R codes and analysis output for Day 1

Data Source: https://creativecommons.org/licenses/by-nc-sa/4.0/ Imported CSV into google sheets and ran VLOOKUP to calculate information such as profit & revenue by category.

IBM Data Scientists created a fictional data set exploring employee attrition. We will explore each category within the data set. We will also compare and contrast the former workers with the other employees. Eventually, we will categorize the former and current employees into three groups: Category Green: Retained, Category Yellow: At-Risk, and Category Red: Attrited. We will also create a trial data set to determine if we can retain “at-risk” employees based on the data provided.

As usual, in this weekly review of December 29th, 2024, we will examine the top-performing and underperforming stocks on the Euronext and BRVM (Bourse Régionale des Valeurs Mobilières) financial markets.

"This analysis aims to study and analyze diamond data using a variety of statistical techniques and visual methods." To master the analysis in R, the following key steps are essential: Exploration: Explore the dataset to understand its structure, summarize the data, and identify potential patterns or anomalies. Cleaning: Clean the data by handling missing values, removing duplicates, and correcting any inconsistencies to prepare it for analysis. Visualization: Use visual techniques, such as plots and charts, to represent the data and insights, helping to uncover trends and relationships.

Aquí te dejo un minitutorial de como hacer un vector y los tipos de vectores que puedes crear con R y RStudio

Reminder, raw & central moments, quantiles, percentiles, modes, etc... any details which are easily forgotten