Deepening your knowledge

Completing the basic assignments that demonstrate you can apply what we cover in class to your own data, is enough for a B, to earn an A you need to extend your knowledge.

You have some different pathways to earning an A.

import yaml as yml
import pandas as pd
import os
pd.set_option('display.max_colwidth', None)


def yml_df(file):
    with open(file, 'r') as f:
        file_unparsed = f.read()

    file_dict = yml.safe_load(file_unparsed)
    return pd.DataFrame(file_dict)

outcomes_df = yml_df('../_data/learning_outcomes.yml')
outcomes_df.set_index('keyword',inplace=True)
schedule_df = yml_df('../_data/schedule.yml')
schedule_df.set_index('week', inplace=True)
# schedule_df = pd.merge(schedule_df,outcomes_df,right_on='keyword',  left_on= 'clo')
rubric_df = yml_df('../_data/rubric.yml')
rubric_df.set_index('keyword', inplace=True)
rubric_df.replace({None:'TBD'},inplace=True)
rubric_df.rename(columns={'mastery':'Level 3',
              'compentent':'Level 2',
              'aware':'Level 1'}, inplace=True)

assignment_dummies  = pd.get_dummies(rubric_df['assignments'].apply(pd.Series).stack()).groupby(level=0).sum()
assignment_dummies['# Assignments'] = assignment_dummies.sum(axis=1)
col_rename = {float(i):'A' + str(i) for i in range(1,14)}
assignment_dummies.rename(columns =col_rename,inplace=True)

portfolio_dummies  = pd.get_dummies(rubric_df['portfolios'].apply(pd.Series).stack()).groupby(level=0).sum()
col_rename = {float(i):'P' + str(i) for i in range(1,5)}
portfolio_dummies.rename(columns =col_rename,inplace=True)


rubric_df = pd.concat([rubric_df,
                      assignment_dummies,
                      portfolio_dummies],axis=1)

assignment_cols =  ['A'+ str(i) for i in range(1,14)] + ['# Assignments']

portfolio_cols = [ 'Level 3'] + ['P' + str(i) for i in range(1,5)]
portfolio_df = rubric_df[portfolio_cols]

Extending Assignments¶

Starting in week 3 it is recommended that you spend some time each week working on extensions to earn level 3 on the skills.

Use the feedback you get on assignments to inspire your extensions.

To submit these, submit the work to a separate <assignment>extended branch so for assignment 2 extension, submit to assignment2extended.

You should not extend every assignment since skills overlap and relate to one another.

I recommend making an issue that is a plan and asking for feedback before you work on extensions, so that you do not do too much extra work.

Some ideas:

• extend A5 to add in a database source or combine the data in new ways, plus do extended EDA to earn level 3 for: access, construct, prepare, visualize and summarize

• extend one of A7-9 to have experiments that more carefully analyze and compare different models at a task for one of the ml tasks (classification, regression, clustering) and visualize and summarize

Deployment and Distribution¶

Instead of earning all 15 level 3s you can earn any 10 plus one of the following:

• transform your portfolio to a publish-able portfolio by making it a jupyter book (mostly set up already, just need to clean up)

• share a model and use a classmate’s model with huggingface (making the model card would count for process and fitting it could count for one of the 3 tasks)

if you go this route, I recommend level 3 for:

• access (loading models from huggingface will count)

• python

• process (see process.md)

• summarize

• visualize

• evaluate

• one of: classification, regression, clustering

• optimize

• compare

• workflow

If your dataset of interest is hard to work with, images, or text, you might swap in representation instead of access

this means skipping level 3 for:

• prepare

• construct

• 2 of: classification, regression, clustering

• representation