Data Analysis Boot Camp
Course Details

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Data Analysis Boot Camp

Category ID Duration Price
Business Analysis 13412 3 Day(s) $1,875.00
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Description

This course, organized into key topic areas, leverages straightforward business examples to explain practical techniques for understanding and reviewing data quality and how to translate data into analysis of business problems to begin making informed, intelligent decisions. 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 the decisions that drive your organization forward. 

This data analysis 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.

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

Prerequisites

Threre are no prerequisites for this course.

Outline

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

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