Description
This course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality and how to translate data into analysis of business problems to begin making informed, intelligent decisions. 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 the decisions that drive your organization forward.
In–Class Exercises, Demos, and Real-World Case Studies This data analysis 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.
Who should attend
- Business Analyst, Business Systems Analyst, CBAP, CCBA
- Systems, Operations Research, Marketing, and other Analysts
- Project Manager, Program Manager, Team Leader, PMP, CAPM
- Data Modelers and Administrators, DBAs
- IT Manager, Director, VP
- Finance Manager, Director, VP
- Operations Supervisor, Manager, Director, VP
- Risk Managers, Operations Risk Professionals
- Process Improvement, Audit, Internal Consultants and Staff
- Executives exploring cost reduction and process improvement options
- Job seekers and those who want to show dedication to process improvement
- Senior staff who make or recommend decisions to executives
Prerequisites
If you have basic familiarity with Excel, this three-day course can teach you practical applied analysis techniques to leverage data for relatively common decision making methods.
Additionally, although it is not mandatory, students who have completed the self-paced Introduction to R eLearning course have found it very helpful when completing this course.
Course Objectives
- 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
Outline: Data Analysis Boot Camp (DABC)
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
