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Data Analysis Boot Camp

Duration: 3 Days
Course Price: $1,995

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.

InformsTechtown Training is proud to announce that we are an offical Registered Education provider (REP) with Informs® for the Certified Analytics Professional (CAP®) Exam.

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 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.

InformsTechtown Training is proud to announce that we are an offical Registered Education provider (REP) with Informs® for the Certified Analytics Professional (CAP®) Exam.

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

If you have basic familiarity with Excel, this three-day course can teach you practical applied analysis techniques to leverage data for relatively common decision making methods.

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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