FIT1043 · Introduction to Data Science
Data Visualisation & Descriptive Statistics
Week 4 of Monash FIT1043 Introduction to Data Science pairs descriptive statistics — mean, median, mode, range, standard deviation and quartiles — with choosing an appropriate visualisation (line, histogram, boxplot, bar, pie, scatter). It teaches you to summarise a variable numerically and to pick the chart that answers a given question, then to read and critique what a chart shows. These are high-yield exam skills: short-answer questions ask for a statistic or the right chart, and Test 1 (Weeks 1-4) draws heavily on them.
What this chapter covers
- 01Why visualise: get a feel for the data before and alongside numeric summary
- 02Descriptive statistics: mean, median, mode; range, variance, standard deviation; quartiles (25%/50%/75%)
- 03Choosing a chart: line plot (trend), histogram (distribution of one variable), boxplot (five-number summary + outliers)
- 04Bar chart (compare categories), pie chart (parts of a whole), scatter plot (relationship between two variables)
- 05Reading a boxplot: median line, interquartile-range box, whiskers and outliers; grouping by a category
- 06Histograms as a special kind of bar chart; bins control granularity
- 07df.describe(): count, mean, std, min, quartiles, max in one call
- 08Group-by then chart: count children (<18) vs adults (>=18) per class, then bar-chart
Descriptive statistics of a small sample and choosing a chart
- +1Statistics: mean = (1+2+2+2+3)/5 = 10/5 = 2. Median = the middle value of the sorted list {1,2,2,2,3} = 2. Mode = the most frequent value = 2. (All three coincide here because the data is symmetric around 2.)
- +1Charts: the distribution of one numeric variable is best shown by a HISTOGRAM (a special bar chart whose bins show frequency); the relationship between two numeric variables is best shown by a SCATTER PLOT (one point per observation, x vs y).
Key terms
- Mean / median / mode
- The three centre measures: mean = arithmetic average; median = middle value of the sorted data; mode = most frequent value. For {1,2,2,2,3} all equal 2.
- Quartiles / IQR
- The 25%, 50% (median) and 75% split points of sorted data; the interquartile range (Q3 - Q1) is the middle-50% spread and is the box in a boxplot.
- Standard deviation
- A measure of spread: the typical distance of values from the mean (the square root of the variance). Range is the simpler spread measure (max - min).
- Histogram
- A chart of the distribution of one numeric variable, splitting it into bins and showing each bin's frequency; a special type of bar chart where bin width controls granularity.
- Boxplot
- A five-number-summary chart: the box spans Q1 to Q3 with a line at the median, whiskers reach the non-outlier range, and points beyond mark outliers; can be grouped by a category.
- Scatter plot
- A chart with one point per observation plotting two numeric variables against each other, used to reveal the relationship (and any correlation) between them.
Data Visualisation & Descriptive Statistics FAQ
Which chart should I use for which question?
Match the chart to the intent: a histogram for the distribution of one numeric variable, a scatter plot for the relationship between two numeric variables, a boxplot for a five-number summary with outliers, a bar chart to compare categories, a pie chart for parts of a whole, and a line plot for a trend over an ordered axis. The exam often gives a scenario and asks you to pick and justify.
Is a histogram a bar chart?
Effectively yes — the unit teaches that a histogram is a special type of bar chart. The key difference is that a histogram shows the frequency of a numeric variable across bins (so bin width matters and bars are usually adjacent), whereas a bar chart compares distinct categories.
How do I read a boxplot?
The box spans the interquartile range (Q1 to Q3), the line inside is the median, the whiskers extend to the last non-outlier values, and any points beyond the whiskers are outliers. Roughly a quarter of the data sits below the box, a quarter inside the lower box half, a quarter inside the upper half, and a quarter above. You can also group a boxplot by a category to compare distributions.
What does df.describe() give me?
For each numeric column it returns count, mean, standard deviation, minimum, the 25%/50%/75% quartiles and maximum in one call — a fast numeric summary to pair with a chart. It is the quickest way to get the mean, median (the 50% value) and spread before you visualise.
Can AI help me choose visualisations in FIT1043?
Yes. Sia can take a scenario, suggest the appropriate chart and explain why, compute descriptive statistics with you, and quiz you on reading a boxplot or histogram, step by step. It explains the method and checks your reasoning; it does not do your assessment for you, and Monash academic-integrity rules apply. Confirm details on Moodle.
Exam move
Build two quick-reference lists: one mapping each chart to the question it answers (distribution -> histogram, relationship -> scatter, five-number summary -> boxplot, categories -> bar, parts of a whole -> pie, trend -> line), and one for the statistics (how to compute mean, median, mode, range, std and read quartiles off describe()). Practise on the Week 3-4 applied solutions by computing a statistic by hand and predicting which chart the code produces. Because Week 4 is inside Test 1's Weeks 1-4 scope and recurs on the final, rehearse writing a one-line justification for a chart choice — that justification is where the short-answer marks are.
Working through Data Visualisation & Descriptive Statistics in FIT1043? Sia is AskSia’s AI Information Technology tutor — ask any FIT1043 Data Visualisation & Descriptive Statistics question and get a clear, step-by-step explanation grounded in how FIT1043 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
