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Data Analysis Boot Camp

SS Course: 9000399

Course Overview


This three-day course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality. You will learn how to make more informed, intelligent business decisions by analyzing data using Excel functions and the R programming language.

You will 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 decisions that drive your organization forward.

This data analyst training class is a lively blend of expert instruction combined with hands-on exercises so you can practice new skills. Leave prepared to start performing practical analysis techniques the moment you return to work. Through a combination of demonstrations and hands-on practice, you will learn to use data analysis techniques, which are typically the domain of expensive consultants.

Labs for this course are primarily in Microsoft Excel, however, students will get an opportunity to practice using R in some labs.


Scheduled Classes

05/13/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
06/19/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
07/17/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
08/12/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
09/23/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
10/16/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
11/18/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)
12/04/24 - AVT - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

  • Identify opportunities, manage change and develop deep visibility into your organization
  • Understand the terminology and jargon of analytics, business intelligence, and statistics
  • Learn a wealth of practical applications for applying data analysis capability
  • Visualize both data and the results of your analysis for straightforward graphical presentation to stakeholders
  • Learn to estimate more accurately than ever, while accounting for variance, error, and Confidence Intervals
  • Practice creating a valuable array of plots and charts to reveal hidden trends and patterns in your data
  • Differentiate between "signal" and "noise" in your data
  • Understand and leverage different distribution models, and how each applies in the real world
  • Form and test hypotheses use multiple methods to define and interpret useful predictions
  • Learn about statistical inference and drawing conclusions about the population


Viewing outline for:

Part 1: The Value and Challenges of Data-Driven Disruption

  1. Objectives and expectations for this class
  2. Hurdles to becoming a data-driven organization
  3. Data empowerment
  4. Instilling data practices in the organization
  5. The CRISP-DM model of data projects

Part 2: Tying Data to Business Value

  1. What constitutes data-driven value?
  2. Requirements gathering: How to approach it?
  3. Kanban for data analysis
  4. Know your customers
  5. Stakeholder cheat sheets

CLASS EXERCISE: Data-driven project checklist

LAB: Data analysis techniques: Aggregations

Part 3: Understanding Your Data

  1. Data defined
  2. Data vs Information
  3. Types of data
    • Unstructured vs. Structured
    • Time scope of data
    • Sources of data
  4. Data in the real world
  5. The 3 V's of data
  6. Data Quality
    • Cleansing
    • Duplicates
    • SSOT
    • Field standardization
    • Identify sparsely populated fields
    • How to fix common issues

LAB: Prioritizing data quality

Part 4: Analyzing Data

  1. Analysis Foundations
    • 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
  2. Averages in Data
    • Mean
    • Median
    • Mode
    • Range
  3. Central Tendency
    • Variance
    • Standard Deviation
    • Sigma Values
    • Percentiles
  4. Demystifying statistical models
  5. Data analysis techniques

LAB: Central Tendency

LAB: Variability

LAB: Distributions

LAB: Sampling

LAB: Feature Engineering

LAB: Univariate Linear Regression

LAB: Prediction

LAB: Multivariate Linear Regression

LAB: Monte Carlo Simulation

Part 5: Thinking Critically About Your Analysis

  1. Descriptive Analysis
  2. Diagnostic Analysis
  3. Predictive Analysis
  4. Prescriptive Analysis

Part 6: Data Analysis in the Real World

  1. Deployment of analyses
  2. Best practices for BI
  3. Technology Ecosystems
    • Relational databases
    • NoSQL databases
    • Big data tools
    • Statistical tools
    • Machine learning
    • Visualization and reporting tools
  4. Making data useable

Part 7: Data Visualization & Reporting

  1. Best practices for data visualizations
    • Visualization Essentials
    • Users and Stakeholders
    • Stakeholder Cheat Sheet
  2. Common presentation mistakes
  3. Goals of Visualization
    • Communication and Narrative
    • Decision Enablement
    • Critical Characteristics
  4. Communicating Data-Driven Knowledge
    • Formats & Presentation Tools
    • Design Considerations

Part 8: Hands-On Introduction to R and R Studio

  1. What is R?

LAB: Intro to R Studio

LAB: Univariate Linear Regression in R

LAB: Multivariate Linear Regression in R



If you have basic familiarity with Excel, this three-day course can teach you practical applied analysis techniques to leverage data for relatively common decision-making methods.

Additionally, although it is not mandatory, students who have completed the self-paced Introduction to R eLearning course have found it very helpful when completing this course.

    Who Should Attend

    • 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

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