claudeEy20/Udacity-Data-Analyst-Projects

This repository is about the projects i have done during the Udacity Data Analyst NanoDegree sponsored by ALX Africa program # UDACITY DATA ANALYST Nanodegree > Gather, Assess, Analyse and Visualize insights from data using Python ## Summary ### Prerequisites To follow the projects, you will need to install: - Python (3.x or higher) - Jupyter Notebook - Pandas - Numpy - Matplotlib - Seaborn And some additional librairies defined in each project. Recommended: - Anaconda ## Projects Overview ### Project 1: Investigate a Dataset (Tmdb Movies Dataset) This project was all about the data analysis process as whole. From gathering, cleaning, assessing and wrangling to visualizing the data from The Movies Database (TMDb) dataset. This project included therefore all steps of the typical data analysis process. This includes: > - posing questions > - gather and wrangle data > - communicate answers to the questions assited through visualizations and statistics. ### Project2: Gather, Clean and Analyse Twitter WeRateDogs (@dog_rates) Data This project was a deep dive into the data wrangling part of the data analysis process. We learned about the difference between messy and dirty data, how tidy data should look like, about the assessing, defining, cleaning and testing process, etc. Moreover, we talked about many different file types and different methods of gathering data. We gathered data from different sources (for example the Twitter API), identified issues with the dataset in terms of tidiness and quality. Afterwards we had to solve these problems while documenting each step. The end of the project was then focused on the exploration of the data. ### Project3: Communicate Data Findings (Ford GoBike Dataset) The final project was focused on proper visualization of data. We learned about chart junk, uni-, bi- and multivariate visualizations and how to use some functions parameters as color, shape, hue, jitter and transparency to improve our visuals.