Batch 5 Students Repository

Welcome to Lisbon Data Science Academy Batch 5 Students repository!

Your first step in this journey is to carefully read the steps in this tutorial. You'll learn:

➡️ How to set up your environment;
➡️ The weekly workflow to follow during the Academy

Initial Setup
Learning Unit Workflow
Updates to Learning Units
Help

Initial Setup

Windows 10 Setup

This section deals with setting up Windows Subsystem for Linux (WSL) on Windows 10.

If you are using MacOS or Linux you can skip this section.

Why do I need to install WSL?

Because of the differences in command line syntax between Windows vs Mac OS/Linux, it would be a great challenge for us to support and provide instructions for both Operating Systems. For this reason, we’d ask you to install Windows Subsystem for Linux which enables you to run Linux command lines inside Windows.

⚠️ Keep in mind that these are simply extensions to your Windows operating system, hence, installing this software will not do any changes on your laptop. It is also quick to do so. ⚠️

Step 1: Follow this guide to setup WSL on Windows 10.

Step 2: Open a terminal (remember this!!) and run the following command:

sudo apt update && sudo apt upgrade && sudo apt install git

Step 3: Open a terminal (remember this!!) and check if you already have python3.7 by usind the command below. If your version is Python 3.7.x (x = any number), you can skip to step 4, otherwise continue with step 3.1 and 3.2

python3.7 --version

Step 3.1: Run the following commands to setup Python 3.7 (if you get an error with this command, check this ):

sudo add-apt-repository ppa:deadsnakes/ppa

Step 3.2: Run the following commands to install Python 3.7

sudo apt update && sudo apt install python3.7 -y

Step 4 Run the following command to get pip and venv:

sudo apt update && sudo apt upgrade && sudo apt install python3-pip python3.7-venv -y

Why do we install these?

We'll be using pip which is the reference Python package manager. You should always use a virtual environment to install python packages. We'll use venv to set them up.

MacOS Intel Setup

Some of the steps in the following sections will require Homebrew for MacOS. Homebrew will make it easier to install software that we will use later on.

Step 1: To open the terminal, choose one:

In Finder , open the /Applications/Utilities folder, then double-click Terminal.
By pressing cmd + space then type terminal and press enter.

The terminal should now be open:

Step 2: To install Homebrew for MacOS, copy and paste the following line in the terminal:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

Step 2.1: Sometimes it's necessary to install xcode command line utils. To do so, do the following command before installing homebrew:

xcode-select --install

You may be prompted to install the Command Line Developers Tools. Confirm and, once it finishes, continue installing Homebrew by pressing enter again.

Step 3: open a terminal and run the following command:

brew update --verbose

Step 4: then run the following command:

brew install git

Step 5: then run the following command:

brew install [email protected]

Step 6: then run the following command:

brew link [email protected]

MacOS M1 Setup

So you got the new M1 and you're supper happy with how fast it is.. Unfortunately dealing with apple silicon requires a little get around. But don't worry, we'll be able to get there in the end.

Step 1: To open the terminal, choose one:

In Finder , open the /Applications/Utilities folder, then double-click Terminal.
By pressing cmd + space then type terminal and press enter.

The terminal should now be open:

Step 1.1: To use intel-based software, you'll need Rosetta2. Most of you should already have it installed for varied reasons. If you don't simply run the following line in the terminal:

softwareupdate --install-rosetta

This will launch the rosetta installer and you’ll have to agree to a license agreement.

Step 2: To install Homebrew x86 version, aka ibrew for MacOS, copy and paste the following line in the terminal:

arch -x86_64 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

Step 2.1: Sometimes it's necessary to install xcode command line utils. To do so, do the following command before installing homebrew:

xcode-select --install

Step 3: Add an alias with ibrew to your $PATH

echo 'alias ibrew="arch -x86_64 /usr/local/bin/brew"' >> ~/.zshrc

Step 4: Activate the alterations done to .zshrc

source ~/.zshrc

Step 5: Install python 3.7 with ibrew

ibrew install [email protected]

Step 6: Add python 3.7 to $PATH

export PATH="/usr/local/opt/[email protected]/bin:$PATH" >> ~/.zshrc

Step 7 Re-activate the alterations done to .zshrc

source ~/.zshrc

Ubuntu Setup

So you're using Ubuntu, hun? Well, kudos to you. You just need to install a couple of packages.

Step 1: Open a terminal and check what version of Python you have by using the command below. If your version is Python 3.7.x (x = any number), you can skip to step 2, otherwise continue with step 1.1 and 1.2

python3.7 --version

Step 1.1: Run the following commands to setup Python 3.7 (if you get an error with this command, check this ):

sudo add-apt-repository ppa:deadsnakes/ppa

Step 1.2: Run the following commands to install Python 3.7

sudo apt update && sudo apt install python3.7 -y

Step 2 Run the following command to get pip and venv:

sudo apt update && sudo apt upgrade && sudo apt install python3-pip python3.7-venv -y

Why do we install these?

We'll be using pip which is the reference Python package manager. You should always use a virtual environment to install python packages. We'll use venv to set them up.

Setup for all Operating Systems

Creating a Python Virtual Environment

Bellow are the instructions that are enough to get the setup done and get you up and running :) You can also follow this guide for a more in depth set of instructions that accomplish exactly the same thing.

⚠️ You should always be using a virtual environment to install python packages. ⚠️ We'll use venv to set them up.

To install and update packages, we'll be using pip which is the reference Python package manager.

Step 1 Start by installing ensuring pip, setuptools, and wheel are up to date:

python3.7 -m pip install --user --upgrade pip setuptools wheel

Step 2 Create a virtual environment with the name slu00

python3.7 -m venv ~/.virtualenvs/slu00

Step 3 Activate the environment

source ~/.virtualenvs/slu00/bin/activate

Note: after you activate your virtual environment you should see at the leftmost of your command line the name of your virtual environment surrounded by parenthesis, like this:

mig@my-machine % source ~/.virtualenvs/slu00/bin/activate
(slu00) mig@my-machine %

And you're able to make sure your virtual environment is active using the which command (it outputs the location of your virtual environment's python installation):

(slu00) mig@my-machine % which python
/Users/mig/.virtualenvs/slu00/bin/python

Step 4 Now update pip.

(slu00) pip install -U pip

Setup Git and GitHub

Having a GitHub account and knowing the basics of committing and pushing changes are mandatory for this academy.

⚠️If you don't have a GitHub account, complete the following steps:

Sign up for a GitHub account if you don't already have one.

⚠️If you have a GitHub account but git is not set up in your system, complete the following steps:

Setup your Workspace Repository

The workspace directory/repository is where you will place everything you are working on, solve exercises, make changes to files, etc. In this academy that is a requirement as it is how you will make your work available to us.

Creating the Workspace

Log into GitHub
Create a new private GitHub repository called batch5-workspace, see Creating a new repository. IMPORTANT The repo MUST be named batch5-workspace! If you name it anything else, you will be unable to submit any of your work!
1. You need to explicitly select Private - This is your work and nobody else's. You will be graded based upon the merits of what you are able to do here so this should not be open to the world while you are working on it. Maybe after the course is completed, you can open-source it but not this time.
2. Initialize with a README. This is mostly just so that you don't initialize an empty repo.
3. Add a Python .gitignore. This step is insanely important. If you don't do this, you may end up checking things into the repo that make it un-gradeable by our grading system. ADD THE .gitignore PLEASE!!!! <--- 4 * ! isn't enough

Add a Deploy Key to your Repository

Since the repository is private you will have to explicitly give access so that our grading system can fetch the repository. To do this you will be adding a deploy key to the repository, which we provide to you in our Portal.

Head on to the Portal
Log in with your GitHub account
Go to your profile and copy the deploy key (including the ssh-rsa part)
Go back to the repository you have just created
Go to Settings > Deploy Keys
Click "Add deploy key" (no need to grant Write Access)
Give it a recognizable name like "grader" and paste the key from the Portal

Cloning the Workspace

Open a Terminal or Git Bash, the next steps are on this terminal
Clone your <username>/batch5-workspace repository

If you're not sure where to clone the repository in, you can create a ~/projects folder, and clone it there
Clone the students repository If you have your ssh keys set up as instructed:

git clone [email protected]:<username>/batch5-workspace.git

else

git clone https://github.com/<username>/batch5-workspace.git

Get the Learning Material

You will be cloning the batch5-students repository. All of the learning material you need will be made available on this repo as the academy progresses.

Open a Terminal or Git Bash, the next steps are on this terminal
Clone the students repository batch5-students

git clone https://github.com/LDSSA/batch5-students.git

Or if you have your ssh keys set up:

git clone [email protected]:LDSSA/batch5-students.git

Running and Submitting a Learning Unit

In the batch5-students repository that you just cloned there is a sample learning unit. It's used to give instructors guidelines to produce the learning units. We are also using it to ensure that you are able to run and submit a learning unit.

So go ahead and copy the sample directory sample/SLU00 - LU Tutorial from the batch5-students repository to your repository (named batch5-workspace).

The grader only requires you to have the contents in a directory starting with the learning unit's ID, but we highly advise to keep the same directory structure as the students repository. All learning units are organized as:

<specialization ID> - <specialization name>/<learning unit ID> - <learnin unit name>

Doing so will help you keep organized and ease copying data from the students repository to yours.

Working on the Learning Unit

All learning units come as a set of Jupyter Notebooks (and some links to presentations). Notebooks are documents that can contain text, images and live code that you can run interactively.

In this section we will launch the Jupyter Notebook application. The application is accessed through the web browser.

Once you have the application open feel free to explore the sample learning unit structure. It will give you a handle on what to expect and what rules the instructors follow (and the effort they put) when creating a learning unit.

So let's start the Jupyter Notebook app:

Activate your virtual environment

source ~/.virtualenvs/slu00/bin/activate

Enter the Learning unit directory in your workspace directory (batch5-workspace).

Note: It is VERY IMPORTANT that you ALWAYS work on the files on your batch5-workspace repository, and NEVER work on files that are in your batch5-students repository!
```
cd ~/projects/batch5-workspace/sample/"SLU00 - LU Tutorial"
```
Installing the necessary packages
```
pip install -r requirements.txt
```
Run the jupyter notebook If you are running WLS on Windows 10 run the following:
```
jupyter notebook --NotebookApp.use_redirect_file=False
```

else:

```bash
jupyter notebook
```

When you run the jupyter notebook command, you should see something similar to this in your terminal: Your browser should pop up with Jupyter open, however, if this does not happen, you can simply copy the link you see on your terminal (the one that contains localhost) and past it in your browser's address bar:

Note: If you see these scarry looking error messages, don't worry, you can just ignore them.

The Exercise Notebook

Make sure you open and go through the Learning Notebook first.

Every learning unit contains an exercise notebook with exercises you will work on. So let's have a look at the sample Learning Unit.

On the Jupyter Notebook UI in the browser open the exercise notebook
Follow the instructions provided in the notebook

Besides the exercises and the cells for you to write solutions you will see other cells with a series of assert statements. This is how we (and you) will determine if a solution is correct. If all assert statements pass, meaning you dont get an AssertionError or any other kind of exception, the solution is correct.

Once you've solved all of the notebook we recommend the following this simple checklist to avoid unexpected surprises.

Save the notebook (again)
Run "Restart & Run All"
At this point the notebook should have run without any failing assertions

If you want to submit your notebook before it is all the way done to check intermediate progress, feel free to.

If you are able to go through the entire process and get a passing grade on the sample LU you'll have a good understanding of the same flow that you'll use for all LUs throughout the academy.

Commit and Push

Now you have worked on the sample learning unit and you have some uncommitted changes. It's time to commit the changes, which just means adding them to your batch5-workspace repository history, and pushing this history to you remote on GitHub.

Using the terminal commit and push the changes

git add .
git commit -m 'Testing the sample notebook'
git push

Grading

Go to the Portal and select the learning unit
Select "Grade"
After grading is complete you should have 20/20
If everything passes locally but the grader doesn't give you the excepted output head to out troubleshooting

Learning Unit Workflow

You will need to follow this workflow whenever new learning materials are released.

Learning units will be announced in the academy's #announcements channel. At this point they are available in the batch5-students repository. A new Learning Unit is released every Monday, and its solutions are then released the next Monday.

The steps you followed during the initial setup are exactly what you are going to be doing for each new Learning Unit. Here's a quick recap:

If you haven't, activate your virtual environment
```
source ~/.virtualenvs/slu00/bin/activate
```
Once a new Learning Unit is available, pull the changes from the batch5-students repo:
- enter the ~/projects/batch5-students/ using the cd command, then use the git pull command:
```
cd ~/projects/batch5-students/
git pull
```

Copy the Learning Unit to your batch5-workspace repo

cp -r ~/projects/batch5-students/"<specialization ID> - <specialization name>"/"<learning unit ID> - <learnin unit name>" ~/projects/batch5-workspace/"<specialization ID> - <specialization name>"

For example, for the S01 - Bootcamp and Binary Classification and SLU01 - Pandas 101, it would look like this:

cp -r ~/projects/batch5-students/"S01 - Bootcamp and Binary Classification"/"SLU01 - Pandas 101" ~/projects/batch5-workspace/"S01 - Bootcamp and Binary Classification"

Create a new virtual environment for the Learning Unit you'll be working on.
- To do this you will run the following command:
```
python3.7 -m venv ~/.virtualenvs/<learning unit ID>
```
- and you would replace the <learning unit ID> with the learning unit ID, such that for SLU01, for example, the command would be:
```
python3.7 -m venv ~/.virtualenvs/slu01
```

Activate your virtual environment

source ~/.virtualenvs/slu01/bin/activate

Install the python packages from requirements.txt for the specific Learning Unit (you must do this for each Learning Unit, and there are multiple Learning Units in a Specialization)
```
pip install -r ~/projects/batch5-workspace/"<specialization ID> - <specialization name>"/"<learning unit ID> - <learnin unit name>"/requirements.txt
```
For example, for the S01 - Bootcamp and Binary Classification and SLU01 - Pandas 101, it would look like this:
```
pip install -r ~/projects/batch5-workspace/"S01 - Bootcamp and Binary Classification"/requirements.txt
```
Change to the batch5-workspace dir
```
cd ~/projects/batch5-workspace
```
Open Jupyter Notebook
```
jupyter notebook
```
Work
Once all tests pass or once you're happy, save your work, close the browser tab with the Jupyter Notebook, close the terminal and open a new terminal

Then commit the changes and push

cd ~/projects/batch5-workspace
git add .
git commit -m "Worked on SLU01 exercises"
git push

Profit

Updates to Learning Units

As much as we try and have processes in place to prevent errors and bugs in the learning units some make it through to you. If the problem is not in the exercise notebook you can just pull the new version from the students repo and replace the file. The problem is if the correction is in the exercise notebook, you can't just replace the file your work is there and you'll lose it!

When a new version of the exercise notebook is released (and announced) two things will happen. If you submit an old version of the notebook it will be flagged as out of date and not graded. You will have to merge the work you've already done into the new version of the notebook.

At the moment our suggestion to merge the changes is:

Rename the old version
Copy the new exercise notebook over
Open both and copy paste your solutions to the new notebook

We understand it's not ideal and are working on improving this workflow using nbdime. If you are comfortable installing Python packages you can try it out, but we offer no support for this at the moment.

Add your Slack ID to the Portal

In your Profile in the Portal, besides your GitHub Handle, you should add your SlackID. You can find information on how to find it following this link

Help

During the academy you will surely run into problems and have doubts about the material. We provide you with some different channels to ask for help.

Learning Unit

If you feel something is not clear enough or there is a bug in the learning material please follow these steps. Remember, there is no such thing as a dumb question, and by asking questions publicly you will help others!

If you have more conceptual questions about the materials or how to approach a problem you can also reach out to the instructors on slack. You can find the main contact for the learning unit in the Portal this instructor can help you out or redirect you to someone that is available at the moment.

Portal

Are you getting different results locally than what you are getting in the Portal? If so we will first ask to do a bit of troubleshooting.

Ensure that you have saved the changes in the notebook
Ensure that you have committed and pushed the changes
Ensure that you are not using packages that are not present in the original requirements.txt file (changes to this file or your local environment have no effect)
In the learning unit page in the Portal you are able to download the exercise notebook with the results of the grader by clicking your grade, have a look to figure out what went wrong. If none of these steps helped go ahead and open a support ticket for the portal here.

Is the Portal down or acting out in some unexpected way? Then please open a support ticket for the portal here.

Troubleshooting

When I open Windows Explorer through Ubuntu it goes to a different folder than in the guide
Ubuntu on Windows 10 high CPU usage crashes
When I pull from the batch5-students repository I get an error
When I try to open jupyter notebook I get an error
When I use the cp command the > sign appears and the command does not execute
When setting up python 3.7 I get an error
Nothing happens when I type my password
I still have a NotImplemented error
I get an error when creating the virtual environment
Checksum verification failed
My problem is not listed here what should I do?
Tutorial videos from Prep Course 2020

When I open Windows Explorer through Ubuntu it goes to a different folder than in the guide

Please make sure:

you are running the command explorer.exe . including the dot at the end.
you are running Windows 10 version 1909 or newer.

Ubuntu on Windows 10 high CPU usage crashes

First please make sure you are running Windows 10 version 1909 or newer.
Then, try following these steps

When I pull from the `batch5-students` repository I get the error

error: Your local changes to the following files would be overwritten by merge:
<some files>
Please commit your changes or stash them before you merge.
Aborting

git is telling us that changes were made by you to the files on the ~/projects/batch5-students folder, and is not pulling the changes made by the instructors because they would override the changes that you made there. To fix this do the following:

make sure that any change you made to the files on ~/projects/batch5-students (that you want to not lose) is saved in your ~/projects/batch5-workspace repository (see https://github.com/LDSSA/batch5-students#updates-to-learning-units for how to do this), and if you don't want to keep the changes you made to these files, just continue on to the next step
go to the ~/projects/batch5-students folder and run:
```
cd ~/projects/batch5-students
git stash
```
now you can pull from the batch5-students repository:
```
git pull
```

When I try to open `jupyter notebook` I get the error

migs-MBP% jupyter notebook
zsh: command not found: jupyter

Before opening jupyter notebook activate your virtual environment:

source ~/.virtualenvs/slu00/bin/activate

When I use the `cp` command the `>` sign appears and the command does not execute

cp -r ~/projects/batch5-students/"S01 - Bootcamp and Binary Classification"/"SLU01 - Pandas 101" ~/projects/batch5-workspace/"S01 - Bootcamp and Binary Classification"
>

Make sure to use this type of quotes " and not these ones “.

When setting up python 3.7 I get an error

When I run this command:

sudo add-apt-repository ppa:deadsnakes/ppa

I get this error:

W: GPG error: http://apt.postgresql.org/pub/repos/apt focal-pgdg InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 7FCC7D46ACCC4CF8

Solution: Take the id in front of NO_PUBKEY (in my case its 7FCC7D46ACCC4CF8) and run the following command:

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 7FCC7D46ACCC4CF8

Nothing happens when I type my password

In step two it asks me for the computer password. However, I am not being able to write anything

Solution: When you write your password you might not get any visual feedback and that's okay! Write it as normal and hit enter when you're done!

I still have a NotImplemented error

I've completed the exercise in the Exercise Notebook but when I run the cell I get a NotImplementedError.

Solution: The raise NotImplementedError() are added to the exercise cell as a placeholder for where you're supposed to add your solution/code. It is meant to be removed!

I get an error when creating the virtual environment

I ran python3.7 -m venv ~/.virtualenvs/slu00, but got the following error:

The virtual environment was not created successfully because ensurepip is not available.

This can happen if either you skipped the installation of python-pip, or the version of the python you're calling doesn't have python pip installed.

As we're using python3.7 for this academy, and if you've followed all the steps in this README correctly, you should be able to create the virtual environment with:

python3.7 -m venv ~/.virtualenvs/slu00

Checksum verification failed

I'm getting "checksum verification failed" on a portal submission.

Some metadata can change when you open your jupyter notebook using vscode. Try running the notebooks from start to end in Jupyter and resubmit.

My problem is not listed here what should I do?

If the above steps didn't solve the problem for you, please contact us on Slack or open an issue in this repo.

Tutorial videos from Prep Course 2020

If you want a visual guide, you can look at the tutorial videos from the Prep Course of year 2020.

⚠️ These videos are out of date, and should only be used as a visual guide of what the setup process looks like. The steps you should follow are detailed in this document.

Other

If your problem doesn't fit in any of the previous categories head over to slack and ask. Someone will surely point you in the right direction.

If you're looking for some specific part of our organization head over to the Member Directory and search for the area of responsibility you're looking for.

Name		Name	Last commit message	Last commit date
Latest commit History 180 Commits
S01 - Bootcamp and Binary Classification		S01 - Bootcamp and Binary Classification
S02 - Data Wrangling		S02 - Data Wrangling
S03 - Time Series		S03 - Time Series
S04 - Text Classification		S04 - Text Classification
S05 - Recommender Systems		S05 - Recommender Systems
S06 - DS in the Real World		S06 - DS in the Real World
assets		assets
guides		guides
sample/SLU00 - LU Tutorial		sample/SLU00 - LU Tutorial
LICENSE		LICENSE
README.md		README.md

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wilsonramos1/batch5-students

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