Dataiku ML Practitioner study guide
Dataiku ML Practitioner · Work through your exam, one topic at a time.
The ML Practitioner Certificate is one of the certifications offered through Dataiku Academy, the learning arm of the Dataiku platform. According to the Academy catalog, the certificate validates knowledge of topics such as visual machine learning and interactive statistics - the two areas a practitioner uses when building models with Dataiku's visual tools rather than code. The catalog tags the credential toward roles including data scientist, data engineer, and citizen data scientist, so it suits analysts and model builders who work primarily through the point-and-click interface and want a structured way to prove that skill. The associated learning path is described as honing skills on the visual tools in Dataiku for building machine learning models, and the Academy lists individual recommended courses such as Machine Learning Basics, Scoring Basics, and Interactive Statistics that map onto the two domains. Topics on this exam: Visual machine learning and Interactive statistics. The Dataiku ML Practitioner study guide has 2 sections and about 1,008 words.
01Visual Machine Learning
Dataiku's Visual ML lab is a guided environment for training, comparing, and deploying models without writing code. The exam checks whether you can configure a modeling session correctly: choose the target and prediction type, define a train/test strategy, handle features, select metrics, train candidate algorithms, optimize hyperparameters, interpret results, and deploy the winning model into the flow. Questions often test judgment rather than menu recall, especially around leakage, imbalance, and metric selection. Target and prediction type: Set the target before training.
503 words, about 3 min02Interactive Statistics
The Interactive Statistics domain covers Dataiku's no-code exploration environment on a dataset: the Statistics tab, worksheets, cards, filters, sampling, and the interpretation of univariate, bivariate, correlation, PCA, clustering, and statistical-test output. The exam expects you to know how to profile a dataset quickly, isolate a segment, choose a suitable card, and avoid drawing causal or overconfident conclusions from exploratory results. Worksheets and cards: A worksheet groups cards. Each card answers one question: distribution, correlation, scatter, box plot, PCA, clustering, or a statistical test.
505 words, about 3 min