Data Description

MS03-001-G

Undergraduate course providing the fundamental mathematical intuition to understand, describe, and analyse data in finance, economics, and management.

1 Course description

Data Description introduces the basic statistical tools corresponding to the first stage of data analysis: numerical summaries and graphical representation of data, computed hands-on in Excel. By the end of the course, you will be able to take a raw column of business data — sales, weather, customer counts — and turn it into frequency tables, charts, and summary indicators that support a real business decision. A single running case study, “The Wandering Fork” (a fictional food-truck business), carries every worked example from Module 2 onward, so results build on each other across the whole course.

The course has 10 modules of 3 hours (30 contact hours). Each module follows the same pattern: a business question; concrete activities on a few numbers you compute by hand, before any formula; a notation decoder that reads every formula aloud and links each symbol to those numbers; a worked example on the shared dataset; a short derivation; exercises by hand with collapsible solutions; common mistakes; a short exit check; and an Excel lab done as homework at the end of the module page, so class time goes to concepts, notation and practice.

2 Curriculum placement

This course is the natural sequel to Mathematical Fundamentals (MS01, teaching/mathematical-fundamentals/) and Mathematical Analysis (MS02, teaching/mathematical-analysis/), and it is itself the prerequisite refresher for Inferential Statistics (MS04, teaching/decision-making-stat/), whose own Session 6 explicitly revisits the numerical summaries introduced here before moving on to confidence intervals and hypothesis testing.

3 Learning objectives

By the end of this course, you will be able to:

  1. Identify the type and level of measurement of a variable and say which summaries it allows (Module 1).
  2. Build the right frequency table and chart for a qualitative or a quantitative variable, by hand in class and in Excel with COUNTIFS, PivotTables and charts (Modules 2–3).
  3. Compute and interpret central tendency, position, dispersion and shape, by hand in class and in Excel at home with AVERAGE, MEDIAN, QUARTILE.EXC, STDEV.S and SKEW (Modules 4–7).
  4. Build and read a contingency table and a scatter plot for two variables observed together (Module 8).
  5. Compute and interpret covariance, correlation and a simple linear regression line, by hand in class and in Excel at home with CORREL, SLOPE, INTERCEPT and RSQ (Module 9).
  6. Run a complete descriptive analysis of a new dataset and explain it to a business audience (Module 10).

4 Module outline

Part # Module Topic Hours
I. Organising data 1 Module 1 What is statistics? Data types and measurement 3
I. Organising data 2 Module 2 Organising qualitative data: frequency tables and charts 3
I. Organising data 3 Module 3 Organising quantitative data: histograms and cumulative curves 3
II. Summarising one variable 4 Module 4 Measures of central tendency 3
II. Summarising one variable 5 Module 5 Measures of position: quartiles, percentiles, boxplot 3
II. Summarising one variable 6 Module 6 Measures of dispersion: variance, standard deviation, CV 3
II. Summarising one variable 7 Module 7 Shape of distributions and univariate review 3
III. Two variables and synthesis 8 Module 8 Bivariate analysis I: contingency tables and scatter plots 3
III. Two variables and synthesis 9 Module 9 Bivariate analysis II: covariance, correlation, regression 3
III. Two variables and synthesis 10 Module 10 Synthesis, review and mock exam 3

Part I — Organising data (Modules 1–3, 9h) · Part II — Summarising one variable (Modules 4–7, 12h) · Part III — Two variables and synthesis (Modules 8–10, 9h).

Reference pages: the dataset (download the files and the data dictionary), the glossary of every symbol, and the Excel functions page with English and French function names.

5 Assessment

Examination % of overall grade Assessment mode Duration Covers
Midterm Exam (ME) 35% Individual written, MCQ component 45 min Modules 1–5
Final Exam (FE) 65% Individual written, MCQ component 90 min Modules 1–10

Module 10 closes the course with an original mock exam in the same format; each solution names the module to revise.

6 Personal work

Volume: 20 hours. Complete the exercises associated with each part of the course (Organising Data, Summarising One Variable, Two Variables and Synthesis) between modules — every module page’s collapsible exercises are designed for exactly this.

Each module ends with an Excel homework (about 50–60 minutes), debriefed during the first 10 minutes of the next module (Module 10’s homework is a self-check against the solutions on the page). That is about 10 of the 20 hours; the rest is by-hand revision with the exercises and exit checks.

7 Conventions

Several textbook formulas differ slightly; this course always uses the following (see also the glossary).

Question Convention Excel
Quartiles and percentiles Position \((n+1)\times p\) with interpolation QUARTILE.EXC, PERCENTILE.EXC
Variance and standard deviation Sample formulas (\(n-1\)) by default VAR.S, STDEV.S
Skewness Adjusted Fisher–Pearson coefficient SKEW
Kurtosis Conceptual only: no formula —
Excel function names English locale; French names on the functions page —