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Data Analysis Boot Camp


3 Days Classroom Session   |  
3 Days Live Online
Classroom Registration
Individual:
$1995.00
Group Rate:
$1795.00
(per registrant, 2 or more)
GSA Individual:
$1456.35
Live Online Registration
Live Online:
$1995.00
Private Onsite Package

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

Request a Private Onsite Price Quote

Professional Credits

IIBA (CDU)

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.

PMI (PDU)

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.

This course offers:
    21.00 PMP/PgMP Technical PDUs

21
PMI PDUs
Certification
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.

data-analysis-training-course
Fill out the form on the left to download the Data Analysis Boot Camp Brochure.

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.

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

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. 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.

Labs for this course are primarily in Microsoft Excel, however, students will get an opportunity to practice using R in some labs. Labs for this course can also be taught using the Python programming language for private onsite clients only.

In This Data Analysis Training Course, You Will: 

  • 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
All Live Online times are listed in Eastern Time Guaranteed To Run
Request a quote for private onsite training Request
Oct 28, 2019 – Oct 30, 2019    8:30am – 4:30pm Live Online Register
Nov 4, 2019 – Nov 6, 2019    8:30am – 4:30pm San Jose, California

Please call ASPE for location details
at 1-877-800-5221
San Jose, CA 95101
United States

Register
Nov 11, 2019 – Nov 13, 2019    8:30am – 4:30pm Phoenix, Arizona

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

Register
Nov 11, 2019 – Nov 13, 2019    11:30am – 7:30pm Live Online Register
Nov 18, 2019 – Nov 20, 2019    8:30am – 4:30pm Columbia, Maryland

Homewood Suites by Hilton
8320 Benson Drive
Columbia, MD 21045
United States

Register
Dec 9, 2019 – Dec 11, 2019    8:30am – 4:30pm Houston, Texas

Texas Training and Conference
11490 Westheimer Rd.
Suite 600
Houston, TX 77077
United States

Register
Dec 9, 2019 – Dec 11, 2019    9:30am – 5:30pm Live Online Register
Dec 16, 2019 – Dec 18, 2019    8:30am – 4:30pm Boston, Massachusetts

Microtek Boston
25 Burlington Mall Road
2nd Floor
Burlington, MA 01803
United States

Register
Jan 21, 2020 – Jan 23, 2020    8:30am – 4:30pm Live Online Register
Jan 21, 2020 – Jan 23, 2020    8:30am – 4:30pm Raleigh, North Carolina

ASPE Training
2000 Regency Parkway
Suite 335
Cary, NC 27518
United States

Register
Jan 27, 2020 – Jan 29, 2020    8:30am – 4:30pm Boston, Massachusetts

Microtek Boston
25 Burlington Mall Road
2nd Floor
Burlington, MA 01803
United States

Register
Feb 18, 2020 – Feb 20, 2020    8:30am – 4:30pm Denver, Colorado

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

Register
Feb 18, 2020 – Feb 20, 2020    10:30am – 6:30pm Live Online Register
Feb 24, 2020 – Feb 26, 2020    8:30am – 4:30pm San Francisco, California

Learn IT
33 New Montgomery St.
Suite 300
San Francisco, CA 94105
United States

Register
Mar 23, 2020 – Mar 25, 2020    8:30am – 4:30pm Live Online Register
Mar 23, 2020 – Mar 25, 2020    8:30am – 4:30pm Atlanta, Georgia

Microtek Atlanta
1000 Abernathy Rd. NE Ste 194
Northpark Bldg 400
Atlanta, GA 30328
United States

Register
Mar 30, 2020 – Apr 1, 2020    8:30am – 4:30pm Austin, Texas

Embassy Suites Austin Central
5901 North IH-35
Frontage Rd
Austin, TX 78723
United States

Register
Apr 20, 2020 – Apr 22, 2020    8:30am – 4:30pm Philadelphia, Pennsylvania

Hyatt Place
440 American Avenue
King Of Prussia, PA 19406
United States

Register
Apr 27, 2020 – Apr 29, 2020    8:30am – 4:30pm San Jose, California

ExecuTrain West
2025 Gateway Place
Suite 390
San Jose, CA 95110
United States

Register
Apr 27, 2020 – Apr 29, 2020    11:30am – 7:30pm Live Online Register
Course Outline

Part 1: Data Fundamentals

  1. Course Overview and Level Set
    • Objectives of the Class
    • Expectations for the Class
  2. Understanding “Real-World” Data
    • Unstructured vs. Structured
    • Relationships
    • Outliers
    • Data Growth
  3. Types of Data
    • Flavors of Data
    • Sources of Data
    • Internal vs. External Data
    • Time Scope of Data (Lagging, Current, Leading)
  4. LAB: Get Started with our Classroom Data
  5. Data-Related Risk
    • Common Identified Risks
    • Effect of Process on Results
    • Effect of Usage on Results
    • Opportunity Costs, Tool Investment
    • Mitigation of Risk
  6. Data Quality
    • Cleansing
    • Duplicates
    • SSOT
    • Field standardization
    • Identify sparsely populated fields
    • How to fix common issues
  7. LAB: Data Quality

Part 2: Analysis Foundations

  1. 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

Part 3: Analyzing Data

  1. Averages in Data
    • Mean
    • Median
    • Mode
    • Range
  2. Central Tendency
    • Variance
    • Standard Deviation
    • Sigma Values
    • Percentiles
    • Use Concepts for Estimating
  3. LAB: Hands-On – Central Tendency
  4. Analytical Graphics for Data
  5. Categorical
    • Bar Charts
  6. Continuous
    • Histograms
  7. Time Series
    • Line Charts
  8. Bivariate Data
    • Scatter Plots
  9. Distribution
    • Box Plot

Part 4: Analytics & Modeling

  1. 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
  2. Understanding Clustering
  3. Segmentation
  4. Common Algorithms
  5. K-MEANS

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

  1. R Basics
  2. Descriptive Statistics
  3. Importing and Manipulating Data
  4. R Scripting
  5. Data Visualization with R
  6. Regression in R
  7. K-MEANS in R
  8. Monte Carlo in R
  9. Demo/Lab: Hands-on R work

Part 6: Visualizing & Presenting Data

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

This data analysis training course is designed for the following professions:

  • 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
Pre-Requisites

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.

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