Work with Pandas, SQL Databases, JSON, Web APIs and more to master your real-world Machine Learning and Finance Projects
InstructorAlexander Hagmann
Free
Free
The curriculum follows a roadmap of data analysis, moving from basic cleaning to complex tasks like feature engineering and financial strategy backtesting.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This diagram outlines the full data workflow from import to use, illustrating key stages like cleaning, merging, and exploring data using tools such as APIs, JSON, and SQLite.
This geographic heatmap visualization overlays a map of California with distinct red and blue data point clusters to demonstrate advanced spatial data presentation.
This introductory slide presents the instructor's profile, displaying a headshot of Alexander Hagmann along with his professional titles as a Data Scientist and Finance Professional.
Instruction covers a wide breadth of Pandas applications, including working with SQL databases and web APIs.
One signal from 2024 suggests that the 12 projects may overlap significantly, with several sections acting as different stages of a single dataset analysis. Note that while the course has an update label, this specific review predates it and does not prove the issue was unaddressed.
One signal from 2024 suggests that the 12 projects may overlap significantly, with several sections acting as different stages of a single dataset analysis rather than distinct scenarios. Note that while the course has an update label, this specific review predates it and does not prove the issue was unaddressed.
Technical learners may find the lack of explicit financial concept explanations a hurdle when engaging with the investment-focused sections.
The content aligns well with the stated intermediate level and target audience of data professionals, though financial concepts may require more context for non-specialists.