Course Taxonomy: Data & AI

Building Microsoft Power BI Dashboards

Posted on October 23, 2020 by -

Part 1: What is BI?

We’ll start out by covering business intelligence basics to lay the groundwork for an intelligent approach to reporting and visualizing data.

  • Business Intelligence Overview
  • Common Challenges
  • Benefits of Power BI

Part 2: Getting started with Power BI

Power BI is an extensive toolbox for working with and analyzing data. We’ll cover the fundamentals of the service, how Power BI’s features are organized, and immediately orient towards dashboards and visualization.

  • Overview & Pricing/Licensing
  • Components of Power BI
  • Building Blocks of Power BI
  • Quick Tour of Power BI Service

Part 3: Building simple reports

Reports are the first step in graphically communicating information related to your data. In this section of the class, you’ll learn to use and navigate the types of datasets you encounter every day, and how to use them to begin shaping meaningful communication.

  • Importing excel data
  • Using preexisting datasets
  • Creating visualizations
  • Using slicers

Part 4: Dashboards

In this section, we’ll cover how to create and use dashboards for common needs. By the end of this section, you’ll understand what’s realistic to expect from your PowerBI dashboards and how to set them up, share them, and produce valuable insights with your team quickly.

  • Dashboard expectations vs. features
  • Using KPI
  • Create and Configure a Dashboard
  • Shared Dashboards with your Organization
  • Pinning visuals

Part 5: Exploring data

In this final section of class, we’ll get a bit more granular about navigating, analyzing and communicating about your data. By the time we conclude, you’ll be ready to start applying what you’ve learned in your own real-world situations. 

  • Use Quick Insights
  • Display Visuals and Tiles Full-Screen
  • Edit Tile Details
  • Get More Space on Your Dashboard
  • Ask Questions of your Data with Natural Language
  • Advanced Navigation

Splunk Boot Camp

Posted on October 17, 2020 by -

*All lab exercises are run in a Linux environment. A Windows environment can be provided upon request. 

Part 1: Introduction to Splunk

  1. What’s Splunk?
  2. Authentication Methods
  3. Access Controls & Users
  4. Products, Licensing, and Costs
  5. Quick Tour Guide: User Interface
  6. Exercise: Lab Environment and Configuration

Part 2: Indexes

  1. Splunk Data
  2. What are Indexes?
  3. What are Indexers?
  4. Exercise: Create Your First Index
  5. Search-Head
  6. Index Clusters
  7. Index Pipeline
  8. Exercise: Upload Data Manually
  9. Events
  10. Fields & Field Extraction
  11. Exercise: Using the Field Extractor Tool
  12. Forwarders
  13. Metrics
  14. Exercise: Using the Forwarder to Send Data
  15. Removing Data

Part 3: Splunk Architecture

  1. Components of Splunk Deployments
  2. Deployment Scenarios

Part 4: Search Processing Language

  1. What is Search Processing Language (SPL)?
  2. Searching Operators
  3. Search Commands
  4. Search Pipeline
  5. Exercise: Search Examples
  6. Subsearches
  7. Commonly Used Search Commands
  8. Exercise: Search Examples II
  9. Drilldowns
  10. Lookups
  11. Exercise: Using Lookups
  12. Optimize Searches
  13. Exercise: Search Examples III

Part 5: Dashboard & Visualizations

  1. Dashboards in Splunk
  2. Creating Dashboards
  3. Visualization Types
  4. Search as Reports
  5. Dashboards
  6. Exercise: Creating a Dashboard
  7. Drilldown
  8. Forms
  9. Exercise: Add Input Forms
  10. Exercise: Drilldown

Part 6: Alerts

  1. Creating Alerts
  2. Scheduling Alerts
  3. Alerts Notifications
  4. Exercise: Creating Alerts

Part 7: Scheduled Reports

  1. Creating Scheduled Reports
  2. Exercise: Create a Scheduled Report

Part 8: Putting All Pieces Together

Exercise: As a final lab, you’ll configure a typical scenario when using Splunk. You'll install and configure an NGINX, then the Splunk forwarder to collect logs in Splunk. The idea is that you can apply everything you've learned within the Bootcamp: creating searches, visualizations, dashboards, etc.

Microsoft Power BI Boot Camp

Posted on October 17, 2020 by -

Part 1: What is BI

  1. Business Intelligence Overview
  2. Evolution of Business Intelligence
  3. Common Challenges
  4. Benefits of Power BI

Part 2: Getting Started with Power BI

  1. Overview & Pricing/Licensing
  2. Components of Power BI
    1. Power BI Desktop
    2. Power BI Mobile
    3. Power BI Embedded
    4. Power BI Gateway
  3. Building Blocks of Power BI
    1. Datasets
    2. Visualization
    3. Reports
    4. Tiles
    5. Dashboards
  4. Power BI Workflows
  5. Resources for Inspiration

Demo: Quick Tour of Power BI Service

Lab Exercise: Quick Tour of Power BI Service

Part 3: Getting Data

  1. Navigating Power BI Desktop
  2. Connect to Data Sources in Power BI Desktop
    1. File Data Sources
    2. Database Data Sources
    3. Azure Data Sources
    4. Online Service Data Sources
    5. Miscellaneous Data Sources
  3. Clean and Transform your Data
  4. Advanced Data Sources
    1. Advanced Editor
    2. Shaping Data
    3. Applied Steps
  5. Transformations
  6. Cleaning Irregularly Formatted Data
  7. Data Types
  8. Combining Data
  9. AI Analytics
    1. Text Analytics
    2. Vision
    3. Azure Machine Learning
    4. Invoking the Shared Models
    5. Considerations and Limitations

Demo: Quick Tour of Power Query Editor

Lab Exercise: Import Data and Create Queries with Power BI Desktop

Lab Exercise: Transform Data

Lab Exercise: Combining Data

Lab Exercise: Create a Report using Power BI Desktop

Lab Exercise: Publish a PBIX File to the Power BI Service

Part 4: Power BI and Excel

  1. Excel Integration
    1. Import an Excel Table into Power BI
    2. Use Excel as a Dataset
    3. Import Excel Files with Data Models
    4. Connect, Manage and View Excel in Power BI
  2. Publishing and Sharing

Part 5: Modeling Data

  1. Overview
  2. How to Manage Your Data Relationships
  3. Create Calculated Columns
  4. Optimizing Data Models for Better Visuals
  5. Create Measures and Work with Time-Based Functions
  6. Create Calculated Tables
  7. Explore Time-Based Data
  8. Grouping
  9. Binning
  10. Hierarchies

Demo: Modeling Relationships Column by Example Conditional Columns, Groups, Hierarchies

Part 6: Visualizations

  1. Overview
  2. Create and Customize Simple Visualization
    1. Numerical Fields
    2. Text Fields
    3. Geographic Fields
  3. Modify Colors Insert Static Objects, and Set Page Properties
  4. Styling with Shapes, Text Boxes, and Images
  5. Page Layout and Formatting
  6. Z-Order of Report Elements
  7. Customize Visuals with Summarizations
  8. How to:
    1. Use Combination Charts
    2. Manage Slicers and Sync Slicer
    3. Use Map Visualizations
    4. Implement Tables and Matrixes
    5. Apply Conditional Formatting
    6. Interpret Scatter Charts
    7. Work with Water & Funnel Chart
    8. Use Gauges and Single Number Cards
  9. Creating Complex Interactions Between Visuals
  10. Advance Concepts and New Features
    1. Decomposition Tree
    2. Key Influencers
    3. Insights
    4. Visual Hierarchies and drill-down behavior
    5. Custom Visualizations
    6. Drillthrough
    7. Analytics Pane
    8. Forecasting
    9. Tooltips
    10. Chiclet Slicer
    11. Hierarchy Slicer
    12. Synoptic Panel
  11. Other Powerful Custom Visualizes
    1. Pivot Slicer
    2. Smart Filter
    3. Hierarchy Filter
    4. Card Browser
    5. Visio Visual
    6. Infographic Designer
    7. D3JS Visualizer

Demo: Visualizations, Slicers, and Advanced Interactions

Part 7: Publishing and Sharing

  1. Print and Export Power BI dashboards
  2. Creating Content Packs
  3. Publishing to Web
  4. Embed in SharePoint
  5. Export to PowerPoint
  6. Power BI Mobile
  7. Creating Workspaces in Power BI

Part 8: Exploring Data

  1. Overview
  2. Use Quick Insights
  3. Create and Configure a Dashboard
  4. Shared Dashboards with your Organization
  5. Display Visuals and Tiles Full-Screen
  6. Edit Tile Details
  7. Get More Space on Your Dashboard
  8. Ask Questions of your Data with Natural Language
    1. Custom Q&A suggestions
    2. Adding Q&A to a Report
    3. Adding a Q&A to a Report
    4. Q&A Tooling
    5. Review Questions
    6. Teach Q&A
    7. Manage Terms
    8. Data Sources for Q&A
    9. Bulk Synonyms
    10. Q&A Best Practices
  9. Bookmarks
  10. Themes

Demo: Quick sights

Demo: Q&A in Dashboards and Reports

Demo: Bookmarks

Part 9: Administration & Security

  1. Securing Content in Power BI
    1. My workspace
    2. Sharing a Dashboard
    3. Sharing to Web
    4. Admin Access
  2. Row-level Security
  3. Permissions
  4. Defining and Creating toles within Power BI Desktop
  5. Row-Level Security
  6. Managing Data Capacity
  7. Subscriptions
  8. Resources

Part 10: Introduction to DAX

  1. DAX Calculation Types
    1. Calculated Columns
    2. Calculated Measures
  2. DAX Functions
    1. Aggregation Functions
    2. Counting Functions
    3. Logical Functions
    4. Information Functions
    5. Text Functions
    6. Date Functions
  3. Using Variables in DAX Expressions
  4. Table Relationships in DAX
  5. DAX Tables and Filtering
  6. Quick Measures
  7. What-if Parameters
  8. Dynamic Labeling
  9. Resources

Demo: Quick Measures

Demo: What-if

Demo: Dynamic Labeling

Hands-on Lab Exercises:

  • Retail Sales Exercise
  • Retail Analysis – Overview
  • Retail Analysis – District Monthly Sales
  • Retail Analysis – New Stores
  • Customer Profitability Exercise
  • Customer Profitability – Team Scorecard
  • Customer Profitability – Industry Margin Analysis
  • Customer Profitability – Executive Scorecard
  • Opportunity Analysis: Exercise
  • Opportunity Analysis – Opportunity Count
  • Opportunity Analysis – Revenue Overview
  • Opportunity Analysis – Upcoming Opportunities
  • Opportunity Analysis – Region Stage Counts
  • Opportunity Analysis Data Model

Artificial Intelligence Implementation Boot Camp

Posted on October 17, 2020 by -

Part 1: Introduction

  1. Working definitions: AI, Machine Learning, Deep Learning,  Data Science & Big Data 
  2. State of AI: summarizing major analysts’ statistics & predictions
  3. Summarizing AI misinformation
  4. Effects on the job market
  5. Today’s AI use  cases
    • Where it works well
    • Where it doesn’t work well
  6. What do high profile uses have in common?
  7. Addressing legitimate concerns & risks

Case study break: We will introduce the class to three real-world use cases – one in finance, one in health science, and one in general operations. In small groups, you will discuss implications of the cases and see if you and your peers can spot any parallel opportunities in your own business.

Part 2: The Big Data Prerequisite

  1. Evaluating your big data practice
  2. State of tools – understanding intelligent big data stacks
    • Visualization and Analytics
    • Computing
    • Storage
    • Distribution and Data Warehousing
  3. Strategically restructuring enterprise data architecture for AI
  4. Unifying data engineering practices
  5. Datasets as learning data
  6. Defeating Bias in your Datasets
  7. Optimizing Information Analysis
  8. Utilizing the IoT to amass a large amount of data

Part 3: Implementing Machine Learning

  1. Examine pillars of a practicing AI team
    • Business case
    • Domain expertise
    • Data science
    • Algorithms
    • Application integration
  2. Bettering Machine Learning Model Management
  3. State of tools – understanding intelligent machine learning stacks
  4. Machine Learning Methods and Algorithms
    • Decision Trees
    • Support Vector Machines
    • Regression
    • Naïve Bayes Classification
    • Hidden Markov Models
    • Random Forest
    • Recurrent Neural Networks
    • Convolutional Neural Networks
  5. Developing Validation Sets
  6. Developing Training Sets
  7. Accelerating Training
  8. Encoding Domain Expertise in Machine Learning
  9. Automating Data Science
  10. Deep Learning

Example: TensorFlow – We will take a look at Google’s TensorFlow as a tool for integrating machine learning features.  We’ll come away from the exercise with an understanding of the programming skills needed to leverage TensorFlow and the impacts of normal application workflow.

Part 4: Creating Concrete Value

  1. Opportunities for automation
  2. Understanding automation vs. job displacement vs. job creation
  3. Finding hidden opportunities through improved forecasting
  4. Production and operations
  5. Adding AI to the Supply Chain
  6. Marketing and Sales Applications
    • Predict Customer Behavior
    • Target Customers Efficiently
    • Manage Leads
    • AI-powered content creation
  7. Enhancing UX and UI
  8. Next-Generation Workforce Management
  9. Explaining Results

Use case breakout: Scoring the criteria for three potential applications. In groups, we’ll evaluate application use cases for machine learning: Medical imaging, electronic medical records, and genomics. We’ll grade each use case based on a scorecard for the following:

  • Quantity of data
  • Quality of data
  • ML techniques

Part 5: Machine intelligence as part of the customer experience

  1. IoT and the role of machine learning
  2. Projects based on customer & user needs
  3. Handling customer inquiries with AI
  4. Creating empathy-driven customer facing actions
  5. Narrowing down intent
  6. AI as part of your channel strategy

Part 6: Machine Intelligence & Cybersecurity

  1. How can ML help with security?
    • Advance cyber security analytics
    • Developing defensive strategies
    • Automating repetitive security tasks
    • Close zero-day vulnerabilities
  2. How are attackers leveraging ML and AI?
  3. Building up trust towards automated security decisions and actions
  4. Automated application monitoring as a security layer
  5. Identifying Vulnerabilities
  6. Automating Red Team/Blue Team Testing Scenarios
  7. Modeling AI after previous security breaches
  8. Automating and streamlining Incident Responses
  9. How use deep learning AI to detect and prevent malware and APTs
  10. Using natural language processing
  11. Fraud detection
  12. Reducing compliance testing & cost

Part 7: Filling the Internal Capability Gap

  1. Assessing your technological and business processes
  2. Building your AI and machine learning toolchain
  3. Hiring the right talent
  4. Developing talent
  5. How to make AI more accessible to people who are not data scientists
  6. Launching pilot projects

Part 8: Conclusion and Charting Your Course

  1. Review
  2. Charting Your Course
    • Establishing a timeline
  3. Open Discussion

Introduction to Data Analysis

Posted on October 17, 2020 by -

Part 1: Data and Information

  1. Data in the Real World
  2. Data vs. Information
  3. The Many “Vs” of Data
  4. Structured Data and Unstructured Data
  5. Types of Data

Part 2: Data Analysis Defined

  1. Why do we analyze data?
  2. Data Analysis Mindset
  3. Data Analysis Steps
  4. Data Analysis Defined
  5. Descriptive Statistics vs Inferential Statistics

Part 3: Types of Variables

  1. Categorical vs Numerical
  2. Nominal Variables
  3. Ordinal Variables
  4. Interval Variables
  5. Ratio Variables

Part 4: Central Tendency of Data

  1. (Arithmetic) Mean
  2. Median
  3. Mode

Part 5: Basic Probability

  1. Probability Uses In Business
  2. Ways We Can Calculate Probability
  3. Probability Terms
  4. Calculating Probability
  5. Calculating Probability from a Contingency Table
  6. Conditional Probability
  7. Frequency Distribution

Part 6: Distributions, Variance, and Standard Deviation

  1. Discrete Distributions
  2. Continuous Distributions
  3. Range
  4. Quartiles
  5. Variance
  6. Standard Deviation
  7. Population vs. Sample
  8. Application of the Standard Deviation
    • Standard Deviation and the Normal Distribution
    • Sigma (σ) Values (Standard Deviations)
  9. Bimodal distribution
  10. Skew and Summary
  11. Other Distributions
    • Poisson Distribution
    • Exponential Distribution
    • Pareto Distribution (“80/20”)
    • Log Normal Distribution
  12. Distributions in Excel
     

Part 7: Fitting Data

  1. Bivariate Data (Two Variables)
  2. Covariance and Correlation
  3. Simple Linear Regression
  4. Linear Regression
  5. Fitting Functions
    • Linear Fit
    • Polynomial Fit
    • Power-Law Fit

Part 8: Predictive Analytics Overview

  1. Monte Carlo Method

Data Analysis Boot Camp

Posted on October 17, 2020 by -

Part 1: The Value and Challenges of Data-Driven Disruption

  1. Objectives and expectations
  2. Hurdles to becoming a data-driven organization
  3. Data empowerment
  4. Instilling data practices in the organization
  5. The CRISP-DM model of data projects

Part 2: Tying Data to Business Value

  1. What constitutes data-driven value
  2. Requirements gathering: How to approach it
  3. Kanban for data analysis
  4. Know your customers
  5. Stakeholder cheat sheets
  • EXERCISE: Data-driven project checklist
  • LAB: Data analysis techniques: Aggregations

Part 3: Understanding Your Data

  1. Data defined
  2. Data versus information
  3. Types of data
    1. Unstructured vs. Structured
    2. Time scope of data
    3. Sources of data
  4. Data in the real world
  5. The 3 V’s of data
  6. Data Quality
    1. Cleansing
    2. Duplicates
    3. SSOT
    4. Field standardization
    5. Identify sparsely populated fields
    6. How to fix common issues
  • LAB: Prioritizing data quality

Part 4: Analyzing Data

  1. Analysis foundations
    1. Comparing programs and tools
    2. Words in English vs. data
    3. Concepts specific to data analysis
    4. Domains of data analysis
    5. Descriptive statistics
    6. Inferential statistics
    7. Analytical mindset
    8. Describing and solving problems
  2. Averages in data
    1. Mean
    2. Median
    3. Mode
    4. Range
  3. Central tendency
    1. Variance
    2. Standard deviation
    3. Sigma values
    4. Percentiles
  4. Demystifying statistical models
  5. Data analysis techniques
  • LAB: Central tendency
  • LAB: Variability
  • LAB: Distributions
  • LAB: Sampling
  • LAB: Feature engineering
  • LAB: Univariate linear regression
  • LAB: Prediction
  • LAB: Multivariate linear regression
  • LAB: Monte Carlo simulation

Part 5: Thinking Critically About Your Analysis

  1. Descriptive analysis
  2. Diagnostic analysis
  3. Predictive analysis
  4. Prescriptive analysis

Part 6: Data Analysis in the Real World

  1. Deployment of analyses
  2. Best practices for BI
  3. Technology ecosystems
    1. Relational databases
    2. NoSQL databases
    3. Big data tools
    4. Statistical tools
    5. Machine learning
    6. Visualization and reporting tools
  4. Making data useable

Part 7: Data Visualization & Reporting

  1. Best practices for data visualizations
    1. Visualization essentials
    2. Users and stakeholders
    3. Stakeholder cheat sheet
  2. Common presentation mistakes
  3. Goals of visualization
    1. Communication and narrative
    2. Decision enablement
    3. Critical characteristics
  4. Communicating data-driven knowledge
    1. Formats and presentation tools
    2. Design considerations

Part 8: Hands-On Introduction to R and R Studio

  1. What is R?
  • LAB: Intro to R Studio
  • LAB: Univariate linear regression in R
  • LAB: Multivariate linear regression in R