What’s the fastest way to learn enough Python and SQL for a summer commodities trading role?

A good crash plan would be: Python fundamentals and pandas first, SQL fundamentals next, then timed practice assessments. Learn variables, conditionals, loops, functions, lists and dictionaries, file handling, exceptions, and basic debugging. In pandas, practice filtering rows, selecting columns, grouping, joining, sorting, and handling missing values. For SQL, concentrate on joins, aggregations, date filtering, and window functions. Keep an error log so you review the mistakes that actually slow you down.

If you already understand basic programming concepts, a broad introductory computer science course can work, but you may not have time to complete every section. Skim the general material and prioritize the Python and SQL units, then spend the rest of your time solving realistic data questions under a time limit.

Use a structured plan instead of just randomly practicing. Spend a few days working through an interactive Python course, then solve small exercises without looking at the answers. For SQL, use an interactive site with progressively harder query problems. After learning the basics, build a small project that loads market or weather data, cleans it with Python, stores it in a database, and answers questions with SQL. That will help you explain your reasoning during interviews instead of merely recognizing solutions.

You can use an AI tutor to explain concepts, generate small quizzes, and identify gaps, but don’t let it write the solutions you’ll be tested on. Ask it to give you a problem, attempt the solution yourself, and then have it review your code line by line. Interviewers may ask you to explain why your code works or modify it under pressure, so understanding the basics matters more than producing a polished project with assistance.

Focus on SQL first because it’s usually the quicker, higher-yield skill for assessments. Make sure you understand SELECT statements, filtering, joins, GROUP BY, aggregations, subqueries, and window functions. Practice writing queries against small datasets until the syntax feels automatic. For Python, prioritize data handling with lists, dictionaries, functions, loops, and especially pandas DataFrames. You probably don’t need to spend most of your limited time on advanced algorithms or difficult coding puzzles.