PCED study plan

Set up a local Python environment and run a script from the command line without an IDE

Write 10 small functions that each take and return a value, using default and keyword arguments

Practice choosing between list, tuple, dict, and set for 8 described scenarios and justify each choice in one line

Write loops with break, continue, and else, plus a comprehension for filtering and mapping a list

Handle a missing file with try/except/finally and log a clear error message

Read a CSV with the csv module using both reader and DictReader, and write results back with DictWriter

Implement a function that validates required columns and reports missing ones by name

Compare CSV, JSON, and newline-delimited JSON for a described source and note trade-offs

Distinguish structured, semi-structured, and unstructured data across 6 given examples

Identify population versus sample and a biased sampling method in 3 short scenarios

Profile a messy dataset by counting nulls, blanks, duplicates, and type mismatches per column

Normalize inconsistent text values (case, whitespace, category synonyms) with a documented mapping

Convert columns to correct types and record every coercion failure instead of silently dropping it

Decide and justify a handling strategy for each missing-value pattern found

Write a cleaning log that states what changed, why, and how many rows were affected

Parse and format dates with datetime, including strptime, strftime, and timedelta arithmetic

Compute mean, median, mode, variance, and standard deviation with the statistics module and explain when each is appropriate

Use collections.Counter and defaultdict to produce frequency tables and grouped aggregates

Load the same data into a NumPy array and compute column means, medians, and standard deviations with axis arguments

Compare a pure-Python aggregation with the NumPy equivalent and confirm matching results

Compute and interpret correlations and simple group differences without overstating causation

Write a text-based summary that states the headline finding, the supporting numbers, and one caveat

Build a bar chart and a line chart with labeled axes, units, and a title that states the takeaway

Select the chart type for 6 described questions and justify each choice

Rewrite 3 vague findings into specific, quantified statements

Apply the principle of proportional ink to critique 3 misleading charts and describe the fix for each

Identify at least 5 cognitive or design biases that distort chart reading and give a countermeasure for each

Draft a consent and data-handling checklist covering purpose, minimization, retention, and anonymization

Anonymize a sample dataset by removing or hashing direct identifiers and note residual re-identification risk

Outline a narrative structure (context, conflict, resolution) for a 5-minute data presentation

Design a collection plan for a stated question, naming the unit of observation, fields, and collection method

Document data provenance and a refresh cadence for the dataset you have been using

Add automated validation checks that fail loudly on schema drift or out-of-range values

Handle encoding issues and delimiter edge cases in a second, differently formatted file

Re-run the full pipeline end to end and confirm it is reproducible from raw input

Assemble a 10-minute presentation with a single clear recommendation and supporting evidence

Anticipate and answer 5 likely stakeholder objections with data-backed responses

Time a full rehearsal and cut content until it fits the time limit

Complete a 40-question timed practice set and review every missed item by domain

Write a personal weak-area list with one corrective action per item