What’s keeping you from learning with Dataquest?

Sometimes, things can get in the way of progressing in your learning journey - and the community is here to help. What’s keeping you from learning with Dataquest?

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@vikp Thanx for bringing out this topic. Am in the midst of my DataScientist Path (25% done). Have a few concerns - hope you’d be able to guide!!

Though I am excelling well through each Mission, but after each passing Mission - I feel as if I couldn’t absorb any knowledge, as I don’t seem to remember anything about the previous Missions & finally feel that the whole time has been wasted.

This frustrates me a lot… coz, inspite of all the time am investing (almost on a daily basis) to finish the path… am still not being able to move the needle & feel all the productive time is being wasted… Which makes me rethink on whether or not continue the path further - as its a massive investment of time (which I am ok to invest) - but doubt I wouldn’t be able to remember/recollect/connect to any of the content, at the end of all this.

Also, considering the pretty long time frame the path demands - to complete, is it wise or foolish, to pick Jose Portilla’s Python for Data Science and Machine Learning Bootcamp Udemy Course - to be able to complete the same curriculum at a pretty faster pace??!!

Pls suggest/guide.

- Premium Subscriber (Annual Plan)

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Hi, I got stuck as you did. But recently i realized we didn’t remember how to use python doesn’t mean that we didn’t absorb knowledge we learned. It’s just because there are so many tiny usages spread among different sections don’t related to each other in python. So it’s quite normal we forget knowledge don’t have a relationship fast. Python is just like a tool we have to use regularly and a lot, otherwise it will slip away our minds until next time we re-pick up.

I think get some supplement materials like the course you mentioned above, or do some projects after learning will be helpful.

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I am afraid that Data Analysis won’t be a fit for me, maybe because i changed learning paths so many times. This fear paralize me most of the time and starting a lesson fills me with anxiety. Then i go to your Student Stories Archive and read again all the testimonies, trying to remember myself why i wanted to try this at first and realizing that if they could maybe i can.

I have a Bachelor’s Degree in Genetics and Biotechnology, graduated 5 years ago, never practiced the career. I went working in my sister Start Up as a Moodle Administrator, i spent 4 years there before i quit due to stress issues. What i used to like about the job are the times i got to use excel an SQL to make reports for the client. That is the only clue i got that i am choosing the right path.

As far as changing careers is concerned, I find it very difficult to get a data science job without first being a data analyst. And as far as obtaining that goes, the vast majority of data analyst jobs require primarily SQL and some sort of viz tool (tableau, looker). I feel like the python for data science here is very strong, but the more basic tools for SQL are fairly standard and understandably I have to go elsewhere to learn viz tools

Right now there are some technical issues which are getting in the way.

The most pressing is the Command Line missions, where progress isn’t properly tracked and the terminal is constantly disconnecting especially between missions.

Also the Basecamp module is deprecated, this is causing many issues with running code. I’ve set up a venv just for Basecamp however ideally this should be dealt with - either update the code for Cartopy or find another solution.

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Like other’s pointed out, While I progress through missions , I feel like I am forgetting the concepts I studied in previous missions, seems like I haven’t been studying anything. Since I struggle to put the concepts in use with the later missions, I have to keep coming back to previous missions. It has become a very tiring task and I am starting to lose interest.
Some suggestions, if I may,

Please include some refresher sessions in between, with some coding examples and quizzes(along with refresher modules,and not standalone quizzes).

There can be some visualizations in the concepts window on left, where we have only written concepts. It would an easy and faster way to recollect and remember what we did in those missions while progressing forward.
It can keep us hooked to the course.

Premium Subscriber

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Hi there,
I have the same feeling than my peers, I’ve tried different path 25% done for the data analyst path. However I feel like I can’t remember the concepts that I have learn and this feeling is one of the reasons I am not really consistent on my learning.
It would be great if you can suggest any tips or advices to learning consistently and finished at least one path before the end of my yearly subscription on June 2020.

Mouhamadou

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Technical issues… “Run Code” runs fine but “Submit Answers” is not getting the same results. Can’t advance if keep getting issues and this is the first module. :roll_eyes:

Should I be going to DataCamp?

Hi @Datom,

I also address this to @anuragdora5, @mogueye87, and others who find they are not holding on to concepts as you complete missions.

TL;DR:

  1. It helps to stop and try to talk it out with yourself. "I don’t exactly know what I just did. What do I know? What success can I own today and what would it help for me to read more about tomorrow?
  2. When you start to do analysis outside of DQ, you’ll be surprised how much you’ve actually retained, and how well you can recall things that felt entirely foreign to start.

For one, I share this feeling often while working through DQ. I subscribed 2 years previously and did not complete the full data scientist path: first because I didn’t prioritize my learning and second because I came to doubt that data science was the career I wanted and felt that meant I should focus my efforts elsewhere. I’ve returned this year because I realized that, even if I never become a data scientist, the skills I learned on this platform have been immensely helpful in my current work. They have helped me deliver greater value to my employer and differentiate myself as a valuable resource for working with data. (I’m currently employed as a product manager, but had been using many of the skills I developed on DQ as a customer service associate as well).

Both in my first run and my current run through DQ, I find I will get some concepts quickly and easily retain them, and others I have to keep referring back to old lessons to understand what I’m supposed to be doing and why. This is the nature of experiential learning: learning by doing. My brain has room to understand the code, or the concept, but not both at the same time, and especially not while trying to correctly answer a prompt to get to the next page. This is actually what makes learning with a platform like DataQuest so special.

In general, when I come out of something feeling like I have no idea what just happened, I’ve found it very helpful to try to hold a conversation with myself about the situation. What did I take away from what I just did(discussed/read/etc.)? What did I expect to take away before I went into the lesson? Now that I can reason about the gap between expectation and reality, what’s a good place to start in closing that gap? Should I read more about a topic, or try a technique again in a different way? Or if the gap isn’t too large, can I keep going and fill in the blanks later?

As a larger, likely dissatisfying reply (at least it was unsatisfying for me when I heard this while first learning): There’s some magic that comes in down the road. When you start working on the more challenging projects on the platform, or when you come across a real-world/work problem. The turning point for me was when I wanted to know how to do something DQ hadn’t taught me, or wanted to ask questions I wasn’t prompted to ask.

Even if you don’t come out of a lesson with the ability to recite exactly what you learned and how to do it, you have likely retained some base memory that tells you, in the back of your head, “I know I’ve done something like this before.” This is a cue to yourself: I know this, and I’m not sure exactly how. But I’ve done it before, so I know I can do it again.

Then when you’re in the “wild west” that is reasoning through a data set on your own, you’ll start to build your own patterns and methods. You’ll get stuck and have to look up documentation because no lesson has covered exactly what you’re trying to do but you’ve done something like it. I’ve found going from lessons to documentation to be a key transition from being a novice to an effective data analyst (lower case, not as a career but as a practice).

And all of this isn’t meant to imply DQ is perfect or not retaining information is a “you problem”. No platform is perfect. I will say in my 1+ year gap here the team has made impressive strides in improving the lessons, and building in little things to help improve retention and confidence. I’m sure they will continue to do so, and our feedback will always be valuable.

I offer my experience up merely as encouragement. Keep developing personal ownership of your learning, and keep digging into concepts you find challenging or confusing. You’re building a home, and it has to happen piece by piece. Keep the bigger picture in mind. Then one day you’ll be in a room of people with important problems, and there will be a click in your mind when you realize you have exactly the toolbox you need to offer solutions.

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Awsum advise @wolfahoward !! :clap::pray::bowing_man:
Thanx a ton for taking time to put it across!!

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I would like to second both points @otto.roberson is making.

  • Command Line missions are still not a smooth ride and
  • Basemap is definitely not a technology we should be learning as it is depreciated in favor of Cartopy.

It would be great, if these hiccups would be fixed / updated.

But in general: thanks a lot for all the great content, the community and the customer service! :slight_smile:

Well, I’m living in Iran. And I cannot pay for anything out of Iran because of sanctions against Iran.