You can put all your data into your google drive and then mount drive. This is how I have done it. Let me explain in steps.

Step 1: Transfer your data into your google drive.

Step 2: Run the following code to mount you google drive.

# Install a Drive FUSE wrapper.
# https://github.com/astrada/google-drive-ocamlfuse
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse



# Generate auth tokens for Colab
from google.colab import auth
auth.authenticate_user()


# Generate creds for the Drive FUSE library.
from oauth2client.client import GoogleCredentials
creds = GoogleCredentials.get_application_default()
import getpass
!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL
vcode = getpass.getpass()
!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}


# Create a directory and mount Google Drive using that directory.
!mkdir -p My Drive
!google-drive-ocamlfuse My Drive


!ls My Drive/

# Create a file in Drive.
!echo "This newly created file will appear in your Drive file list." > My Drive/created.txt

Step 3: Run the following line to check if you can see your desired data into mounted drive.

!ls Drive

Step 4:

Now load your data into numpy array as follows. I had my exel files having my train and cv and test data.

train_data = pd.read_excel(r'Drive/train.xlsx')
test = pd.read_excel(r'Drive/test.xlsx')
cv= pd.read_excel(r'Drive/cv.xlsx')

I hope it can help.

Edit

For downloading the data into your drive from the colab notebook environment, you can run the following code.

# Install the PyDrive wrapper & import libraries.
# This only needs to be done once in a notebook.
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials



# Authenticate and create the PyDrive client.
# This only needs to be done once in a notebook.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)



# Create & upload a file.
uploaded = drive.CreateFile({'data.xlsx': 'data.xlsx'})
uploaded.SetContentFile('data.xlsx')
uploaded.Upload()
print('Uploaded file with ID {}'.format(uploaded.get('id')))

Here are few steps to upload large dataset to Google Colab

1.Upload your dataset to free cloud storage like dropbox, openload, etc.(I used dropbox)
2.Create a shareable link of your uploaded file and copy it.
3.Open your notebook in Google Colab and run this command in one of the cell:

    !wget your_shareable_file_link

That's it!
You can compress your dataset in zip or rar file and later unizp it after downloading it in Google Colab by using this command:

    !unzip downloaded_filename -d destination_folder

Zip you file first then upload it to Google Drive.

See this simple command to unzip:

!unzip {file_location}

Example:

!unzip drive/models.rar