Problem & background

Analysing daily weather conditions in Madrid from 1997-2015.The dataset includes information such as max/min/mean temperature, dew point, humidity, visibility, and wind speed, along with precipitation events, cloud cover, and wind direction. The goal of the analysis is to answer several questions related to the weather conditions in Madrid during this time period. These questions include determining the percentage of days with precipitation, identifying the warmest month for planning a vacation, finding the date with the fastest wind gust, and comparing average visibility for clear and foggy days.

Recommended Analysis:

During the time period in this sample, what % of days had some sort of precipitation event? What % were clear?

Suppose you're planning a vacation to Madrid and hoping for the warmest temperature. Which month might you plan to travel?

On which date in the sample did Madrid see the fastest wind gust? What was the weather like on that day?

How does the average visibility (Km) compare for clear days vs. foggy days?

Methodology & Project scope

Methodology:

The methodology for this project involved using Excel to analyse the daily weather conditions in Madrid from 1997-2015. The raw data was collected and compiled into a single spreadsheet, which was then cleaned and processed to prepare it for analysis. In order to answer the research questions, two additional columns were added to the spreadsheet: visibility and precipitation. The visibility column was used to categorise days as either clear or foggy based on the visibility range, and the precipitation column was used to categorise days as either having precipitation or being clear based on the precipitation amount.

Project Scope:

The scope of this project was limited to analysing the daily weather conditions in Madrid from 1997-2015. The focus was on answering specific research questions related to precipitation, temperature, wind, and visibility. The project involved cleaning and processing the raw data using Excel and adding two columns to the spreadsheet for analysis. The project did not include any statistical modelling or predictive analysis beyond simple calculations and comparisons. The goal was to provide insights and answers to the research questions using the available data.

Goals & KPIs

To measure the success of the project, the following goals and KPIs have been established:

Goal 1: To determine the percentage of days with precipitation and clear weather during the time period of the sample.

KPI: Calculate the percentage of days with precipitation and clear weather using the additional analysis columns created in Excel.

Goal 2: To identify the month with the warmest temperature in Madrid from 1997-2015.

KPI: Analyse the temperature data and determine the month with the highest maximum temperature.

Goal 3: To compare the average visibility (Km) on clear days versus foggy days.

KPI: Analyse the visibility data and calculate the average visibility on clear days and foggy days separately. Compare the two averages to determine if there is a significant difference.

Concepts Used

Weather data analysis: This involves analysing and interpreting weather data to gain insights and make decisions.

Data visualisation: This involves using charts, graphs, and other visual aids to represent data in a meaningful and understandable way.

Excel: This is a software program commonly used for data analysis, which includes tools for organising, manipulating, and analysing large sets of data.

Visibility: This refers to the distance at which objects can be clearly seen, and is often affected by factors such as weather conditions, air pollution, and geographic features.

Temperature: This refers to the degree of hotness or coldness of the atmosphere, and is typically measured using a thermometer.