This applied Data Science project requires students to use Python to analyse passenger demand, ticket prices and airline revenues across the period 2021–2023. Two datasets are provided: one containing daily passenger counts and revenue or average-price information for 2021 and 2022, and another containing a representative sample of individual journeys made during 2023. The datasets may include domestic, European and intercontinental routes as well as standard/economy and business/club travel classes. 202526_SmallProject_semB Students must develop Python code that reads the assigned datasets and produces a series of analytical visualisations. A core requirement is to plot daily passenger numbers for economy and business classes across all three years, combining scatter plots with smoothed trend lines. The smoothing must be calculated using the first eight terms of a Fourier series through the supplied scrfft.py module. 202526_SmallProject_semB Further analysis examines changes in average ticket prices, including monthly comparisons across 2021, 2022 and 2023, together with annual average prices that incorporate revenue generated from additional services such as seat reservations and extra baggage. 202526_SmallProject_semB A fourth analysis is personalised according to the final digit of the student's ID. Depending on the allocated task, students may investigate weekly journey distributions, seasonal travel patterns, price-versus-distance relationships, business and economy class behaviour, additional-service revenues, weekend journeys or linear relationships between selected variables. Three additional numerical values, labelled X, Y and Z, must also be calculated according to the assigned scenario. 202526_SmallProject_semB The final report must include four figures, relevant mathematical formulae and a critical discussion of the findings. Students are expected to interpret trends in passenger numbers, prices, revenues, seasonal patterns and airline profitability rather than simply presenting calculations. The report is limited to five A4 pages, while the Python source code and report are submitted separately. 202526_SmallProject_semB Overview word count: approximately 330 words. Note: the university name is not printed directly in the visible assignment heading, but the submission instructions use a herts.ac.uk module-leader address and StudyNet/Canvas, which identifies the institution as the University of Hertfordshire. 202526_SmallProject_semB
Megaminds has supported academic requirements in data science / data analytics, fundamentals of data science and related disciplines.