Data Analysis Boot Camp

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.

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

Course Outline

Part 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: Get 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
• Mitigation of Risk

Data Quality

• Cleansing
• Duplicates
• SSOT
• Field standardization
• Identify sparsely populated fields
• How to fix common issues

LAB: Data Quality

Part 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

Part 3: Analyzing Data

Averages in Data

• Mean
• Median
• Mode
• Range

Central Tendency

• Variance
• Standard Deviation
• Sigma Values
• Percentiles
• Use Concepts for Estimating

LAB: Hands-On – Central Tendency

Analytical Graphics for Data

Categorical

• Bar Charts

Continuous

• Histograms

Time Series

• Line Charts

Bivariate Data

• Scatter Plots

Distribution

• Box Plot

Part 4: Analytics & Modeling

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

• Understanding Clustering

• Segmentation

• Common Algorithms

• K-MEANS

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

• R Basics
• Descriptive Statistics
• Importing and Manipulating Data
• R Scripting
• Data Visualization with R
• Regression in R
• K-MEANS in R
• Monte Carlo in R
• Demo/Lab: Hands-on R work

Part 6: 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