Course Taxonomy: Analytics

Advanced Splunk Boot Camp

Posted on August 27, 2024 by -

Advanced Splunk Boot Camp

Part 1: Advanced Data Ingestion

  • Advanced Indexing Concepts
  • Handling High Volume Data
  • Data Parsing and Transformation
  • Exercise: Advanced Data Parsing Techniques

Part 2: Advanced Search Processing Language (SPL)

  • Advanced Search Commands
  • Data Models and Pivots
  • Creating and Using Macros
  • Exercise: Writing Advanced SPL Queries
  • Custom Commands and Scripts
  • Exercise: Developing Custom Commands
  • Transaction Searches and Anomalies
  • Exercise: Complex Searches and Data Correlation

Part 3: Performance Optimization

  • Search Performance Tuning
  • Resource Management
  • Index and Search Head Performance Optimization
  • Exercise: Optimizing Search Performance
  • Monitoring Console and Usage Dashboards
  • Exercise: Using Monitoring Console for Optimization

Part 4: Security and Monitoring

  • Role-Based Access Control (RBAC)
  • Data Integrity and Confidentiality
  • Auditing and Monitoring User Activity
  • Exercise: Implementing Security Best Practices
  • Incident Detection and Response
  • Exercise: Building Incident Response Dashboards

Part 5: Advanced Dashboard and Visualization

  • Advanced Dashboarding Techniques
  • Custom Visualization Options
  • Integrating with External Systems
  • Exercise: Creating Advanced Dashboards
  • Real-time Dashboards and Alerts
  • Exercise: Building Real-time Monitoring Dashboards

Part 6: Splunk Machine Learning Toolkit



  • Introduction to the Splunk Machine Learning Toolkit
  • Building Machine Learning Models in Splunk
  • Using Pre-built Machine Learning Algorithms
  • Exercise: Implementing Machine Learning Use Cases
  • Anomaly Detection and Predictive Analytics
  • Exercise: Building and Applying Predictive Models
  • Monitoring and Tuning Machine Learning Models

Basic Data Management – A Comprehensive Overview eLearning

Posted on January 4, 2024 by -

Part 1: Understanding what data management is and why it is an important asset

  • Why we need data management
  • Who and what is impacted by data
  • Data as an organizational asset

Part 2: Understanding strategic data priorities

  • Prioritize data assets 
  • What’s at stake if we fail to properly manage data?
  • How data relates to AI

Part 3: Data Strategy

  • Seven goals
  • Guiding Principles

Part 4: Overview of the Data Lifecycle

  • Phases of the lifecycle
  • Data Framework

Part 5: Where Data Management Fits

  • Manage for planning
  • Planning key questions
  • Collect key data and questions
  • Developing a quality insurance plan

Part 6: Manage Across the Lifecycle

  • What is data architecture? 
  • What is data governance? 
  • Core data stewardship concepts 
  • Data security and privacy

Part 7: Final Assessment

Data Visualization eLearning

Posted on June 15, 2023 by -

Part 1: Data Visualization

  • Understand what data visualization and its various benefits
  • Learn each step of the Data Visualization Process
  • Learn how AI and data visualization are interconnected

Part 2: Types of Charts and Graphs

  • Understand when to use popular charts and graphs
  • Learn the advantages and disadvantages of popular charts and graphs
  • Learn design tips for popular charts and graphs  

Part 3: Reporting Options

  • Know what different reporting options are available
  • Know which reporting option is appropriate for an audience
  • Learn when to self-serve data and when not to 

Part 4: Design Best Practices

  • Understand different visual design challenges
  • Learn various design tips when making visualizations

Part 5: How to Present Your Data

  • Deal with different data visualization stakeholders
  • Effectively tell a story using data visualizations
  • Avoid common presentation mistakes  

Part 6: Final assessment

 

 

Mastering DAX for Microsoft Power BI

Posted on November 5, 2020 by -

Part 1: Introduction to DAX

  1. What is DAX?
  2. Why use DAX with Power BI
  3. Calculated Columns vs Measures
  4. Types of Functions
    1. Logical Functions
    2. Table manipulation functions
    3. Date and time functions
    4. Filter functions
    5. Financial functions
    6. Math and Trig functions 
    7. Parent and Child functions
    8. Relationship functions 
    9. Statistical functions
    10. Text functions
    11. Time intelligence functions

Part 2: Logical functions

  1. TRUE
  2. FALSE
  3. IF
  4. IFERROR
  5. AND
  6. OR
  7. SWITCH
  8. COALESCE

Part 3: Tables

  1. Tables Overview
  2. Filtering Tables
    1. FILTERS
    2. TOPN
  3. Summarizing Tables
    1. SUMMARIZE
    2. SUMMARIZECOLUMNS
    3. ROLLUP
    4. GROUPBY
  4. Generating Tables
  5. Distinct Values
    1. DISTINCT column
    2. DISTINCT table
  6. Joining Tables
    1. CROSSJOIN
    2. NATURALINNERJOIN
    3. NATURALLEFTOUTERJOIN
  7. Adding Values
    1. Columns
    2. Missing items
  8. Table Constructor

Part 4: Date and Time Functions

  1. Date and Time Functions Overviews
  2. Units of Time
  3. Creating a Calendar
    1. CALENDAR
    2. CALENDARAUTO
  4. Dates
  5. TODAY
  6. NOW
  7. TIME
  8. TIMEVALUE

Part 5: Filter Functions

  1. Filter Functions Overview
  2. Filtering Tables
    1. FILTER
    2. REMOVEFILTERS
    3. ALL (ALL, ALLCROSSFILTERED, ALLEXCEPT, ALLNOBLANKROW, & ALLSELECTED)
    4. CALCULATETABLE
    5. KEEPFILTERS
  3. CALCULATE
  4. LOOKUPVALUE
  5. SELECTEDVALUE

Part 6: Financial functions

  1. Accrued Interest
    1. ACCRINT
    2. ACCRINTM
  2. Depreciation
    1. DB
    2. DDB

Part 7: Relationships Functions

  1. CROSSFILTERS
  2. RELATED
  3. RELATEDTABLE
  4. USERELATIONSHIP
  5. Parent and Child Functions
    1. PATH
    2. PATHCONTAINS
    3. PATHITEM
    4. PATHITEMREVERSE

Part 8: Text functions

  1. FIND
  2. SEARCH
  3. REPLACE
  4. FORMAT
  5. LOWER
  6. UPPER
  7. RIGHT
  8. LEFT
  9. COMBINEVALUES
  10. CONCATENATE
  11. CONCATENATEX
  12. EXACT
  13. FIXED
  14. LEN
  15. MID
  16. SUBSTITUTE
  17. TRIM
  18. VALUE

Part 9: Statistical functions

  1. Central Tendencies
    1. Averages
    2. Means
    3. Geo means
  2. Counting
    1. Counting and COUNTX
    2. Blanks, and rows
    3. Distinct count: distinct, approximate, no blank
  3. Min/Max
    1. MAX, MAXA and MAXX
    2. MIN, MINA and MINX
  4. Sample
  5. Distributions
    1. Normal
    2. Exponential
    3. Beta
    4. Poisson
  6. Inverse
  7. Percentiles
  8. Ranking
  9. Standard Deviation
  10. Variance
    1. VAR.P and VARX.P
    2. VAR.S and VARS.P

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

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