https://github.com/trainingbypackt/sql-for-data-analytics

Perform fast and efficient data analysis with the power of SQL
https://github.com/trainingbypackt/sql-for-data-analytics

aggregate-functions copy data-analytics datetime geospatial group-by hash-scan having index-scan join killing-queries sequential-scan sql sqlalchemy window-function

Last synced: 5 days ago
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Perform fast and efficient data analysis with the power of SQL

• Host: GitHub
• URL: https://github.com/trainingbypackt/sql-for-data-analytics
• Owner: TrainingByPackt
• License: mit
• Created: 2019-03-29T09:05:01.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2024-08-18T22:28:08.000Z (5 months ago)
• Last Synced: 2025-01-11T14:04:24.832Z (12 days ago)
• Topics: aggregate-functions, copy, data-analytics, datetime, geospatial, group-by, hash-scan, having, index-scan, join, killing-queries, sequential-scan, sql, sqlalchemy, window-function
• Language: Jupyter Notebook
• Homepage:
• Size: 20.9 MB
• Stars: 277
• Watchers: 21
• Forks: 436
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# SQL for Data Analytics

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don’t know how to use it to gain business insights from data, this course is for you.

SQL for Data Analysis covers everything you need progress from simply knowing basic SQL to telling stories and identifying trends in data. You’ll be able to start exploring your data by identifying patterns and unlocking deeper insights. You’ll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you’ll understand how to become productive with SQL with the help of profiling and automation to gain insights faster.

By the end of the course, you’ll able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of analytics professional.

## What you will learn

* Perform advanced statistical calculations using the WINDOW function

* Use SQL queries and subqueries to prepare data for analysis

* Import and export data using a text file and psql

* Apply special SQL clauses and functions to generate descriptive statistics

* Analyze special data types in SQL, including geospatial and time data

* Optimize queries to improve their performance for faster results

* Debug queries that won’t run

* Use SQL to summarize and identify patterns in data

 

### The examples of this title has been implemented in the Windows/MAC/Linux operating system.

### Software Requirement

You’ll also need the following software installed in advance:

* [Anaconda Python 3.7](https://www.anaconda.com/distribution/#downloadsection)

* [PostgreSQL 10.9](https://www.postgresql.org/download/)

* Git 2 or later

### Loading the sample datasets

You can find updated instructions on loading the sample datasets via this [link](https://github.com/TrainingByPackt/SQL-for-Data-Analytics/blob/master/Datasets/Loading_the_sample_datasets_instructions.pdf).