Data Analysis Boot Camp
ASPE | Techtown
How long?
- 3 days
- online, in person
What are the topics?
ASPE | Techtown
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Essentials
- What do past participants have to say about the course?
Full
- How many participants were promoted within three years after graduation?
- How did this course affect participants' professional trajectories?
- How many participants got their salary increased within two years after completing the program?
- What do past participants have to say about the course?
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
About the course
This three-day course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality. You will learn how to make more informed, intelligent business decisions by analyzing data using Excel functions and the R programming language.
You will 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 decisions that drive your organization forward.
In–Class Exercises, Demos, and Real-World Case Studies This data analyst 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.
Labs for this course are primarily in Microsoft Excel, however, students will get an opportunity to practice using R in some labs. Labs for this course can also be taught using the Python programming language for private onsite clients only.
In This Data Analysis Training Course, You Will:
- 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
Course Outline
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
Experts
Kevin Hill
Kevin Hill, Ph.D., is an expert living at the intersection of human behavior and artificial intelligence. He received his Ph.D. in Cognitive Neuroscience from the University of California, Davis, and presented internationally recognized research in the fields of Neuroeconomics and Human Decision ...
Sam Polsky
As one of the ASPE instructor team’s most senior experts, Sam Polsky does more than just teach. Sam leads teams of subject-matter experts and instructional designers to design and deliver in-depth, extended technology training curricula to some of the largest companies in the world. With a centra...
Erick Polsky
Erick Polsky's career integrating application development with training delivery started back in 1989 when he developed and introduced the first online multimedia training content system to Harvard University. Since then he has honed his business, programming, and database skills to provide high...
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Data Analysis Boot Camp at ASPE | Techtown
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