Missed two weeks of classes? A catch-up triage plan

First, the three things to do today

1. 1Message your mentor or batch coordinator. Say what you missed and when you can resume. A short note works: "I missed sessions 9–12 due to a family emergency. I am attending from Saturday. Which topics from those sessions do the next two classes build on?" That last question saves you hours.
2. 2Download or bookmark every missed recording, notebook and assignment brief in one folder, so you are not hunting for them later.
3. 3Attend the very next live class, even underprepared. You will follow more than you expect, and it tells you which gaps actually hurt.

Sort the missed topics

Put every missed topic in one of three buckets:

Foundations come first, then assessed work, then depth. If you are unsure which bucket something belongs in, that is exactly the question for your mentor.

Order the foundations by dependency

Within the foundation bucket, some topics depend on others. Covering them out of order wastes time. For a short list you can work this out by hand; for a longer one, Python's standard library can sort it for you.

from graphlib import TopologicalSorter

# Hypothetical missed topics -> the topics each depends on
depends_on = {
    "logistic regression": {"train/test split", "feature scaling"},
    "confusion matrix": {"logistic regression"},
    "precision and recall": {"confusion matrix"},
    "feature scaling": {"pandas basics"},
    "train/test split": {"pandas basics"},
    "pandas basics": set(),
}

# Estimated catch-up hours for each (recall attempt + targeted rewatch + exercise)
hours = {
    "pandas basics": 1.0,
    "train/test split": 1.0,
    "feature scaling": 1.0,
    "logistic regression": 2.5,
    "confusion matrix": 1.5,
    "precision and recall": 1.5,
}

order = list(TopologicalSorter(depends_on).static_order())
running = 0.0
for topic in order:
    running += hours[topic]
    print(f"{topic:<22} {hours[topic]:>4} h   cumulative {running:>4} h")

assert order[0] == "pandas basics"
assert order.index("confusion matrix") < order.index("precision and recall")
assert running == 8.5

In this hypothetical case, 8.5 hours of focused work covers the foundations. Spread over a week alongside current classes, that is realistic. Rewatching the same sessions in full might have taken twelve hours or more and still left you unable to do the exercises.

A ten-day catch-up plan

Assume you can find about 1.5 extra hours on weekdays and 3 hours on one weekend day, on top of attending live classes.

Depth topics go on a list for the following fortnight. Some of them you will find you never need.

How to catch up on a single missed session quickly

For each foundation topic, follow the same short loop instead of watching the full recording:

1. 1Read the class notebook or slides first — 10 minutes — to see what was covered.
2. 2Watch only the segments where code was written or a new idea introduced. Use the chapter markers or scrub through at speed to find them.
3. 3Close everything and redo the key exercise from a blank notebook.
4. 4Note one question you still have.

The redo is what tells you whether you have caught up or only watched.

Talking to your mentor about the missed assignment

Ask, rather than assume, what the rules are. Useful questions:

• Can I submit the missed assignment late, and is there a final date?
• If I have to choose, which of the missed exercises matters most for the capstone?
• Is there a doubt session or lab this week where I could get through the foundations faster?

If the course also has optional in-person labs, a lab day can be an efficient place to clear several doubts at once.

Preventing the next gap

A two-week gap often starts as one missed week that nobody noticed. Two habits help:

• Keep a weekly progress check — how many sessions attended, exercises completed from blank, doubts asked. Our weekly progress measure sets this out.
• When you know a busy period is coming — an audit, a wedding, exams — tell your mentor in advance and ask what to prioritise.

For using recordings efficiently once you are back on track, see how to use class recordings without rewatching everything. Because every live online session of the Data Science programme is recorded, a two-week gap there is recoverable rather than terminal.