Course Overview:

The Data Analysis Boot Camp equips candidates with the knowledge, techniques and models to transform data into usable insights for making business decisions. The course simplifies complex concepts, breaks down math jargon and helps navigate complex symbols and equations. These skills enable candidates to zoom in on the most useful data and apply it in the real world. It also provides practical techniques for presenting findings to quickly make decisions that drive organizations forward. These tools include graphic presentation techniques and simplified models to transform the results of data analysis into digestible, easy-to-understand insights and usable recommendations.

Target Audience:

Anyone involved in operations, project management, business analysis, or management would benefit from this course. This training is invaluable for:

• Business Analysts, Business Systems Analysts, CBAP, CCBA
• Systems, Operations Research, Marketing, and other Analysts
• Project Managers, Program Managers, Team Leaders, PMP, CAPM
• Data Modelers and Administrators, DBAs
• IT Managers, Directors, VPs
• Finance Managers, Directors, VPs
• Operations Supervisors, Managers, Directors, VPs
• Risk Managers, Operations Risk Professionals
• Process Improvement, Audit or Internal Consultants and Staff
• Executives exploring cost reduction and process improvement options
• Professionals who want to show dedication to process improvement
• Senior staff who make or recommend decisions to executives

Learning Objectives:

• Identify opportunities, manage change and develop deep visibility into the organization
• Understand analytics, business intelligence and statistics terminology
• Practical applications of data analysis across business
• Data visualization and effective presentation of data analysis results
• Create more accurate projections while accounting for variance, error, and confidence intervals
• Using the array of data plots and chart formats to analyse underlying data trends and patterns through practical exercises
• Differentiating data "signal" from data "noise" – separating critical from extraneous data
• Understand and leverage different distribution models, and the real world applications
• In-depth practical statistics knowledge, and how it relates to risk, probability, results and action
• Develop a robust, practical understanding of probability theory – and how to leverage it
• Form and test hypotheses – using multiple methods to define and interpret projections
• Statistical inference and interpretation of population wide conclusions
• Substantial set of practical modeling skills
• Using computation to mine data, run simulations, find data clusters and critical attributes
• Application of data for practical use: Reporting, Dashboards, Metrics, Quality, Financial Modeling and more
• Hands-on predictive analytics knowledge including key terminology and algorithms
• Forecasting future results, finding opportunities for process improvement, and analyzing past performance
• Real-world labs using data analysis tools as per the course syllabus
• Applicability of data analysis skills and methods to candidates’ own scenarios with a data expert instructor over the three days

Prerequisites:

None

Course Materials:

Students will receive a course manual with presentation slides and reference materials.

Examination:

None

Technical Requirements:

For eBooks:

Internet for downloading the eBook

Laptop, tablet, Smartphone, eReader (No Kindle)

Adobe DRM supported software (e.g. Digital Editions, Bluefire Reader)

eBook download and activation instructions

Agenda:

Data Fundamentals

Course Overview and Level Set

Understanding "Real-world" Data

Types of Data

LAB: Hands-on – Profiling Data

Data-related Risk

Data Quality

LAB: Hands-on – Dealing with Duplicates

Relationships

LAB: Hands-On – Data Relationships using PowerPivot

Analysis Foundations

Statistical Practices: Overview

Domains of Data Analysis

Analyzing Data

Statistical Practices: Overview

Averages in Data

Central Tendency

LAB: Hands-On – Central Tendency

LAB: Hands-On – Linear Regression

Distributions

Correlation

LAB: Hands-On – Distributions in Consumer Finance Data

Analytical Graphics for Data

Analytics & Modeling

ROI & Financial Decisions

LAB: Hands-On – Helpful Financial Metrics in Excel

Using Financial Data

Random Numbers

Demo / Lab – Monte Carlo Analysis in Excel

Predictive Analytics

Demo / Lab – Using R for Powerful Analysis

Clustering

Data Modeling

Data Warehousing

Visualizing & Presenting Data

• Goals of Visualization
• Visualization Essentials
• Demo / Lab – Improving a Difficult Report
• Communicating Data-Driven Knowledge