
Cap certification empowers analytics professionals through a software-neutral exam, focusing on domain knowledge and preparation strategies to advance your career. Learn eligibility and the 100 questions across six domains.
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.
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.
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.
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.
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.
Learn how data science works via the four steps acquire, prepare, analyze, and act, highlighting data types, silos, and robust predictive modeling.
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 learning models, supervised and unsupervised, and how offline and online training, plus batch and streaming execution, shape scheduling and sequencing in analytics.
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.
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.
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.
Master data cleaning fundamentals by diagnosing data quality issues, including soft data, labeling, invalid responses, and inconsistent encodings, then apply deletion and imputation techniques.
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 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.
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.
Introduction:
The Certified Analytics Professional (CAP) certification is a globally recognized credential that validates your expertise in analytics. This course is designed to help you master the essential topics and skills needed to excel in the CAP exam. You will gain insights into business problem framing, analytical problem-solving, data science, and the importance of data visualization. Whether you are an aspiring data scientist, an analytics professional, or someone aiming to advance their career with a CAP certification, this course offers structured learning to help you succeed.
Section 1: Introduction to CAP Exams
The course begins with an introduction to the CAP certification, outlining the benefits of earning this credential for analytics professionals. You'll gain a deep understanding of the CAP certification process and how it can boost your career. Additionally, you'll learn the relevance of the certification across different industries and how it serves as a benchmark for analytic skills.
Section 2: Understanding Objectives
In this section, you will dive into the key objectives of the CAP exam and their respective weightages. Lectures cover topics such as business problem framing, analytical problem framing, and the methodological approach to solving business challenges. The concept of "knowledge statements" and effective presentation techniques will also be explored, helping you understand what the exam evaluators are looking for.
Section 3: Understanding Business Problem Identification
This section focuses on the critical task of identifying business problems and conducting stakeholder analysis. You’ll learn how to refine problem statements and agree on initial business benefits with stakeholders. The goal is to ensure that you can clearly define problems before jumping into analytical solutions.
Section 4: Further Reading on Business Problem Framing
Here, you will be guided through the process of writing effective problem statements. This section emphasizes problem-solving techniques, the process of defining a problem, and the powerful impact of re-framing problems. You'll be equipped with questions to frame business problems more effectively, setting the stage for impactful analytical work.
Section 5: Analytical Problem
This section delves into the process of analytical problem framing and introduces you to frameworks such as Kano’s Requirement Model. You’ll explore key success metrics, how to propose drivers and relationships between inputs, and understand the core principles that guide successful analytics problem framing.
Section 6: Certified Analyst Professional Training – Data Science
Data science plays a crucial role in CAP certification. This section covers data science fundamentals and explores the differences between business intelligence (BI) and data science. You’ll learn the step-by-step process of acquiring and preparing data, analyzing it, and transforming data into actionable insights. Key concepts like feature engineering, dimensionality reduction, and model validation are also discussed.
Section 7: Certified Analyst Professional Training – Five E’s of CAP Exam
This section introduces the Five E’s of the CAP exam, focusing on the key skills and soft skills required to pass the exam. You’ll learn how to clarify the analytical process, understand CAP-specific terminology, and apply regression, predictive, and prescriptive analytics. Real-world examples will demonstrate the practical applications of these skills.
Section 8: Data Visualization – CAP Certification
In this section, you’ll explore the importance of data visualization in presenting analytics results. Learn common data visualization techniques such as decision trees and heat maps, and how to effectively communicate data insights through data storytelling. Data quality, cleaning, and building a data mart are also discussed, providing you with the tools to create meaningful, accurate visual representations.
Section 9: Analytics Methodology and Test Analytics Model
The final section focuses on different analytics methodologies and how to validate analytics models. You'll learn about predictive methodologies, simulation techniques, and software tool selection. This section ensures you are prepared to test, refine, and implement analytics models in a real-world context.
Conclusion:
By the end of this course, you will have a solid understanding of the key components required to excel in the CAP exam. You will be proficient in framing business and analytical problems, applying data science techniques, utilizing data visualization tools, and validating analytics models. This comprehensive training will equip you with the skills necessary to become a Certified Analytics Professional.