Final-Project-Transforming-and-Analyzing-Data-with-SQL

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Project/Goals🎈

In this project, we will combine and practice implementing what we have learned throughout this course, including:

• Extracting data from a SQL database
• Cleaning, transforming and analyzing data
• Loading data into a database
• Developing and implementing a QA process to validate transformed data against raw data

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Files Used📁

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README.md file

• This file should be an overview of your project, covering roughly the same points as your presentation

cleaning_data.md file

• Fill out this file with a description of the issues that will be addressed by cleaning the data Include the queries used to clean the data

startingwithquestions.md file

• Provide the answer to the 5 questions and the queries used to answer each question startingwithdata.md file Provide the 3 - 5 new questions you decided could be answered with the data Include the answer to each question and the accompanying queries used to obtain the answer

QA.md file

• Identify and describe your risk areas Develop and execute a QA process to address the risk areas identified, providing the SQL queries used to implement

schema.png file

• This file should contain the ERD of the database

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Process⏩

Loading CSV tables into pgadmin

When loading CSV's into pgAdmin4:

1. right click your public schema. Click Create > Table
2. In the General Tab, name your new table the same as your CSV(i.e. all_sessions)
3. In the columns tab, create each column from your CSV file in the exact order it appears
4. Set each column data type to an acceptable type (remember to adjust Length/scale to either 255 or 550)
5. click save
6. navigate to tables on the left hand menu
7. right click your new table, and click import/export data

After data is loaded

1. conduct sql queries on relevant files and screenshot the results into the project

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References:

1. https://www.postgresqltutorial.com/postgresql-tutorial/import-csv-file-into-posgresql-table/
2. https://www.youtube.com/watch?v=6Jf7eTkIaR4

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Data Cleaning🧼

• dealing with null values, getting rid of them
• Handle missing data - imputing missing values
• address outliers - remove outliers
• data normalization (changing data type)
• duplicate data
• data transformations

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Results🔍

• This data could tell me all about customer behaviour and which products are popular in which countres, i used the data to make these queries!
• The data helped me to answer various questions about inventories and tracking
• questions file
• data exploration
• QA file

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Challenges🎱

• had a lot of challenges with importing the data, writing a query to import it effectively at first
• doing joins and connecting queries together
• casting as integers

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Future Goals🥅

• more data cleaning and joining with other tables
• making a legend for the dataset and table of values
• maybe doing predictive analytics down the line

Project Presentation🌠

• https://docs.google.com/presentation/d/17vUFTBp6rq80fNdA5jdDX8FNj_PqUAQxWrnVqPet5Sk/edit