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Data Analysis Boot Camp - 3 Day

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.

In–Class Exercises, Demos, and Real-World Case Studies

This data analysis 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. Every Data Analysis Boot Camp instructor is a veteran consultant and data guru who will guide you through effective best practices and easily-accessible technologies for working with your data. 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:

  • 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



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: Getting 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

  • Mitigating common risks

Data Quality

  • Cleansing

  • Duplicates

  • SSOT

  • Field standardization

  • Identifying sparsely populated fields

  • How to fix some common issues

LAB: Data Quality


  • Finding common attributes

  • 1:N, N:N, 1:1

LAB: Relationships in a dataset

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


3. Analyzing Data

Averages in data

  • Mean

  • Median

  • Mode

  • Range

Central Tendency

  • Variance

  • Standard deviation

  • Sigma values

  • Percentiles

  • Using these concepts to estimate things

LAB: Hands-On – Central Tendency

LAB: Hands-On – Linear Regression

Overview of commonly useful distributions

  • Probability distribution

  • Cumulative distribution

  • Bimodal distributions

  • Skewness of data

  • Pareto distribution


LAB: Distributions

Analytical Graphics for Data

  • Categorical – bar charts

  • Continuous – histograms

  • Time series – line charts

  • Bivariate data – scatter plots

  • Distribution – box plot


4. Analytics & Modeling

ROI & Financial Decisions

Common uses of financial data

  • Earned Value

  • Actual Cost, BAC and EAC

  • Expected Monetary Value

  • Cost Performance/Schedule Performance Index

Common uses for random numbers

  • Sampling

  • Simulation

  • Monte Carlo analysis

  • Pseudo-random sequences

Demo / Lab – Random numbers in Excel

An introduction to Predictive Analytics

  • A discussion about patterns

  • Regression and time series for prediction

  • Machine learning basics

  • Tools for predictive analytics

Demo / Lab – Getting started with R

Understanding Clustering

  • Segmentation

  • Common algorithms


  • PAM

Fundamentals of Data Modeling

  • Architecture and analysis

  • Stages of a data model

  • Data warehousing

  • Top-down vs. Bottom-up

Understanding Data Warehousing

  • Context tables

  • Facts

  • Dimensions

  • Star vs. Snowflake Schema


5. 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

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