Can soft skills be taught as I have always planned to design a course on soft skills for data professionals ?

    • @MattMike
      @MattMike  5 месяцев назад

      That's awesome! I definitely think they're harder to teach and that's partly what makes them so valuable. With that said, I think if you provide the right frameworks and systems, they can definitely be taught. It will just require more work on part of the student to internalize those characteristics.

  • @ATHARVAMATHE
    @ATHARVAMATHE 5 месяцев назад +1

    Thank you for your review but as a 20 year old beginner I literally couldn't understand what you are saying.
    Plzz if you can ease it up a little for beginners like me it would be really helpfull!!!!

    • @MattMike
      @MattMike  5 месяцев назад

      Perhaps I should have spent more time defining each concept. I’m thinking I may do a separate video for each of these 3 topics to go more in-depth and give a roadmap for developing each one.

    • @ATHARVAMATHE
      @ATHARVAMATHE 5 месяцев назад +1

      @@MattMike That's a great idea it would be very helpful

    • @akshaysaraf6865
      @akshaysaraf6865 5 месяцев назад

      @@MattMike Please develop a roadmap of these skills and share with all of us. It will be a lot helpful. Thank you.

  • @napri2450
    @napri2450 5 месяцев назад +6

    I'm hesitant to study data analysis more deeply because in my mind is whether data analysis will be replaced by AI or not?

    • @MattMike
      @MattMike  5 месяцев назад +8

      I don't think you have anything to worry about, at least not any time soon. Studies are showing that the demand for data analysts is actually increasing rather than decreasing and very few companies are actually using Gen AI in their day to day. AI is also far from nailing the "human" component of the data analyst role.

    • @LordSalchipapa9999
      @LordSalchipapa9999 5 месяцев назад +2

      It will help data analysts speed up their work. It also makes a lot of mistakes, lacks domain knowledge, doesn't understand semantics, can't certainly deal with evergreen unreliable business people, etc.
      It could save you some time. Yesterday for example I used it to analyze which combination of columns would add up to a total value. It saved me 10 minutes from writing an algorithm in Python.
      I still had to verify the answer to see if it was correct.
      I asked it to interpret certain data from a table and it did it AWFULLY WRONG. Nor Llama, nor GPT4o, nor Claude Sonnet could do the work.
      It is far from the good the AI company's marketing team fabricated hype tries to convince you it is.