Certified Analytics Professional (Cap) Exam Prep Course
Published 9/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Master the skills and knowledge to ace the CAP exam and advance your career as a Certified Analytics Professional!
What you’ll learn
Understanding the CAP certification process and its benefits.
Mastering business problem framing and analytical problem-solving techniques.
Gaining proficiency in data science, including data acquisition, preparation, analysis, and feature engineering.
Applying the Five E’s of the CAP exam and developing essential soft skills for the certification.
Effectively using data visualization tools to communicate insights and create impactful data stories.
Learning different analytics methodologies, validating models, and using predictive and simulation techniques.
Familiarizing with CAP-specific terminology and concepts like regression, predictive, and prescriptive analytics.
By the end of the course, students will be fully equipped to pass the CAP exam and advance their careers in the field of analytics.
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
Overview
Section 1: Introduction to CAP Exams
Lecture 1 CAP certification and Benefits
Section 2: Understanding Objectives
Lecture 2 Different Objectives and their Weightage
Lecture 3 Objective- Business Problem Framing
Lecture 4 Objective- Analytical Problem Framing
Lecture 5 Objective- Methodology Approach
Lecture 6 What are Knowledge Statements
Lecture 7 Knowledge Statements- Presentation techniques
Section 3: Understanding Business Problem Identification
Lecture 8 Business problem identification and stakeholders analysis
Lecture 9 How to refine problem statement
Lecture 10 Initial business benefits and stakeholders agreement
Section 4: Further Reading Business Problem
Lecture 11 How to Write a problem Statement
Lecture 12 Problem Statement- Issue, Vision etc
Lecture 13 Problem Solving
Lecture 14 The Problem Definition Process
Lecture 15 Power of Re-framing Problems
Lecture 16 Power of Re-framing Problem continued
Lecture 17 Business Problem Framing Questions
Section 5: Analytical Problem
Lecture 18 Analytical Problem Framing
Lecture 19 Kano’s Requirement Model
Lecture 20 Proposed set of drivers and relationship to inputs
Lecture 21 Key Metrics of Success
Section 6: Certified Analyst Professional training- Data Science
Lecture 22 Data Science Introduction and difference between BI and Data Science
Lecture 23 Data Science Introduction and difference between BI and Data Science continued
Lecture 24 How Data Science Work along with Acquire and Prepare Steps
Lecture 25 How Data Science Work along with Acquire and Prepare Steps continued
Lecture 26 How to Analyse and Act Data
Lecture 27 Guiding Principles and Reasoning and Common Sense
Lecture 28 Components of Data Science
Lecture 29 Classes of Analytic Techniques Transforming Learning and Predictive Analytics
Lecture 30 Learning Models , Execution Models Scheduling and Sequencing
Lecture 31 Decomposing Analytical Problem
Lecture 32 Data Science Maturity
Lecture 35 DATA CAP Questions
Section 7: Certified Analyst Professional training- FIVE E of CAP Exam
Lecture 36 The Five E for CAP exam
Lecture 37 The Five E for CAP exam continued
Lecture 38 Soft Skills for CAP exam
Lecture 39 Clarifying the Analytical Process
Lecture 40 CAP Terminology Yield Vechile Routing Problem and TSP
Lecture 41 CAP Terminology supply chain six sigma RFM
Lecture 42 CAP Terminology supply chain six sigma RFM continued
Lecture 43 Pattern Recognition Regression Predictive and Prescriptive Analytics
Lecture 44 Pattern Recognition Regression Predictive and Prescriptive Analytics continued
Section 8: Data Visualization- CAP Certification
Lecture 45 Data Visualization Definition and Importance
Lecture 46 Data Visualization Definition and Importance continued
Lecture 47 Common Techniques for Data Visualization,Data Cardinality and Velocity
Lecture 48 Common Techniques for Data Visualization,Data Cardinality and Velocity Continued
Lecture 49 Decision Trees Heat Maps and other type of Data Visualization Techniques
Lecture 50 How to write Data Story
Lecture 51 Data Cleaning
Lecture 54 CAP Terminology Optimization and Next Best offer
Section 9: Analytics Methodology and Test Analytics Model
Lecture 55 Analytics Methodology Introduction
Lecture 56 Different type of Analytics Methodology
Lecture 57 Software Tool Selection
Lecture 58 Validating Analytics Model and Testing Results
Lecture 59 Predictive Methodlogy and Different Kinds
Lecture 60 Simulation and its Kind
