Dataiku - Data Science Test

Understanding the fundamental architecture of Dataiku, its interface, and the different components such as projects, flows, and datasets. The topic also covers the roles and permissions of different users within the platform. It emphasizes how these elements integrate to form a cohesive data science environment, providing users with the ability to manage datasets, automate workflows, and visualize results.

Data preparation is a crucial step in the data science process. This topic covers techniques for cleaning and transforming raw data, including handling missing values, outlier detection, data imputation, data normalization, scaling, and feature engineering. Students should also demonstrate an understanding of how to apply visual recipes (such as Join, Group, and Filter) within Dataiku to transform raw data into usable formats for analysis or modeling.

This area focuses on the use of exploratory data analysis (EDA) and visualization tools within Dataiku. It involves techniques for summarizing datasets, performing statistical analysis (e.g., mean, variance, correlations), and creating a wide variety of visualizations (e.g., bar charts, histograms, heatmaps, scatter plots). The focus is on using Dataiku’s built-in charting and dashboarding capabilities to convey insights from data, detect patterns, and present findings interactively. This also includes exploring advanced visualizations and dashboards for reporting.

This topic covers the practical application of machine learning algorithms within Dataiku. It tests the ability to use Dataiku’s Visual ML tool for model creation, including supervised and unsupervised learning techniques. Topics covered include decision trees, random forests, linear regression, logistic regression, support vector machines (SVM), and k-means clustering. In addition, learners should be able to assess the suitability of algorithms for different types of data and problems, and how to leverage Dataiku’s AutoML features to automate parts of the modeling process.

Once models are trained, it is essential to evaluate their performance. This topic covers key metrics for assessing model performance, including accuracy, precision, recall, F1-score, ROC curve, AUC, and confusion matrix. Furthermore, it explores methods of hyperparameter tuning, cross-validation, and model optimization to improve model accuracy and robustness. Students will also be tested on their ability to apply validation techniques (e.g., train-test split, k-fold cross-validation) to ensure the generalizability of models.

This topic focuses on the deployment and monitoring of machine learning models in a production environment using Dataiku’s Flow. It covers techniques for model deployment (including setting up API endpoints), creating automated retraining pipelines, and implementing A/B testing to assess the impact of different model versions. In addition, students will be tested on how to monitor model performance over time and detect issues such as model drift or concept drift, using Dataiku’s monitoring tools.

This topic explores the ethical implications and practical applications of Responsible AI within Dataiku. It emphasizes techniques for ensuring fairness, transparency, and accountability in machine learning models. Topics include using SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) for explaining model predictions, as well as evaluating bias in models and ensuring compliance with ethical AI guidelines. This also involves documenting and communicating the decision-making process behind model predictions, particularly in high-stakes domains like healthcare or finance.

This topic covers the creation, management, and optimization of data pipelines within Dataiku. Students will demonstrate the ability to design scalable workflows, partition large datasets, and implement efficient data processing techniques. Topics include automating data transformations, scheduling tasks, managing dependencies between steps, and optimizing workflow performance using partitioning, parallel processing, and resource management. Knowledge of using Dataiku’s scenario management tools for automating and scheduling tasks is also covered.

This topic tests the ability to extend and customize Dataiku workflows using code. Students will need to demonstrate proficiency in writing custom Python, R, or SQL code within Dataiku’s interface, particularly for tasks that require advanced modeling, data transformation, or external library integration. Topics covered include custom scripts, the use of external libraries, writing reusable code, and integrating with other systems (APIs, databases, etc.). The goal is to test the ability to extend Dataiku’s capabilities for specialized tasks.

In this topic, learners will be tested on their ability to extend Dataiku’s functionality using custom plugins. This includes creating plugins for new data connectors, machine learning algorithms, or visualization tools. Students will also explore how to use existing plugins from the Dataiku Plugin Store to enhance workflows, simplify tasks for non-technical users, and integrate external systems. Knowledge of creating user-friendly interfaces for business users, or customizing workflows for non-coders, is key in this area.