Data Analysis Boot Camp

About this Course

This course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality and how to translate data into analysis of business problems to begin making informed, intelligent decisions. Get an overview of data quality and data management, followed by foundational analysis and statistical techniques. Throughout the course, you will learn to communicate about data and findings to stakeholders who need to quickly make the decisions that drive your organization forward.

At the end of the class, we provide an overview of the Certified Analytics Professional certification. We discuss business applications for professionals with the certification, the main focus areas behind the certification, test-preparation and test-taking anecdotes.

Audience Profile

Primary audience for this course :

• Business Analyst, Business Systems Analyst, CBAP, CCBA
• Systems, Operations Research, Marketing, and other Analysts
• Project Manager, Program Manager, Team Leader, PMP, CAPM
• Data Modelers and Administrators, DBAs
• IT Manager, Director, VP
• Finance Manager, Director, VP
• Operations Supervisor, Manager, Director, VP
• Risk Managers, Operations Risk Professionals
• Process Improvement, Audit, Internal Consultants and Staff
• Executives exploring cost reduction and process improvement options
• Job seekers and those who want to show dedication to process improvement
• Senior staff who make or recommend decisions to executives

At Course Completion

Upon course completion, students will be able to:

 *Delivered by ASPE, ICAgile Member Organization

Course Outline

Section 1: Data Fundamentals 

Course Overview and Level Set

• Objectives of the Class
• Expectations for the Class

Understanding “Real-World” Data

• Unstructured vs. Structured
• Relationships
• Outliers
• Data growth

Types of Data

• Flavors of Data
• Sources of Data
• Internal vs. External Data
• Time Scope of Data (Lagging, Current, Leading)

LAB: Get Started with our Classroom Data
Data-Related Risk

• Common Identified Risks
• Effect of Process on Results
• Effect of Usage on Results
• Opportunity Costs, Tool Investment
• Mitigation of Risk

Data Quality

• Cleansing
• Duplicates
• SSOT
• Field standardization
• Identify sparsely populated fields
• How to fix common issues

• LAB: Data Quality

Section 2: Analysis Foundations

Statistical Practices: Overview

• Comparing Programs and Tools
• Words in English vs. Data
• Concepts Specific to Data Analysis
• Domains of Data Analysis
• Descriptive Statistics
• Inferential Statistics
• Analytical Mindset
• Describing and Solving Problems

Section 3: Analyzing Data

Averages in Data

• Mean
• Median
• Mode
• Range

Central Tendency

• Variance
• Standard Deviation
• Sigma Values
• Percentiles
• Use Concepts for Estimating

LAB: Hands-On – Central Tendency
Analytical Graphics for Data
Categorical

• Bar Charts

Continuous

• Histograms

Time Series

• Line Charts

Bivariate Data

• Scatter Plots

Distribution

• Box Plot

Section 4: Analytics & Modeling

• Overview of Commonly Useful Distributions
  • Probability Distribution
  • Cumulative Distribution
  • Bimodal Distributions
  • Skewness of Data
  • Pareto Distribution
    • Correlation
  • LAB: Distributions
  • Predictive Analytics
  • A Discussion about Patterns
  • Regression and Time Series for Prediction
  • LAB: Hands-On – Linear Regression
    • Simulation
  • Pseudo-random Sequences
  • Monte Carlo Analysis
  • Demo / Lab: Monte Carlo in Excel
• Understanding Clustering
• Segmentation
• Common Algorithms
• K-MEANS

Section 5: Hands-On Introduction to R and R Studio

• R Basics
• Descriptive Statistics
• Importing and Manipulating Data
• R Scripting
• Data Visualization with R
• Regression in R
• K-MEANS in R
• Monte Carlo in R
• Demo/Lab: Hands-on R work

Section 6: Visualizing & Presenting Data

• Goals of Visualization
  • Communication and Narrative
  • Decision Enablement
  • Critical Characteristics
• Visualization Essentials
  • Users and Stakeholders
  • Stakeholder Cheat Sheet
  • Common Missteps
• Communicating Data-Driven Knowledge
  • Alerting and Trending
  • To Self-Serve or Not
  • Formats & Presentation Tools
  • Design Considerations