Learn MySQL Basics for Data Science

Learn how to install MySQL using the community edition, navigate the installer prompts, apply configuration, and complete the setup to start data science work.

Learn how to uninstall MySQL and clean up related folders to prevent future installation issues and prepare for a clean reinstallation.

Search for solutions via Google to address MySQL uninstall issues, download fixes, and follow the solution to fix the problem.

Learn how to create and use databases, show and manage tables, and load data in MySQL for data science, using employees and salaries tables.

Explore creating tables from an ERD in MySQL Workbench, building a star schema with dimension tables, defining one-to-many, one-to-one, and many-to-many relationships, and auto-generating create table statements.

Master the select statement by querying the employees table to retrieve fields like employee number, first name, last name, and birth date, with a sample limit of 100 records.

Learn how to use select distinct to retrieve unique values from the employees table, count rows, view the first 100 rows, and identify male and female gender values.

Learn how to select data in MySQL using operators and conditions, including equals, not equals, greater than, less than, between, and like to filter results.

Learn how to use SQL with and, or, not and the between operator to filter employees by gender and a date range from 1960 to 1980.

learn to sort data with the order by clause in MySQL, including sorting by a column and applying ascending or descending order such as dates from February to January.

Learn how to use insert into employee values to add rows to the employees table and how to update and manage data for employee records within the table.

Identify null and missing values in data using examples with employee data, and learn how to detect and handle such values in datasets.

Learn how to update data in a MySQL table with update statements, change first and last names in place, and verify results.

Learn how to use delete statements in MySQL to remove rows with a where clause, delete all rows, and empty a table.

Learn to query a salaries table to find the minimum and maximum salaries using select statements.

Learn how to apply count, sum, and average aggregations in MySQL on a salaries table to derive insights for data science projects.

Explore using the MySQL LIKE operator to filter data with pattern matching, including percent and underscore wildcards, and apply it to last names and sample data.

Explore how to use group by in MySQL to create a frequency table by gender and race, counting individuals in each group.

Join the employees and salaries tables on employee_number to show matching employee numbers and salaries.

Learn how to join two tables using a join statement, combining the employees and salaries tables on the employee number to retrieve combined results for data science.

Learn how to generate a frequency table using join and group by in MySQL, compute average salaries by gender, and compare male and female salary distributions.

Explore advanced join and group by techniques in MySQL, calculating the average salary from the salaries table by grouping on first name, last name, and gender in data science contexts.

Learn how to join three tables in MySQL to retrieve employee salaries by employee number, including July salary data and related employee details.

Join three tables to analyze employee data, salaries, and job titles using MySQL basics. Explore how to query relationships across the employees, salaries, and titles tables.

Learn how to join a fact table to dimension tables in a star schema to analyze sales by country and product.

Explore data warehouse basics, including extracting, transforming, and loading data from multiple databases into a centralized warehouse. Investigate subsets of data and multi-dimensional data warehouse for decision support.

Explore the data mining process from business understanding to deployment, using descriptive statistics, visualizations, data cleaning, normalization, modeling, evaluation, and model deployment decisions.

Demonstrates ROLAP rollup operations for relational, multi-dimensional data, showing how to roll up by attributes like first name, last name, and gender to generate hierarchical summaries.