Data Analysis Bootcamp

Who should attend

This course is intended for any individuals with a basic understanding of Microsoft Excel, a professional interest in data analysis, and the desire to leverage data to help make strategic business decisions. Typical students include business analysts, project managers, and operations supervisors among many others.

Team Pass by Learnit

Data Analysis Bootcamp

From $0
Two, 2-hour Modules (9am - 11:30am PT)

This Data Analysis Boot Camp is designed to teach students how to apply practical analysis techniques to leverage data for common decision making methods. By attending the course, students will learn to leverage critical analytical skills in order to truly understand and act on company data.

Specifically, students will learn about:

  • Understanding and reviewing data quality
  • Translating data in analysis of business problems
  • Making decisions informed by such data
  • Analysis and statistical techniques
  • Communicating data findings clearly, and concisely, to stakeholders

Students will benefit from in-class exercises, professional demonstrations, and case studies based on real companies, and real data.

Course ID
L0109
Available times
June 17 & June 18, 2021
9:00am-11:30am
June 17 & June 18, 2021
9:00am-11:30am
June 17 & June 18, 2021
9:00am-11:30am
June 17 & June 18, 2021
9:00am-11:30am
June 17, 2021
9:00am-11:30am
June 17, 2021
9:00am-11:30am
June 17, 2021
9:00am-11:30am
June 17, 2021
9:00am-11:30am
June 17, 2021
9:00am-11:30am

Course Outline

Module 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: Get 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
    • Mitigation of Risk
  • Data Quality
    • Cleansing
    • Duplicates
    • SSOT
    • Field standardization
    • Identify sparsely populated fields
    • How to fix common issues
  • LAB: Data Quality

Module 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

Module 3: Analyzing Data

 

  • Averages in Data
    • Mean
    • Median
    • Mode
    • Range
  • Central Tendency
    • Variance
    • Standard Deviation
    • Sigma Values
    • Percentiles
    • Use Concepts for Estimating
  • LAB: Hands-On – Central Tendency
  • Analytical Graphics for Data
  • Categorical
    • Bar Charts
  • Continuous
    • Histograms
  • Time Series
    • Line Charts
  • Bivariate Data
    • Scatter Plots
  • Distribution
    • Box Plot

Module 4: Analytics & Modeling

  • Overview of Commonly Useful Distributions
    • Probability Distribution
    • Cumulative Distribution
    • Bimodal Distributions
    • Skewness of Data
    • Pareto Distribution
      • Correlation
    • LAB: Distributions
    • Predictive Analytics
    • A Discussion about Patterns
    • Regression and Time Series for Prediction
    • LAB: Hands-On – Linear Regression
      • Simulation
    • Pseudo-random Sequences
    • Monte Carlo Analysis
    • Demo / Lab: Monte Carlo in Excel
  • Understanding Clustering
  • Segmentation
  • Common Algorithms
  • K-MEANS

Module 5: Hands-On Introduction to R and R Studio

  • R Basics
  • Descriptive Statistics
  • Importing and Manipulating Data
  • R Scripting
  • Data Visualization with R
  • Regression in R
  • K-MEANS in R
  • Monte Carlo in R
  • Demo/Lab: Hands-on R work

Module 6: 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

Skills covered

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Data Analysis Bootcamp

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