Turn Qlik Into a Full-Scale Data Science Platform
Analytics teams shouldn't need a separate data science stack to run statistical tests, build predictive models, or apply probability distributions. This collection of IPC Global cheat sheets gives Qlik practitioners everything they need, from AutoML algorithms and distribution functions to hypothesis testing and a full research methodology to do serious science inside Qlik Cloud. One platform. Four capabilities. Zero excuses.
Published
2026-06-08
7 (across 4 documents)
IPC Global
Technology
AUTHOR
Igor Alcantara, Alane Miguelis, Priscila Rubim
PUBLISHED
2026
AUDIENCE
Qlik Analysts & Data Scientists
INDUSTRY
Technology (Data & Analytics)
TOPIC
Qlik, statistics, machine learning, research
White Paper Snapshot
Everything you need to know in under 30 seconds
Key Topics
Everything You Need to Do Real Data Science in Qlik
Covers the complete analytical stack in one collection: From connecting data sources and cleaning datasets with Qlik Talend Cloud, through statistical distributions and hypothesis testing, all the way to AutoML model deployment, this series maps the full path from raw data to production insight.
Makes probability distributions immediately actionable: Eight distribution types, Normal, Beta, Binomial, Chi-Square, F, Gamma, Poisson, and T are documented with Qlik-native function syntax, parameter definitions, and a decision table that tells practitioners exactly when to use each one.
Eliminates guesswork in hypothesis testing: Chi-2, T-test, and Z-test functions are explained with full parameter syntax and selection criteria, including when to use weighted variants and one-sample forms, so analysts choose the right test for every scenario without leaving the platform.
Automates machine learning with ten production-grade algorithms: Qlik AutoML handles training, validation, hyperparameter optimization, and deployment end-to-end, with XGBoost, LightGBM, CatBoost, Random Forest, and six additional algorithms available out of the box.
Structures research as a repeatable, evidence-based process: The Qlik Science Research framework defines three stages, Investigate, Prepare the Data, and Use Qlik to Get Answers, giving teams a defensible methodology for any analytical engagement.
Highlights
Evaluate, Validate, and Communicate Results With Confidence
Provides the right evaluation metric for every model type: Regression models are assessed across R², RMSE, MAE, and MSE; classification models use Accuracy, AUC, Precision, Recall, and F1 Macro/Micro/Weighted, each matched to specific dataset characteristics and business needs.
Connects statistical testing to real decision-making: P-values, significance levels, and confidence intervals are framed not as academic outputs but as decision gates, telling teams when evidence is strong enough to act and when it is not.
Turns model outputs into boardroom-ready communication: Interactive Qlik dashboards, SHAP value explanations, and exportable reports convert technical findings into stakeholder-facing narratives that justify both the analysis and the recommendation.
Supports the full reproducibility standard: Every step, from null hypothesis definition to feature scaling and cross-validation, is documented so that any team member can audit, repeat, or extend the analysis without starting from scratch.
Results & Impact
One Platform Replaces Four Tools
By combining data integration via Qlik Talend Cloud, statistical testing, probability modeling, and Qlik AutoML, teams eliminate the need for separate Python environments, BI tools, and statistical software, reducing toolchain complexity and analyst context-switching.
Months of Analysis Compressed to Days
Automated preprocessing, five-fold cross-validation, and behavior-based analytics on publicly available information reduce what once required dedicated data science cycles into guided, repeatable workflows any Qlik-trained analyst can execute.