Data Analysis Boot Camp Training in Aarhus

Who Should Attend?

• 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

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