Free learning packs and a big tickers dataset

A lot of people trying to break into finance or analytics get stuck on the same problem: job posts ask for Excel, Structured Query Language, Python, and Tableau, but most beginners only have one of those four and no project that ties them together. Two widely shared posts this week pointed people to free bundles that try to close that gap in one shot. (x.com) One of those posts pointed to a Udemy-style learning pack built around the exact tools that keep showing up in analyst listings: spreadsheet work in Excel, database querying in Structured Query Language, scripting in Python, and dashboard building in Tableau. Udemy’s own catalog still shows active courses that bundle those skills into one track, including courses updated in 2026. (udemy.com) The second shared item was a Google Drive folder of analytics and resume material, which is a different kind of help. Courses teach syntax, but shared folders usually solve the messier part of the job hunt: example resumes, project prompts, templates, and interview notes that turn practice into something you can actually send to a recruiter. (x.com) (drive.google.com) A separate post from Quant Science pushed a free finance database with about 300,000 ticker symbols for Python workflows. That is the kind of raw material people use for backtesting, which means testing a trading rule on old market data the way a pilot uses a simulator before flying a real plane. (x.com) That dataset matters because most beginner finance projects die at the data step. Python backtesting libraries like Backtesting.py and Backtrader are easy to install, but they still need clean symbol lists and price histories before they can produce a single chart, trade log, or return curve. (github.com 1) (github.com 2) The jump from “I watched a course” to “I built something” is usually one small project. A learner can pull company data with Python, clean fields in Excel, query subsets with Structured Query Language, and finish with a Tableau dashboard that shows returns, drawdowns, or sector comparisons. (udemy.com) (github.com) That is also why free resources spread so fast on social platforms. A paid bootcamp can cost hundreds or thousands of dollars, while Udemy still lists a large free-course section for data analysis and related skills, which lowers the cost of trying a project before committing money. (udemy.com) The catch is that free packs are only useful if the links stay live and the material stays current. Course pages change, Google Drive permissions break, and finance datasets age quickly, so the best use of these bundles is to download what is legal to save, build one concrete project, and turn that project into a portfolio piece while the resources are still available. (udemy.com) (x.com)