# Data Analytics Foundations Course

Canonical URL: <https://www.graduateschool.edu/courses/data-analytics-foundations>

## Overview

Learn the fundamentals of data analytics in this beginner-friendly course, starting with descriptive and inferential statistics and core data distribution concepts. You’ll see how organizations use predictive and prescriptive analytics to forecast outcomes and guide decision-making, while exploring the tools and techniques different industries rely on.

Along the way, you’ll examine key statistical algorithms, theorems, and modeling approaches that form the foundation of modern data analysis.

## What you'll learn

- Understand core statistical concepts such as measures of central tendency, data dispersion, and the normal distribution
- Explore descriptive and inferential statistics, including probability distributions such as binomial and Poisson
- Learn to analyze and forecast data using correlation, linear regression, and multiple regression models
- Apply predictive analytics using tools such as trendlines, moving averages, and scenario modeling
- Create clear data visualizations with charts, histograms, icon sets, color scales, sparklines, and pivot tables
- Discover prescriptive analytics methods like Solver and linear programming to support optimized decision-making

## Prerequisites

Students should feel comfortable using Excel at a basic level. Experience equal to our [Excel Level II: Intermediate](https://www.nobledesktop.com/classes/intermediate-excel-classes) class is strongly recommended, but not required.

## Curriculum

#### Basic Data Analysis

- Measures of Central Tendency
- Measures of Position
- Measures of Dispersion
- The Normal Curve
- Descriptive Statistics

#### Predictive Analytics I

- Forecasting
- Series Forecast

#### Data Visualization I

- Charts
- Icon Sets
- Histograms
- Moving Average

#### Predictive Analytics

- Correlation
- Regression - overview
- Regression - analysis
- Linear regression
- Multiple regression

#### Probability

- Probability I
- Probability II
- Binomial Probability
- Poisson Probability

#### Prescriptive Analytics I

- What If Analysis
- Data Table (3 variables)
- Scenario Manager
- Scenario Manager - Pivot

#### Data Visualization II

- Sparklines
- Color Scales
- Drawing Shapes
- Pivot Tables
- Pivot Charts

#### Prescriptive Analytics II

- Solver - overview
- Linear Programming
- The Solver model
- Non-Linear Programming
- Evolutionary Solver

## Schedule
- May 26, 2026 – May 27, 2026 — Live Online
- May 31, 2026 – Jun 7, 2026 — Live Online
- Jun 16, 2026 – Jun 25, 2026 — Live Online
- Jul 13, 2026 – Jul 14, 2026 — Live Online
- Aug 31, 2026 – Sep 1, 2026 — Live Online
- Sep 29, 2026 – Oct 8, 2026 — Live Online
- Oct 18, 2026 – Oct 25, 2026 — Live Online
- Oct 19, 2026 – Oct 20, 2026 — Live Online

## Instructors

### Bruce Gay — Instructor

Bruce is an engaging trainers and program manager who brings 25+ years practical experience to deliver effective and experiential training to students. Able to engage adult learners with a range of backgrounds and professional experiences. Successful at building effective stakeholder relationships and coordinating multi-disciplinary teams for solution delivery.

Bruce has over 25 years of project and program management experience across multiple industries. He has a Masters degree from The George Washington University and a B.A. from the University of North Carolina Chapel Hill. 

Bruce currently runs his own freelance training and consulting business, helping project managers and team leaders improve their business skills, become better leaders, and achieve professional greatness. 

Bruce is a well-received speaker in the areas of design thinking, project management, cross-team collaboration, and AI tools for projects, and has presented at regional and international conferences.

### Steve Pesklo — Instructor

Steve is an energetic trainer who focuses on applying technical concepts to everyday work practices. He is the founder and president of SoftLake Solutions, a company that specializes in providing data and AI applications to identify fraud for Internal Audit, Criminal Investigations, Forensic Accounting, Privacy, and Compliance.

Steve brings a large amount of experience across multiple industries and government agencies. He is an expert in implementing large data analysis projects across the world, including Inland Revenue in the UK and Argentina, New Zealand, Africa and across Europe. Previously, he was the manager of Data Architecture and Data Services for a large mortgage company. He is a frequent speaker on data analytics and project management topics and speaks fluent German. He has been teaching at the Graduate School for over 10 years.

Steve has an M.B.A. from the University of St. Thomas and a B.S. in Computer Science from California Lutheran University and the Universität Salzburg in Austria. He is certified as a Certified Fraud Examiner (CFE), Project Management Professional (PMP), and a Certified ScrumMaster (CSM).

### Joe Mlakar — Instructor

Joe has over 27 years of Federal Government and military service and has been a part-time instructor with Graduate School USA since 2023. He enjoys using his technical knowledge in Operations Research to teach his students to provide organization and structure to complex processes, and apply advanced analytical techniques to help leaders make better decisions. Joe is based in Fort Collins, Colorado.

## Pricing

**Tuition:** $595