Could this be the Best Data Science Notebook? (Deepnote)

Key Takeaways

in this video i'm going to be showing you a jupiter compatible platform that you could use for your data science projects and without further ado we're starting right now so the jupiter platform that i'm talking about is called the deepnote.com in a nutshell deep node is a new type of data science notebook that is quite similar to google codelab in that it is a jupyter compatible notebook and it works pretty much like google docs or google sheets in which you could collaborate with other in your data science team by collaborating i mean you could annotate your code or commenting directly into your code cell with your other members in the data science team and aside from having access to a typical jupyter notebook you also have access to the command line whereby you could install packages that you need for your data science projects in addition it also provides the ability to use docker and the great thing about this deep note is that it's provided for free and so you could get started without paying a single dollar and let's go ahead and click on the try deep note for free and so you could log in by either using your google account or your github account okay and so upon logging in with your google account you will be seeing the following getting started notebook so let's have a look here so on the left hand panel here there are various functionalities so let's click on the files so clicking on the files will reveal the contents of the current working directory so my first impression is that this looks pretty much like a jupiter lab and so here you can see the jupiter notebook ipy and b and you could also see the csv data file and so this is the cell allowing us to import the data so why don't we go ahead and try doing that so if you click on the code cell you'll see that you could click on run or you could also annotate this particular code cell like for example if i click on add comments i could add some comments here so it could essentially be a note to self or it could be a note to other team members for example if you would like to make some changes to this or replace the csv file with the solubility dataset okay so i'm just making up some reason in the comment okay so you could add comments and if the comment has been resolved you could also click on resolve as well and then it will be gone and notice that when you add the comments here in the panel to the left there is a comment section and if you click there you'll have access to all of the comments that you have commented inside this entire notebook so this also comes in handy and if you click on a particular note or comment here it will be taken to the particular notes or comments that you have made in the notebook so let me write another comment somewhere else comment number two let's just say that and you you're going to be seeing that comment number two appears here let's click on the first one and so this brings us to the first few comments that we have made and let's click on this one and it takes us to the comment that we have just made a moment ago so this is a very nice feature to have when you're coding your data science project you could also annotate or your other team members could also annotate directly into your notebook and then you could address these comments at a later time okay let's go back here okay so the second item in the left panel is the environment so let's click on it so you're to see that the hardware is currently in offline mode so if you want to start it you could click on the start machine and then you're going to be seeing that you're using the free cloud resource and now the notebook is ready so why don't we go ahead and run the cell and then you're going to be seeing the output so you're going to be seeing the green tick here so this tells us that the cell has successfully been ran let's go to the next one so you see here that they have embedded the youtube video alright so this notebook will just tell us how to run the cell okay let's look at the left panel here again integration so you could have a wide selection of input data that you could import directly into your working directory so you have access to mongodb postgres sql amazon s3 and others and you could also store environmental variables here as well github okay so you could also commit directly to git with this feature and you could link it so this will prompt you to authorize the linking of your particular project right inside the deepnode.com interface okay the next one this is the terminal the command line that i was just mentioning to you so if you click on it and then you have access to the command line okay so you have access to the command line so this is a great feature and you could also install libraries as well let me try installing pi carrot okay so it works out of the box so look at the history okay so you can see what you have done so far okay so this is very nice so you could think of this as kind of like a jupiter environment on steroids because it allows you to do many of the common tasks that you would normally do on your own local computer but the great thing about it is that you could do it on the cloud and i really like the comments section here because it allows you to interact with other members in your data science team okay and so you see that the pi carrier library has already been installed and this was the one that i was talking about you could also configure your docker as well okay you could put in your contents for the docker in here or you could initialize your project with this notebook for example if you would like to install conda or some particular library directly you could either put it in here or in the docker file okay and so let me create a new project and we're going to be importing this tutorial notebook into the new project so why don't we just download this and put it on the desktop there you go so it added the dxt to the file name let me delete that and upload okay here upload upload success okay okay here it goes to the notebook so it's in the files so this is the working directory so let me install rd kits so click on run or the control enter or command enter if you're on a mac so upon running the cell it initiates the hardware and now you see that the computer is being booted okay let me close this so we get the main panel bigger this is also a great feature when you're running the cell you get to see the time that it's taking to run the cell and you could also click here to turn on a notification whenever your code cell has been finished all right now it's finished has been successfully installed let's download the data set here okay so we'll just use the one from the data professor github then okay so let's rerun this cell all right and then you see the output okay so nice histogram in the output here okay so why don't i just run the entire notebook run notebook okay all right so it's now going to run from the top it's going to run the installation of rdkit all over again and you're going to be seeing that the entire notebook is comprised of 59 cells and so the number here will be reduced as each cell has been completed and so this is a great way to look at how many cells are remaining and now the countdown all right and now it's all finished okay this is awesome this is a neat feature let's look at the end all right let's look at the output okay yeah so you could also output it as images as well and it will work so i think we've briefly seen the structure right let's have a look right here so we could also output it as the chemical image okay so scroll down [Music] okay scroll down so the entire notebook here ran without an issue all right okay that's the prediction performance let's see if the image is successfully displayed here vertical plot does it work yes it does work all right horizontal plot okay so everything works here and you could add your code cell text cell also sql and other input as well and so here you have it the deepnode.com so feel free to check this awesome data science jupyter compatible platform and let me know in the comments of this section whether deepnode is worth a try for your data science and diverse and also leave a note in the comments section whether you like it or which feature you like and how it'll improve your data science journey and so i look forward to reading all of your comments and if you're finding value in this video please give it a thumbs up subscribe if you haven't already hit on the notification bell in order to be notified of the next video and as always the best way to learn data science is to do data science and please enjoy the journey