Requirements

• Basic Understanding of Analytics: A foundational knowledge of data analytics concepts and methodologies is recommended to facilitate comprehension of advanced topics.
• Familiarity with Data Science Tools: Prior experience with data analysis tools and software (e.g.
• Excel
• R
• Python
• or SQL) will be beneficial.
• Statistical Knowledge: A basic understanding of statistical principles and methods is essential for grasping analytical problem framing and interpretation.
• Critical Thinking Skills: Students should possess strong analytical and critical thinking skills to effectively identify and solve business problems.
• Desire to Obtain CAP Certification: A motivation to pursue the Certified Analytics Professional (CAP) certification will enhance engagement and commitment to the course material.

Description

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.

Who this course is for:

• Aspiring Analysts: Individuals looking to start a career in analytics and seeking to obtain the Certified Analytics Professional (CAP) certification.
• Data Science Professionals: Those currently working in data science or related fields who wish to deepen their understanding of analytics methodologies and improve their skills.
• Business Professionals: Managers and decision-makers who want to enhance their analytical skills to make data-driven decisions within their organizations.
• Students: University or college students pursuing degrees in business
• data science
• statistics
• or related fields who are interested in gaining practical knowledge and certification in analytics.
• Career Changers: Professionals from non-analytical backgrounds seeking to transition into data analytics roles and wanting a comprehensive understanding of the analytics process.