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Data Analysis Boot Camp

3 Days
Classroom Session   |  
4 Days
Live Online


ASPE is an IIBA Endorsed Education Provider of business analysis training. Select Project Delivery courses offer IIBA continuing development units (CDU) in accordance with IIBA standards.


NASBA continuing professional education credits (CPE) assist Certified Public Accountants in reaching their continuing education requirements.


Select courses offer Leadership (PDU-L), Strategic (PDU-S) and Technical PMI professional development units that vary according to certification. Technical PDUs are available in the following types: ACP, PBA, PfMP, PMP/PgMP, RMP, and SP.

24.00 CPEs
21.00 T-PDUs

Expertise Level: Intermediate
Classroom Registration Fees
Group Rate:
(per registrant, 2 or more)
GSA Individual:
Live Online Registration
Live Online:
Private Onsite Package

This course can be tailored to your needs for private, onsite delivery at your location.

Learn More About Enterprise Team Training


Every day buzzwords like "analytics," "insights" and "big data," permeate the pages of our business journals. Companies and departments are well aware of their huge troves of data, and they have access to common tools for leveraging this data. However, much less available are the actual analysis skills to truly understand and realize the benefits of this information. The potential is very real, but comprehensive skills can be scarce, and outside consultants are expensive. 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.

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.

Informs logo ASPE Training is proud to announce that we are an official Registered Education provider (REP) with Informs® for the Certified Analytics Professional (CAP®) Exam.

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
Upcoming Dates and Locations
Guaranteed To Run
Aug 14, 2017 – Aug 16, 2017    8:30am – 4:30pm Live Online
8:30am – 4:30pm
Sep 11, 2017 – Sep 13, 2017    8:30am – 4:30pm Baltimore, Maryland

Hilton Garden Inn BWI Airport
1516 Aero Drive

Linthicum Heights, MD 21090
United States

Sep 25, 2017 – Sep 27, 2017    8:30am – 4:30pm Denver, Colorado

Microtek Denver
999 18th Street
Suite 300 South Tower
Denver, CO 80202
United States

Sep 25, 2017 – Sep 27, 2017    10:30am – 6:30pm Live Online
10:30am – 6:30pm
Oct 9, 2017 – Oct 11, 2017    8:30am – 4:30pm Minneapolis, Minnesota

Embassy Suites Airport
7901 34th Avenue South

Bloomington, MN 55425
United States

Oct 30, 2017 – Nov 1, 2017    8:30am – 4:30pm Live Online
8:30am – 4:30pm
Oct 30, 2017 – Nov 1, 2017    8:30am – 4:30pm Raleigh, North Carolina

ASPE Training
114 Edinburgh South Dr
Suite 200
Cary, NC 27511
United States

Nov 6, 2017 – Nov 8, 2017    8:30am – 4:30pm Phoenix, Arizona

Dynamic Worldwide
4500 S. Lakeshore Dr
Suite 600
Tempe, AZ 85282
United States

Nov 6, 2017 – Nov 8, 2017    10:30am – 6:30pm Live Online
10:30am – 6:30pm
Nov 27, 2017 – Nov 29, 2017    8:30am – 4:30pm Saint Louis, Missouri

Saint Louis University - Workforce Ctr
3545 Lindell Blvd.
2nd Floor Wool Center
Saint Louis, MO 63103
United States

Dec 4, 2017 – Dec 6, 2017    8:30am – 4:30pm Dallas, Texas

Microtek Dallas
5430 Lyndon B Johnson Fwy
Three Lincoln Centre, Suite 300
Dallas, TX 75240
United States

Dec 11, 2017 – Dec 14, 2017    12:00pm – 4:30pm Live Online
12:00pm – 4:30pm
Dec 18, 2017 – Dec 20, 2017    8:30am – 4:30pm New York, New York

Microtek New York City
180 Maiden Lane
Suite 1102
New York, NY 10038
United States

Course Outline

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


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

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.

Yes, this course looks perfect for my needs!

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