SQL Crash Course for Aspiring Data Scientist

Explore basic sql concepts, including selecting data with from and where, and understand primary and foreign keys, relationships, and joins across one-to-many, one-to-one, and many-to-many structures.

Master basic sql statements to query relational databases, using select and where, filtering with order by, joining tables, grouping by, and using min, max, average, sum, and count functions.

Explore using a safe lab to write queries against a sample database and navigate the setup. Practice joins that count languages represented in films and inventory for analysis.

Install PostgreSQL, set up PGAdmin, create the DVD rental sample database, and run basic queries to explore server objects, schemas, tables, and selective statements with F5.

Learn data retrieval with hands-on SQL queries in Postgres, analyzing film inventory, rentals, payments, and joins to answer real-world business questions.

Develop resourceful data skills by reviewing lab questions that cover count, where, order by desc, casting to float, aggregates, joins, and views in SQL.

Explore when to use views, functions, or stored procedures to persist and reuse SQL logic; this lab introduces views as virtual tables in PostgreSQL.

Create and install a postgres view named customers by total spent to analyze lifetime value, using case and group by, and query it like a table to identify top customers.

Explore how to pull data from a database into a pandas data frame using Python, introduce Jupyter Lab, and explain why writing actual SQL statements isn't central in this lab.

Install and configure essential tools for lab three: Anaconda, Graphviz, pgadmin, and the PostgreSQL odbc driver; then create a dsn named dvd rental, install psycopg2, and load dot sql file.

Run Python and SQL workflows in Jupyter Lab, load CSV data with pandas, and query a view; apply exploratory data analysis and build a decision tree model with visual output.

Discover how to process big data in memory with Apache Spark, Databricks, and Pae Spark, using Spark SQL API and notebook workflows to spin up clusters.

Learn to spin up a spark cluster in Databricks, create a data table via the sequel API, and run notebooks with PySpark to analyze a dataset.

Gain enough SQL to write your own queries and start working tomorrow. Build practical skills from experience to demand higher salaries or consulting rates.