Designed specifically for beginners with no background in Data Science, Computer Science or Alteryx
InstructorJed Guinto
Free
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the course structure in two stages, identifying Data Cleansing and Preparation followed by Machine Learning as the key components of the curriculum.
This slide outlines the beginner-level curriculum, covering essential data manipulation techniques such as inputs, outputs, sorting, filtering, and joins.
This slide outlines the intermediate curriculum, covering essential data preparation techniques such as filtering, formulas, joins, and spatial analytics.
This course slide outlines the Machine Learning and A.I. section, covering data preparation, regression models, decision trees, and time series analysis.
Covers essential data preparation including file ingestion, filtering, summarizing, and formula applications.
Includes a case study focused on stock market and portfolio analysis to apply tool workflows.
One sampled review suggests that despite the title and description, the active curriculum lacks the machine learning and predictive modeling content expected by some learners.
The depth of instruction is geared toward those with no prior experience, which may feel too basic for more advanced users.
Alteryx fundamentals and data cleansing workflows are prioritized over the promised machine learning instruction.