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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.

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

Duration

3 days/24 hours of instruction

Education Credits

21 PDUs

Public Classroom Pricing

$1695(USD)

GSA Price: $1585

Group Rate: $1595

Private Group Pricing

Have a group of 5 or more students? Request special pricing for private group training today.

Download the Course Brochure

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

Objectives and expectations
Hurdles to becoming a data-driven organization
Data empowerment
Instilling data practices in the organization
The CRISP-DM model of data projects

Part 2: Tying Data to Business Value

What constitutes data-driven value
Requirements gathering: How to approach it
Kanban for data analysis
Know your customers
Stakeholder cheat sheets

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

Part 3: Understanding Your Data

Data defined
Data versus information
Types of data
1. Unstructured vs. Structured
2. Time scope of data
3. Sources of data
Data in the real world
The 3 V’s of data
Data Quality
1. Cleansing
2. Duplicates
3. SSOT
4. Field standardization
5. Identify sparsely populated fields
6. How to fix common issues

LAB: Prioritizing data quality

Part 4: Analyzing Data

Analysis foundations
1. Comparing programs and tools
2. Words in English vs. data
3. Concepts specific to data analysis
4. Domains of data analysis
5. Descriptive statistics
6. Inferential statistics
7. Analytical mindset
8. Describing and solving problems
Averages in data
1. Mean
2. Median
3. Mode
4. Range
Central tendency
1. Variance
2. Standard deviation
3. Sigma values
4. Percentiles
Demystifying statistical models
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

Descriptive analysis
Diagnostic analysis
Predictive analysis
Prescriptive analysis

Part 6: Data Analysis in the Real World

Deployment of analyses
Best practices for BI
Technology ecosystems
1. Relational databases
2. NoSQL databases
3. Big data tools
4. Statistical tools
5. Machine learning
6. Visualization and reporting tools
Making data useable

Part 7: Data Visualization & Reporting

Best practices for data visualizations
1. Visualization essentials
2. Users and stakeholders
3. Stakeholder cheat sheet
Common presentation mistakes
Goals of visualization
1. Communication and narrative
2. Decision enablement
3. Critical characteristics
Communicating data-driven knowledge
1. Formats and presentation tools
2. Design considerations

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

What is R?

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

Students attending this class should have a basic understanding of how data is currently used in their organization. They should also have strong Excel aptitude.

Professionals who benefit from this course include:

Business Analyst
Systems Analyst
Operations Researcher
Marketing Analyst
Project Manager
Program Manager
Team Leader
Data Modeler or Administrator
Database Administrator
IT Manager, Director, VP
Finance Manager, Director, VP
Operations Supervisor, Manager, Director, VP
Risk Assessment Manager
Process Improvement Staff
Executives exploring cost reduction and process improvement options
Senior staff who make or recommend key business decisions

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