Udacity - Data Analyst Nanodegree - Project 5 - Data Visualization

Udacity Data Analyst Nanodegree - Project V

Descriptive Statistics 2023/2024

Performed an exploratory data analysis using python and presented explanatory plots that convey insights of data.

HHA507 / Data Science / Assignment 3 / Exploratory Data Analysis

This project is open-ended in that we are not looking for one right answer. As John Tukey stated, "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data." We want you to ask interesting questions about data and give you a chance to explore.

Engage in the critical phase of Exploratory Data Analysis (EDA) using the tools and techniques from Python to uncover patterns, spot anomalies, test hypotheses, and identify the main structures of your dataset.

Perform an exploratory data analysis and provide actionable insights for a food aggregator company to get a fair idea about the demand of different restaurants and cuisines, which will help them enhance their customer experience and improve the business

This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process: Exploratory and Explanatory Data Visualization.

A presentation of my exploratory data visualization results with univariate, bivariate, and multivariate features.

A complete Data Visualization tutorial using Seaborn and Matplotlib

Explanatory/Exploratory Data Analysis on Telecom-Customer-churn.

This project is conducted as a part of Udacity Data Analyst Nanodegree. The purpose of this project is to perform exploratory data analysis, then create a presentation with explanatory charts that conveys findings and insights from the data set provided.

White Wine Exploratory Analysis

STA404 Statistics for Business and Social Sciences (UiTM)

Udacity Data Analyst Nanodegree Project 5 (Data Visualization)

Explanatory/Exploratory Data Analysis on Ford-bike data-set

This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process: Exploratory and Explanatory Data Visualization.