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Data Analysis Boot Camp - Online

Course Code: STTA DABC
Length: 4 Days
Tuition: $1,995.00

Schedule for this Course

There are no dates scheduled for this course.
If you would like to be added to the wait list for this class Click Here

Course Description:

PMI: 21 PDUs

Learn practical, hands-on data analysis skills – Three days of fast-paced, comprehensive training for analysts, subject matter experts, managers and senior staff who need versatile, real-world data capability.

Develop Critical Data Analysis Skills

Today's organizations face both a promise and a dilemma. The growth in availability and quantity of data, as well as the tools to leverage it, is well understood. Every day buzzwords like "big data," "insights" and "analytics" permeate the pages of our business journals. However, much less available are the actual skills to truly understand and realize the benefits of this explosive growth. The potential is very real, but comprehensive skills can be scarce, and outside consultants are expensive.

Fortunately, you don't need a PhD in data science to achieve the rewards of good data analysis and management. If you have basic familiarity with a tool like Excel, this three-day course can teach you the comprehensive skills and tools to maximize and leverage your data assets.

Enhance the Analysis of Business Problems and Root Causes

In today's environment, your organization must make informed, intelligent decisions with speed and accuracy. The only way to consistently achieve this type of insight and agility is to leverage your data, understanding both the technology available to work with it and the tools and processes needed to actually use it. By adopting a comprehensive, real-world skill set for analyzing data, you can unlock competitive advantage and powerful organizational visibility – replacing undue reliance on instinct, gut responses, and emotionally skewed actions with empirical, real-world information that yields actionable decisions.

You will leave this class with a far better understanding of the principles of data analysis, when to utilize them, and focused business examples of the principles in professional environments.

We simplify complex concepts, break down math jargon and help you navigate complex symbols and equations to concentrate on what is actually useful when working with your data in the real world. We also provide practical techniques for presenting findings to executives, managers, and subject matter experts who need to quickly make decisions that drive your organization forward. These tools include graphic presentation techniques and simplified models which allow you to transform the results of your analysis into approachable, easy-to-understand insights and findings.

Practice Real-World Tools and Techniques for Immediate Application

Data Analysis Boot Camp is a fast-paced, three-day classroom experience that builds on basic data and statistics fundamentals for a deep dive into the principles of real-world application. We teach you to use your data to deal with critical factors: risk, performance, quality, forecasting, estimating, simulation, business process improvement, and much more.

Data Analysis Boot Camp is organized into five key topic areas:

  • Understanding Data
  • Looking at Data
  • Modeling Data
  • Mining Data
  • Using Data

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 class is a lively blend of expert instruction combined with hands-on practice so you can cement your new skills. Leave prepared to start performing practical analytics, modeling and interpretation the moment you return to work. Every Data Analysis Boot Camp instructor is a veteran consultant and data guru who will guide you through incredibly effective best practices and cutting-edge technologies for working with your data. Through a combination of demonstrations and hands-on exercises, you will get practice with the sort of skills and techniques which are typically the domain of expensive consultants.

You will quickly master powerful skills and the technologies to deploy them, without reliance on proprietary technology. (We teach data skills you can deploy using Excel, R or Python). Working through these scenarios, you will learn the value of analytics in supporting decision-making processes and the management and controls of business processes. In addition to providing real-world data analysis skills and concepts, you will apply the knowledge you acquire in a number of real business scenarios to learn practical applications.

20 Benefits of Attending This Training Course

  1. Get beyond basics to identify opportunities, manage change and develop deep visibility into your organization
  2. Leave class understanding the terminology and jargon of analytics, business intelligence and statistics
  3. Learn a wealth of practical applications for applying data analysis capability
  4. Visualize both data and the results of your analysis for straightforward graphical presentation to stakeholders
  5. Learn to estimate more accurately than ever, while accounting for variance, error, and Confidence Intervals
  6. Practice creating a valuable array of plots and charts to reveal hidden trends and patterns in your data
  7. Differentiate between "signal" and "noise" in your data – smooth what's extraneous and reveal what's important
  8. Understand and leverage different distribution models, and how each applies in the real world
  9. Get in-depth with practical statistics, and how they relate to risk, probability, results and action
  10. Develop a robust, practical understanding of probability theory – and how to leverage it
  11. Form and test hypotheses – use multiple methods to define and interpret useful predictions
  12. Learn about statistical inference and drawing conclusions about the population
  13. Leave class with a substantial yet practical toolbox of modeling skills
  14. Use computation to mine data, run simulations, find clusters and discover important attributes
  15. Apply your data to practical uses: Reporting, Dashboards, Metrics, Quality, Financial Modeling and more
  16. Get hands-on with predictive analytics – leave class with the vocabulary and algorithms you need
  17. Forecast future results, find opportunities for process improvement, and analyze past performance
  18. Get a comprehensive introduction to the INFORMS CAP (Certified Analytics Professional) certification
  19. Investigate a number of real-world examples that bring to life the new tools you've learned
  20. Over three days, get access to a real-world data expert who relates skills and methods to your own scenario

Who Should Attend
Anyone involved in operations, project management, business analysis, or management would benefit from this class. This training course is invaluable data analysis training for:


  •     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



Course Outline:


  • Statistical Thinking - Overview
    • Do First Babies Arrive Late?
    • A Statistical Approach
    • The National Survey of Family Growth
    • Tables and Records
    • Significance
  • Descriptive Statistics
    • Means and Averages Variance
    • Distributions
    • Representing Histograms
    • Plotting Histograms
    • Representing PMFs
    • Plotting PMFs
    • Outliers
    • Other Visualizations
    • Relative Risk
    • Conditional Probability
    • Reporting Results
  • Cumulative Distribution Functions
    • The Class Size Paradox
    • The Limits of Probability Mass Functions
    • Percentiles
    • Cumulative Distribution Functions
    • Representing Cumulative Distribution Functions
    • Back to the Survey Data
    • Conditional Distributions
    • Random Numbers
    • Summary Statistics Revisited
  • Continuous Distributions
    • The Exponential Distribution
    • The Pareto Distribution
    • The Normal Distribution
    • Normal Probability Plot
    • The Lognormal Distribution
    • Why Model?
    • Generating Random Numbers
  • Probability
    • Rules of Probability
    • Monty Hall
    • Poincaré
    • Another Rule of Probability
    • Binomial Distribution
    • Streaks and Hot Spots
    • Bayes's Theorem
  • Operations on Distributions
    • Skewness
    • Random Variables
    • PDFs
    • Convolution
    • Why Normal?
    • Central Limit Theorem
    • The Distribution Framework
  • Hypothesis Testing
    • Testing a Difference in Means
    • Choosing a Threshold
    • Defining the Effect
    • Interpreting the Result
    • Cross-Validation
    • Reporting Bayesian Probabilities
    • Chi-Square Test
    • Efficient Resampling
    • Power
  • Estimation
    • The Estimation Game
    • Guess the Variance
    • Understanding Errors
    • Exponential Distributions
    • Confidence Intervals
    • Bayesian Estimation
    • Implementing Bayesian Estimation
    • Censored Data
    • The Locomotive Problem
  • Correlation
    • Standard Scores
    • Covariance
    • Correlation
    • Spearman's Rank Correlation
    • Least Squares Fit
    • Goodness of Fit
    • Correlation and Causation


  • Single Variable: Establishing Distribution
    • Dot and Jitter Plots
    • Histograms and Kernel Density Estimates
    • The Cumulative Distribution Function
    • Rank-Order Plots and Lift Charts
    • Only When Appropriate: Summary Statistics and Box Plots
    • Workshop: Creating these plots in Excel (or R or Python)
  • Two Variables: Establishing Relationships
    • Scatter Plots
    • Conquering Noise: Smoothing
    • Logarithmic Plots
    • Banking
    • Linear Regression
    • Showing What's Important
    • Graphical Analysis and Presentation Graphics
    • Workshop: Creating these plots in Excel (or R or Python)
  • Time-series Analysis
    • Examples
    • The Task
    • Smoothing
    • Don't Overlook the Obvious!
    • The Correlation Function
    • Optional: Filters and Convolutions
    • Workshop: Time Series in Excel
  • More than Two Variables
    • False-Color Plots
    • A Lot at a Glance: Multiplots
    • Composition Problems
    • Novel Plot Types
    • Interactive Explorations
    • Workshop: Tools for Multivariate Graphics


  • Guesstimation
    • Principles of Guesstimation
    • How Good Are Those Numbers?
    • A Closer Look at Perturbation Theory and Error Propagation
  • Models from Scaling Arguments
    • Models
    • Arguments from Scale
    • Mean-Field Approximations
    • Common Time-Evolution Scenarios
    • Case Study: How Many Servers Are Best?
    • Why Modeling?
  • Arguments from Probability Models
    • The Binomial Distribution and Bernoulli Trials
    • The Gaussian Distribution and the Central Limit Theorem
    • Power-Law Distributions and Non-Normal Statistics
    • Other Distributions
    • Optional: Case Study—Unique Visitors over Time
    • Workshop: Power-Law Distributions


  • Simulations
    • A Warm-Up Question
    • Monte Carlo Simulations
    • Resampling Methods
    • Workshop: Discrete Event Simulations
  • Finding Clusters
    • What Constitutes a Cluster?
    • Distance and Similarity Measures
    • Clustering Methods
    • Pre- and Postprocessing
    • Other Thoughts
    • A Special Case: Market Basket Analysis
    • A Word of Warning
  • Finding Important Attributes
    • Principal Component Analysis Visual Techniques
    • Kohonen Maps
    • Workshop: PCA with R


  • Reporting, Business Intelligence, and Dashboards
    • Business Intelligence
    • Corporate Metrics and Dashboards
    • Data Quality Issues
    • Workshop: Visualizing real-time quality metrics
  • Financial Calculations and Modeling
    • The Time Value of Money
    • Uncertainty in Planning and Opportunity Costs
    • Cost Concepts and Depreciation
    • Should You Care?
    • Is This All That Matters?
    • Workshop: The Newsvendor Problem
  • Predictive Analytics
    • Introduction
    • Some Classification Terminology
    • Algorithms for Classification
    • The Process
    • The Secret Sauce
    • The Nature of Statistical Learning
    • Workshop: Two Do-It-Yourself Classifiers


  • The Seven Analytics Domains
    • Business Problem Framing
      • Objective 1. Receive and refine the business problem
      • Objective 2. Identify stakeholders
      • Objective 3. Determine whether the problem is amenable to an analytics solution
      • Objective 4. Refine problem statement and delineate constraints
      • Objective 5. Define an initial set of business benefits
      • Objective 6. Obtain stakeholder agreement on the problem statement
    • Analytics Problem Framing
      • Objective 1. Reformulating the business problem statement as an analytics problem
      • Objective 2. Develop a proposed set of drivers and relationships to outputs
      • Objective 3. State the set of assumptions related to the problem
      • Objective 4. Define the key metrics of success
      • Objective 5. Obtain stakeholder agreement
    • Data
      • Objective 1. Identify and prioritize data needs and resources
      • Objective 2. Identify means of data collection and acquisition
      • Objective 3. Determine how and why to harmonize, rescale, clean and share data
      • Objective 4. Identify ways of discovering relationships in the data
      • Objective 5. Determine the documentation and reporting of findings
      • Objective 6. Use data analysis results to refine business and analytics problem statements
    • Methodology (Approach) Selection
      • Objective 1. Identify available problem solving approaches
      • Objective 2. Select software tools
      • Objective 3. Model testing approaches
      • Objective 4. Select approaches
    • Model Building
      • Objective 1. Identify model structures
      • Objective 2. Evaluate and calibrate models and data
      • Objective 3. Calibrate models and data
      • Objective 4. Integrate the models
    • Solution Deployment
      • Objective 1. Perform business validation of the model
      • Objective 2. Deliver report with the findings
      • Objective 3. Create model, usability, and system requirements for production
      • Objective 5. Support Deployment
    • Model Lifecycle
      • Objective 1. Document initial structure
      • Objective 2. Track model quality
      • Objective 3. Recalibrate and maintain the model
      • Objective 4. Support training activities
      • Objective 5. Evaluate the business benefit of the model over time
  • Test Preparation Advice
  • Test Experience Feedback


  • Understanding, Analyzing, and Presenting Data
    • Statistics
    • Graphics
    • Analytics
    • Computing
    • Applications
  • Certified Analytics Professional
  • Student Feedback / Evaluations
  • Networking

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