Certified Analytics Professional (CAP) Exam Prep Course

Explore the cap exam's analytic life cycle, its seven blocks from business problem framing to deployment, learn weightage distribution across data, modeling, and deployment, and practice scenario-based questions.

Explore business problem framing as the foundation of analytics: identify problems, refine problem statements, assess analytical feasibility, map stakeholders, define constraints, and articulate initial business benefits with stakeholder agreement.

Frame business problems as analytical problems by defining drivers, assumptions, and success metrics, securing stakeholder agreement, and mapping data needs, acquisition, cleaning, and relationships for CAP exam prep.

Learn to select approaches and tools for business problems, using supervised or unsupervised methods, clustering, market basket analysis, and propensity analysis, and prepare for model building, deployment, and lifecycle management.

Identify knowledge statements and their four components, including business problem statement characteristics, interviewing techniques, client business process, and org structure; explore modeling options, resources, and risk–return, roi, and kpi considerations.

Master presentation techniques to communicate analytical problems and solutions to business clients, as covered in the cap exam prep course, including decision structures, influence diagrams, data architecture, visualization, and negotiation.

Identify and frame business problems by defining problem statements, recognizing opportunities, threats, and issues, and analyzing stakeholders to align perspectives and drive effective analytics projects.

Identify whether a business problem is amenable to analytics by assessing data availability, organizational control, and modeling needs; refine the problem statement and constraints to enable actionable analytics solutions.

Identify stakeholders, define initial business benefits and KPIs, and secure stakeholder agreement to frame the analytics project and communicate expected sales uplift and SLA improvements.

Frame the business problem clearly, write a solvable problem statement with a clear vision, use the five Ws, and engage stakeholders through open-ended questions.

Outline a clear problem statement with vision and issue, then apply Dmacc or Kaizen and the five whys to identify stakeholders, boundaries, and impact for analytics problem solving.

Define the problem clearly to ensure you are solving the right issue, align with stakeholders and strategy, and ask the right questions before solving.

Master the problem definition process by establishing the need for a solution, justifying and contextualizing the business problem, and writing a clear problem statement with success metrics and constraints.

Reframe business problems to reveal innovative frames and perspectives, expanding possible solutions and clarifying whether you are solving the right problem.

Reframing the problem expands possible solutions by asking why and redefining the frame; apply this mindset to analytics and business with examples like Tesco, Kodak, Netflix, and 3M.

Frame business problems by securing stakeholder agreement on objectives, initial approaches, and resources; analyze opportunities, threats, or issues, and consider constraints—analytical, financial, or political.

Reframe the defined business problem as an analytical problem by identifying key input and output drivers, surfacing assumptions, and assigning goals to each subgroup, then translate what into how.

Learn the Kano requirement model, its four quadrants of normal, expected, exciting requirements, and how to map client needs to prioritize, deliver on time, and communicate risks for analytical projects.

Frame business problems as analytical problems by defining a proposed set of drivers and their input relationships, make inputs visible, and state assumptions to guide pricing strategy data analysis.

Define and agree on key metrics of success, quantify all measures, and align stakeholders to ensure analytics translate business problems into actionable outcomes.

Learn data science basics, from collecting and cleaning data—removing outliers, null values, and duplicates—to building models and turning data into actionable decisions, contrasting data science with traditional analytics.

Explore the interplay of deductive and inductive reasoning in data science, distinguishing it from business intelligence and highlighting how data science turns data into decisions and actions.

Learn how data science works via the four steps acquire, prepare, analyze, and act, highlighting data types, silos, and robust predictive modeling.

Learn to acquire and prepare data by challenging filters, embracing unstructured and external data, and building a data lake to unlock ROI while avoiding silos and overreliance on ETL.

Master the analyze step as the core driver of value from data, leveraging iterative modeling, data lakes, and scalable tools to act on real-time risk, opportunities, and decisions.

Adopt guiding principles for data science, embracing failure as a path to learning through rapid experimentation, while keeping business goals in mind and applying reasoning and common sense.

Explore the four core components of data science—data types, analytical classes, learning models, and execution models—and see how structured, unstructured, and streaming data shape analytics and solutions.

Explore nine classes of analytic techniques across transforming, learning, and predictive analytics, including aggregation, enrichment, processing, regression, clustering, classification, and recommendation, with simulation and optimization applications.

Explore learning models, supervised and unsupervised, and how offline and online training, plus batch and streaming execution, shape scheduling and sequencing in analytics.

Decompose business problems into analytical subproblems using fractal analytics to build end-to-end data solutions, balancing speed, data size, complexity, and accuracy while pursuing discover and predict goals.

Explore stages of data science maturity—from collecting internal and external data to describing, discovering, predicting, and advising outcomes—while building capable teams and selecting operating models.

Explore feature engineering, data veracity, and feature selection methods, including filtering and wrapper approaches, plus dimensionality reduction with principal component analysis for robust model validation.

Validate models by splitting data into training, testing, and validation sets, applying cross-validation and bootstrapping to curb overfitting, and addressing the curse of dimensionality through feature engineering and domain knowledge.

Explore cap exam questions on data filtering, imputation, dimensionality reduction (pca), feature extraction, and clustering methods. Learn variable importance and classification approaches using trees, neural nets, and bayesian nets.

Explore the five e of Cap exam: ethics, education, experience, examination, and effectiveness. Learn how these five tenets distinguish a certified analytics professional and guide ethical practice.

Explore how the CAP exam assesses knowledge through scenario-based items, with careful item writing, domain tasks, and knowledge statements, plus tips for computer-based testing and candidate soft skills.

Clarify the analytical process while weaving soft skills and stakeholder communication to lead analytics projects in cap exam prep. Identify stakeholders, elicit needs, translate language to business terms, ensure transparency.

Explore cap terminology, including yield components and model verification and validation, and delve into vehicle routing problem, traveling salesman problem, web analytics, variation, linear programming, and system dynamics for readiness.

Explore supply chain terminology, six sigma practices, and key analytics concepts such as stepwise regression, statistics, standard deviation, shadow price, and simulated annealing.

Explore CAP terminology, including sensitivity analysis, parametric analysis, scheduling with constraints and precedence, scenario analysis, data visualization with scatter plots, robust optimization, risk, supply chain, six sigma, and RFM.

Explore revenue management strategies to maximize revenue and profit, learn ROI calculations, and master regression analysis, response surface methodology, queueing theory, and the use of proprietary data.

Learn analytics project management, including problem framing, stakeholder communication, and strategic competencies, while examining PCA vs factor analysis, pricing, and prescriptive, predictive, and descriptive analytics.

Explore data visualization essentials for the CAP exam, learning how to present data clearly with charts, dashboards, stories, and interactive visuals that engage stakeholders.

Data visualization communicates information clearly through graphical means, balancing form and function to reveal patterns in large data sets using statistical graphics and thematic cartography.

Explore common data visualization techniques and assess data cardinality to tailor visuals for your audience and chosen graph types.

Explore core data visualization techniques like box plots, scatter plots, and word clouds, and learn data cardinality, velocity, and correlation matrix concepts for cap exam prep.

Explore decision trees, heat maps, and data visualization techniques to reveal strong input-output relationships and support exploratory and report graphics for stakeholders.

Learn to turn data visualization into a compelling data story by crafting a narrative, knowing your audience, and maintaining objectivity, balance, and rigorous editing.

Master data cleaning fundamentals by diagnosing data quality issues, including soft data, labeling, invalid responses, and inconsistent encodings, then apply deletion and imputation techniques.

Explore the ten data quality attributes—completeness, correctness, consistency, currency, collaboration, confidentiality, clarity, common format, convenience, and cost—and learn to score and improve data before analytics.

Learn how data marts provide a single view of data and enable analysts to slice, dice, drill down, roll up, and navigate by dimensions and measures using OLAP cubes.

Explore CAP terminology from prescriptive optimization and next best offer to guide data-driven decisions. Learn about operational research, OLAP cubes, objective functions, normalization, and network optimization.

Learn how to select analytics methodology, from descriptive to prescriptive stats, guided by problem framing, data readiness, timelines, and accuracy for CAP exam prep.

Explore the three analytics methodologies—descriptive, predictive, and prescriptive—and how INFORMS classifies them. Identify what happened, what could happen, and the best outcomes for CAP exam prep.

Learn how to select analytics software tools by evaluating time constraints, data availability and quality, and project relevance across descriptive, predictive, and prescriptive methodologies.

Learn to verify and validate analytics models using data partitioning and testing, and compare tools like Excel, Tableau, R, and SAS for visualization and data mining.

Explore predictive analytics methodology and its types, including forecasting with time series, simulation, regression, classification, clustering, and artificial intelligence.

Learn linear regression basics, including simple regression and the y = a + b x model, and explore stepwise variable selection. Examine discrete event and Monte Carlo simulations.