Used complex SQL queries to analyze data from an E-commerce company. This company wants to understand the most important metrics for the website visitors using the past 6-months data collected from the User Behavior Tracking (UBT) reports. The insights found in this analysis will lead to data driven decisions to adapt the services and marketing to customer preference and consequently increase the number of subscriptions.

In this case, the data had to be filtered, cleaned, formatted,  and prepared before start the actual analysis.  In the report, I  included the queries and their respective outputs. 

Tools in this analysis include MySQL workbench, wizard, joins, data filtering, import/load query Tableau, among others.

I suggest to open the Tableau Dashboard 🌐 while  reading the report.   

In this relational and exploratory analysis, I compare the relationship between some factors that influence the happiness index in various countries. The data used in this analysis was obtained from the Gallup World Poll between 2015 and 2017 released at the United Nations at an event celebrating International Day of Happiness. 

The objective is to analyze how the factors are related to each other, and  how each factor influences the index. 

For this analysis I used  the SQLite library, Pandas, matplotlib, and os,  in python on Jupyter Notebooks

Take a look at the following  interactive visualizations:

The main visual is World Map Viz  which display the happiness index and the live expectancy of each country.

Also I have design an interactive dashboard of scatterplots for each factor in the analysis: Dashboard🌐 

For this analysis, I am assigned to analyze a dataset that contains all the information about hotel bookings in a multinational hotel company. This dataset contains multiple variables, which contain crucial information about each reservation made by customers.

I decided to base my analysis on SQL because it allows me to work with larger datasets faster, it also allows me to create pivot tables to do descriptive and prescriptive analysis regarding the factors included in the data and thus guarantee data driven decisions.

In the report I have included the variable description and its respective data type,  lines of code, the output tables, observations of the results and an explanation of the codes used to answer the questions.