The data science life cycle

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Quick Questions

Shade in the A or B circle for each question.

1. Which list shows the five stages of the data science life cycle in the correct order?

A Define objective, acquire data, clean, analyse, communicate

B Acquire data, analyse, define objective, communicate, clean

2. What is the very first stage of the data science life cycle?

A Acquire data

B Define the objective

3. The data science life cycle is best described as...

A A straight line that you only follow once

B A repeating cycle that can be revisited

4. You start analysing your data and find many missing values. What is the best next step in the life cycle?

A Go back to the clean stage and fix them

B Skip ahead and communicate the results anyway

5. Which stage involves making charts and sharing what you found with an audience?

A Communicate insights

B Acquire data

Match the Pairs

Draw a line connecting each item on the left to its match on the right.

Left column		Right column
1Stage 1: Define the objective		ADecide the question you want to answer before touching any data
2Stage 3: Clean the data		BThe stages are out of order: you cannot analyse data before you have defined the objective, acquired the data and cleaned it.
3The life cycle has five stages in order, from objective to insights		CFix errors, remove duplicates and handle missing values so the data can be trusted
4It is a cycle, not a line: communicating insights can lead to a new question		Dcommunicate -> define (repeat)
5analyse -> acquire -> define -> clean -> communicate		ETreats the life cycle as a one-way line. Findings can send you back to an earlier stage, so the process repeats.
6Life cycle: define -> acquire -> clean -> analyse -> communicate -> END (never go back)		Fdefine -> acquire -> clean -> analyse -> communicate

Definitions

data science life cycle

Fill in the Blanks

Complete each line of code by filling in the missing parts.

Define -> _______ -> Clean -> Analyse -> Communicate

Define -> Acquire -> _____ -> Analyse -> Communicate

Analyse -> Communicate -> ______

______ runFiveStages() UNTIL questionAnswered

IF needsMoreData ____ GO BACK TO Acquire END IF

Spot the Error

Shade in the letter of the line that contains an error.

Question 1

A stage1 ← "define objective"

B stage2 ← "clean data"

C stage3 ← "clean data"

D stage4 ← "analyse data"

E stage5 ← "communicate"

Question 2

A FOR stage ← 1 TO 4

B DISPLAY stages[stage]

C NEXT stage

Question 3

A analyse_data()

B acquire_data()

C clean_data()

D analyse_data()

Question 4

A communicate_insights()

B IF new_question THEN

C STOP

D ENDIF

Question 5

A analyse_data()

B IF results_look_wrong THEN

C check_data_quality()

D GOTO communicate_insights

E ENDIF

Answer Key

The data science life cycle

Section A: Quick Questions

A — Define objective, acquire data, clean, analyse, communicate

B — Define the objective

B — A repeating cycle that can be revisited

A — Go back to the clean stage and fix them

A — Communicate insights

Section B: Match the Pairs

Stage 1: Define the objective → Decide the question you want to answer before touching any data

Stage 3: Clean the data → Fix errors, remove duplicates and handle missing values so the data can be trusted

The life cycle has five stages in order, from objective to insights → define -> acquire -> clean -> analyse -> communicate

It is a cycle, not a line: communicating insights can lead to a new question → communicate -> define (repeat)

analyse -> acquire -> define -> clean -> communicate → The stages are out of order: you cannot analyse data before you have defined the objective, acquired the data and cleaned it.

Life cycle: define -> acquire -> clean -> analyse -> communicate -> END (never go back) → Treats the life cycle as a one-way line. Findings can send you back to an earlier stage, so the process repeats.

Section C: Definitions

data science life cycle — A repeating cycle of five stages: define the objective, acquire data, clean it, analyse it, communicate insights. Findings can send you back to an earlier stage.

Section D: Fill in the Blanks

Define -> Acquire -> Clean ->
Analyse -> Communicate

Define -> Acquire -> Clean ->
Analyse -> Communicate

Analyse -> Communicate -> Define

REPEAT
  runFiveStages()
UNTIL questionAnswered

IF needsMoreData THEN
  GO BACK TO Acquire
END IF

Section E: Spot the Error

B — The second stage of the life cycle is acquiring data. Cleaning comes third, so stage2 should be "acquire data".

A — The life cycle has five stages, so the loop must run from 1 TO 5. Stopping at 4 skips the last stage, which is communicating insights.

A — The cycle starts by defining the objective. Line 1 should be define_objective(). You cannot analyse data before you know what question you are answering.

C — The life cycle repeats. When communicating raises a new question, the program should go back to define_objective instead of stopping.

D — If the results look wrong, you should return to an earlier stage such as clean_data to fix the problem. Communicating wrong results would mislead the audience.