Data Analysis Boot Camp

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: Getting 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
· Mitigating common risks

Data Quality
· Cleansing
· Duplicates
· SSOT
· Field standardization
· Identifying sparsely populated fields
· How to fix some common issues
LAB: Data Quality
Relationships
· Finding common attributes
· 1:N, N:N, 1:1
LAB: Relationships in a dataset
 
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

3. Analyzing Data
Averages in data
· Mean
· Median
· Mode
· Range
Central Tendency
· Variance
· Standard deviation
· Sigma values
· Percentiles
· Using these concepts to estimate things
LAB: Hands-On – Central Tendency
LAB: Hands-On – Linear Regression
Overview of commonly useful distributions
· Probability distribution
· Cumulative distribution
· Bimodal distributions
· Skewness of data
· Pareto distribution
Correlation

LAB: Distributions
Analytical Graphics for Data
· Categorical – bar charts
· Continuous – histograms
· Time series – line charts
· Bivariate data – scatter plots
· Distribution – box plot
4. Analytics & Modeling
ROI & Financial Decisions
Common uses of financial data
· Earned Value
· Actual Cost, BAC and EAC
· Expected Monetary Value
· Cost Performance/Schedule Performance Index
Common uses for random numbers
· Sampling
· Simulation
· Monte Carlo analysis
· Pseudo-random sequences

Demo / Lab – Random numbers in Excel
An introduction to Predictive Analytics
· A discussion about patterns
· Regression and time series for prediction
· Machine learning basics
· Tools for predictive analytics
Demo / Lab – Getting started with R
Understanding Clustering
· Segmentation
· Common algorithms
· K-MEANS
· PAM
Fundamentals of Data Modeling
· Architecture and analysis
· Stages of a data model
· Data warehousing
· Top-down vs. Bottom-up
Understanding Data Warehousing
· Context tables
· Facts
· Dimensions
· Star vs. Snowflake Schema

5. 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