Data Analysis Boot Camp

Make informed, data-driven decisions applicable to real-world situations using hands-on data analysis exercises with Excel and R.

This three-day course leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality.

This course starts with an overview of data quality and data management, followed by foundational analysis and statistical techniques. Throughout the course, you learn to communicate about data and insights to stakeholders who need to make quick business decisions to drive your organization forward.

This data analysis training class is a lively blend of expert instruction combined with hands-on exercises so you can practice new skills and leave prepared to start performing practical analysis techniques. Every Data Analysis Boot Camp instructor is a veteran consultant and data guru who guides you through effective best practices and technologies for working with your data.

In this 3-day class you’ll learn:

• Identify opportunities, manage change and develop deep visibility into your organization
• Understand the terminology and jargon of analytics, business intelligence, and statistics
• Use 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
• Estimate more accurately while accounting for variance, error, and confidence intervals
• Create 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 using multiple methods to define and interpret useful predictions
• Identify statistical inference and drawing conclusions about the population

Course Outline:

Part 1: The Value and Challenges of Data-Driven Disruption

1. Objectives and expectations
2. Hurdles to becoming a data-driven organization
3. Data empowerment
4. Instilling data practices in the organization
5. The CRISP-DM model of data projects

Part 2: Tying Data to Business Value

1. What constitutes data-driven value
2. Requirements gathering: How to approach it
3. Kanban for data analysis
4. Know your customers
5. Stakeholder cheat sheets

• EXERCISE: Data-driven project checklist
• LAB: Data analysis techniques: Aggregations

Part 3: Understanding Your Data

1. Data defined
2. Data versus information
3. Types of data

1. • Unstructured vs. Structured

1. • Time scope of data

1. • Sources of data

1. Data in the real world
2. The 3 V’s of data
3. Data Quality

1. • Cleansing

1. • Duplicates

1. • SSOT

1. • Field standardization

1. • Identify sparsely populated fields

1. • How to fix common issues

• LAB: Prioritizing data quality

Part 4: Analyzing Data

1. Analysis foundations

1. • Comparing programs and tools

1. • Words in English vs. data

1. • Concepts specific to data analysis

1. • Domains of data analysis

1. • Descriptive statistics

1. • Inferential statistics

1. • Analytical mindset

1. • Describing and solving problems

1. Averages in data

1. • Mean

1. • Median

1. • Mode

1. • Range

1. Central tendency

1. • Variance

1. • Standard deviation

1. • Sigma values

1. • Percentiles

1. Demystifying statistical models
2. Data analysis techniques

• LAB: Central tendency
• LAB: Variability
• LAB: Distributions
• LAB: Sampling
• LAB: Feature engineering
• LAB: Univariate linear regression
• LAB: Prediction
• LAB: Multivariate linear regression
• LAB: Monte Carlo simulation

Part 5: Thinking Critically About Your Analysis

1. Descriptive analysis
2. Diagnostic analysis
3. Predictive analysis
4. Prescriptive analysis

Part 6: Data Analysis in the Real World

1. Deployment of analyses
2. Best practices for BI
3. Technology ecosystems

1. • Relational databases

1. • NoSQL databases

1. • Big data tools

1. • Statistical tools

1. • Machine learning

1. • Visualization and reporting tools

1. Making data useable

Part 7: Data Visualization & Reporting

1. Best practices for data visualizations

1. • Visualization essentials

1. • Users and stakeholders

1. • Stakeholder cheat sheet

1. Common presentation mistakes
2. Goals of visualization

1. • Communication and narrative

1. • Decision enablement

1. • Critical characteristics

1. Communicating data-driven knowledge

1. • Formats and presentation tools

1. • Design considerations

Part 8: Hands-On Introduction to R and R Studio

1. What is R?

• LAB: Intro to R Studio
• LAB: Univariate linear regression in R
• LAB: Multivariate linear regression in R