Data Analytics: Intro to SQL using Healthcare Data

Learn to download and install Microsoft SQL Server Management Studio on Windows 10, and explore Azure Data Studio as an alternative while practicing SQL in a database management tool.

Practice self evaluations in Microsoft SQL Server Management Studio after each section to reinforce learning; attempt them on your own before the instructor reviews them in a follow-up video.

Practice self evaluation steps in SQL Server Management Studio, show commenting methods, and create and use a database named SQL Course for interactive SQL exercises.

Learn how data types in SQL tables define columns such as int, varchar, datetime, and decimal. See how correct types prevent errors in the average charge per visit.

Learn how to insert data into a SQL table by specifying the table, columns, and values. Practice with a test table and verify results with selects, including a single-row example.

Complete the section four self-evaluation by seven steps, including creating five rows of your own data to practice insertion and deepen learning, then pause before watching the next video.

Master retrieving and manipulating existing data using where, order by, group by, having, and case statements, preparing clean, consumable data for analysis.

Master the like operator and wildcard symbols in SQL, including brackets, underscore, percent, and dash, to build pattern-based where clauses and handle null and not null values.

Explore how aggregate functions summarize rows with count, including count(*) and distinct count, and how where filters and group by affect results and errors.

Learn how the sum function adds numeric charges across rows, and how changing granularity with group by or roll up shifts sums from patient-level to state-level, avoiding non-numeric data.

Learn how to use the concat function in SQL Server to join two columns, apply an alias, and format results with a separator, using patient, state, and gender data.

Explore where clause operators, including greater than, less than, greater than or equal to, and not equal, and apply them in where and having statements with healthcare charges data.

Update the test table with a where clause after confirming the target row with a select. Learn to set one or more columns and update Nate from Wyoming to California.

Learn to delete data efficiently with a delete from statement and a condition, and avoid risky drop table and reinsert methods when handling large healthcare data.

Alter a table's structure in sql server by changing column data types, adding or dropping columns, and adjusting constraints, including default values and the money data type.

Execute select into with conditions to roll data up by gender, applying case statements, aggregating visits, charges, and patient counts, then create a new table for repeated analysis.

Explore temporary tables in SQL Server, using the hashtag to create test tables for quick validation, stored in tempdb and not in the actual course database.

Update and query a 25-row healthcare dataset using update, delete, and insert into to build a patient table, refine gender values, and add weight, height, and age.

Explore relational databases with star and snowflake schemas, linking a fact table to dimension tables, and use SQL Server Management Studio to run joins on health care data.

Master left joins in SQL by returning all records from the left table and matching rows from the right, with nulls for nonmatches.

Learn how right joins pull all data from the right table and match to the left table, using hospital and test tables to compare with left joins.

Explore the nullif function in SQL by converting zeros to nulls to avoid divide-by-zero errors, applying it while computing charges per visit across hospital data without altering the base table.

Explore left and right functions in SQL to shorten data and extract initials, update gender from m/f to full words, and display a four-character location ID while preserving unique keys.

Wrap up section eight by reviewing the diverse functions used and inviting student feedback. Prepare to dive into section nine and ten with health care data.

Explore how health care data flows from electronic medical records to billing and insurance payments, using a hospital scenario to illustrate CPT codes, billing, SQL-driven analysis, and accounts receivable.

Review the healthcare data model in Excel, covering fact and dim tables, keys, CPT and diagnosis codes, transactions, and preparing imports into SQL Server.

Create and enforce foreign key relationships from the health care fact table to dimension tables using primary keys, add constraints, and generate an erd in sql server management studio.

Learn to join healthcare database tables in SQL to analyze patient visits, diagnoses, CPT codes, charges, and insurance payments, using dim and fact tables in a hospital billing scenario.

Practice extensive SQL with the healthcare database, using section ten questions to master SQL syntax, fact tables, and gross charge filters, after reviewing section nine resources.

Count rows in the fact table for healthcare data where gross charge exceeds 100 to answer question one, with 5513 results. Explain why a where clause preserves granularity over having.

Learn how to count unique patients in a healthcare database using distinct counts from fact tables or patient numbers across tables, yielding 4962 patients.

Count distinct CPT codes by CPT grouping, order results by descending counts, and identify distributions across surgery, radiology, and medicine codes.

Link dim payer and dim physician to the fact table, filter Medicare claims, and count distinct providers (NPI) to find 682 providers submitted Medicare claims.

Compute the gross collection rate by location from the fact and dim location tables, summing payments (negating for credits) and dividing by gross charges, revealing Twins Mountain Hospital at 63.4%.

Analyze CPT codes by summing units in fact table, group by CPT code, filter with having units greater than 100, and use an inner query to count codes, yielding 29.

Learn how to count CPT units by diagnosis code group, filter for J codes with a wildcard search, and interpret ten J-code groups linked to respiratory infections.

Analyze credentialing write-offs across locations, show the highest adjustments at Angel Stone Community Hospital, where 30 physicians are impacted and $2,000 is tied to credentialing issues.

Compute net charge from gross charges minus contractual adjustments and assess net collection rate (payments over net charge) by physician specialty using healthcare data from a fact table.

Create your own sql question using the healthcare erd, practice joining tables, write the syntax to answer it with patient, physician, transaction, and diagnosis code data, then submit for review.

Master SQL using Microsoft SQL Server Management Studio while learning healthcare data for real-world applicability. Invite feedback and express appreciation for your participation with a five-star review.