Data Analysis Boot Camp

Data Analysis Boot Camp Course Details:

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.

*Please note, if you are taking this class as part of the St. Louis University Certificate requirements, there is a $500 fee to claim your certificate once you have completed ALL requirements.

Dec 4 2024

December 4 - 6, 2024 | 8:30 AM - 4:30 PM (EST)

Date: 12/04/2024 - 12/06/2024 (Wednesday - Friday) | 8:30 AM - 4:30 PM (EST)
Location: ONLINE (Virtual Classroom Live)
Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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

*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.

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

This data analysis training course is designed for the following professions:

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

Data Analysis Boot Camp

Data Analysis Boot Camp Course Details:

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