Course Description
Objectives
Lesson objectives help students become comfortable with the course, and also provide a means to evaluate learning. Upon successful completion of this course, students will be able to: · 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
What you will learn
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: Getting 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
· Mitigating common risks
Data Quality
· Cleansing
· Duplicates
· SSOT
· Field standardization
· Identifying sparsely populated fields
· How to fix some common issues
LAB: Data Quality
Relationships
· Finding common attributes
· 1:N, N:N, 1:1
LAB: Relationships in a dataset
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
3. Analyzing Data
Averages in data
· Mean
· Median
· Mode
· Range
Central Tendency
· Variance
· Standard deviation
· Sigma values
· Percentiles
· Using these concepts to estimate things
LAB: Hands-On – Central Tendency
LAB: Hands-On – Linear Regression
Overview of commonly useful distributions
· Probability distribution
· Cumulative distribution
· Bimodal distributions
· Skewness of data
· Pareto distribution
Correlation
LAB: Distributions
Analytical Graphics for Data
· Categorical – bar charts
· Continuous – histograms
· Time series – line charts
· Bivariate data – scatter plots
· Distribution – box plot
4. Analytics & Modeling
ROI & Financial Decisions
Common uses of financial data
· Earned Value
· Actual Cost, BAC and EAC
· Expected Monetary Value
· Cost Performance/Schedule Performance Index
Common uses for random numbers
· Sampling
· Simulation
· Monte Carlo analysis
· Pseudo-random sequences
Demo / Lab – Random numbers in Excel
An introduction to Predictive Analytics
· A discussion about patterns
· Regression and time series for prediction
· Machine learning basics
· Tools for predictive analytics
Demo / Lab – Getting started with R
Understanding Clustering
· Segmentation
· Common algorithms
· K-MEANS
· PAM
Fundamentals of Data Modeling
· Architecture and analysis
· Stages of a data model
· Data warehousing
· Top-down vs. Bottom-up
Understanding Data Warehousing
· Context tables
· Facts
· Dimensions
· Star vs. Snowflake Schema
5. 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
Looking for Group eLearning Options?
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Request On-Site TrainingUpcoming Dates
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Location: , Virtual Live
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Date & Time: May 13, 2024 12:00 PM EST
$1,595.00
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Location: , Virtual Live
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Date & Time: Jun 19, 2024 08:30 AM EST
$1,595.00
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Location: , Virtual Live
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Date & Time: Jul 17, 2024 09:00 AM EST
$1,595.00
Course Dates
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Events
Cancelation Policy
If you cannot attend an event, you may send someone else in your place. If that isn’t an option for you, cancellations received up to five working days before the event are refundable, minus a registration service charge ($10 for one-day events; $25 for multiple-day events). After that, cancellations are subject to the entire seminar fee, which you may apply toward a future seminar. Please note that if you don’t cancel and don’t attend, you are still responsible for payment.
