Course Taxonomy: Data & AI

Gen-AI Overview

Posted on March 4, 2024 by -

  • The AI Revolution: 1950’s to now
  • Generative AI: Real-World Applications
    • Content generation
    • Business development
    • Client delivery
    • Training
  • Try Cprime’s AI Chat Bot: On your laptop, tablet, or phone
    • Chained prompts
    • Strong verbs
    • Summarize or expand responses
    • Focus for an audience or channel
    • … and much more
  • What more you can do: APIs, Plugins, and Vectors – Oh my!
    • A secure AI
    • Leverage your own data
    • Import and data or website you need

GenAI Discovery Workshop

Posted on September 28, 2023 by -

Part 1: Introduction

  • Workshop Objectives & Attendee Pain Points
  • Demystifying AI: From Myth to Reality

Part 2: Technology & Terminology Primer

  • Breaking Down AI: Essential Terms and Concepts
  • Linking AI to Business Operations

Part 3: Pitfalls of AI Adoption

  • Common Challenges: Data Quality, Scalability, and Integration
  • Addressing Ethical and Bias Concerns

Part 4: ROI Analysis for AI Initiatives

  • Quantifying AI's Real-World Business Value
  • Decoding the Real-World Value of AI for Your Business
  • Practical Steps for Measuring AI Impact

Part 5: Map AI Solutions to Customer’s Business

  • From Current Pain Points to Potential AI Solutions
  • Assessing existing systems, databases, and software
  • Identifying communication and integration gaps
  • AI as a solution to streamline and optimize
  • Strategies for Effective Data Management with AI Integration

Part 6: Conclusion

  • Aligning AI with Long-Term Goals & Future Steps

Appendix: Selected AI Solutions Deep Dive

  • Resources and Best Practices for Implementation

ChatGPT Prompt Engineering Primer

Posted on May 30, 2023 by -

Part 1: ChatGPT Basics & Underlying Concepts

We begin by spending just a little time describing ChatGPT and defining relevant terminology to set the stage for learning. The Q&A allows learners to quickly confirm their understandings so we can get to the fun and interesting part, using this tool.

  1. Definitions
  2. Types of GPTs (Chat & others)
  3. The role of data (and your company’s data)
  4. What ChatGPT is good at (and not good at)
  5. Who can use ChatGPT (and for what)?
  6. Q&A

Part 2: Interacting with ChatGPT

Most of our time in this short course is spent interacting with ChatGPT. Each of the sub-sections below introduces a specific usage mode, and includes:

  • Examples – The instructor shows multiple examples of that usage mode.
  • Play-time – Learners are allowed time to try their hand with that usage mode on their own.
  • Show and Tell – Learners share and discuss their experiences with that usage mode with each other and get feedback from the instructor.

Converse with ChatGPT

We start with the most basic interactions with ChatGPT:

  • Ask questions
  • Use chained prompts (and break chains when needed)
  • Use strong and weak verbs in prompts.
  • With Examples, Play-time, Show and Tell

Prompt ChatGPT to tailor its response

ChatGPT’s responses will sometimes not meet your needs. They may be too long and verbose, too short and cryptic, or not laid out in a useful way. So, we will look at various ways to ask it to provide or restate its response in more appropriate ways.

  • Summarize long responses
  • Expand on short responses
  • Format responses (e.g., bulleted or numbered lists, headings, etc.)
  • Provide an example of content & format you want it to respond with

Prompt ChatGPT to use appropriate roles

We will look at how to focus ChatGPT’s interactions with you based on role definitions.

  • Provide responses appropriate for a particular role or audience
  • Provide responses appropriate to a channel (e.g. social media)
  • Play a role in how it responds
  • Role-play with you

Prompting ChatGPT to respond with questions

ChatGPT can do more than just answer questions. We will look at how to prompt it to ask questions as well.

  • Prompt it to provide questions (e.g. for an interview)
  • Prompt it to ask clarifying questions before responding

Providing data to ChatGPT

The free public version of ChatGPT cannot access data. So, we will look at ways you can provide it with the data you want it to use in its responses.

  • Loading the prompt with data
  • Providing data in multiple prompts for a single response
  • With Examples, Play-time, Show and Tell

Part 3: Advanced Use Cases & Interactions

Connecting ChatGPT to other applications

We will explore a variety of tools that can be used to connect applications to the paid version of ChatGPT.

  • Plug-ins for commercial applications (e.g. Excel) to use ChatGPT
  • ChatGPT’s API for writing your own plug-ins or adding ChatGPT capabilities to your company’s applications

Making data available to ChatGPT

We will explore a variety of ways to enable ChatGPT to use other data.

  • Plug-ins for ChatGPT to access external data sources (e.g. Wikipedia)
  • Tools to feed large data sets into ChatGPT’s prompts
  • Using a private LLM (Large Language Model) to enable ChatGPT to use specialized language and terminology
  • Grounding ChatGPT on your company’s data

Part 4: Class wrap-up and Q&A

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

AI Implementation Boot Camp – From Opportunity to Architecture

Posted on October 17, 2020 by -

Module 1: AI Has Changed Engineering Decisions

Start Understand what changed, what did not, and why AI systems require a different engineering mindset.

  • What modern generative AI systems actually do
  • Why AI output is probabilistic rather than deterministic
  • Models, inference, tokens, context, and hallucination
  • Why an impressive prototype can still be a poor production system
  • The role of conventional software around an AI model
  • Recognizing where ambiguity, inconsistency, and hidden assumptions can appear
  • When AI should not be used

Module 2: Choosing the Right AI Opportunity and Architecture

Learn how to decide what belongs in AI and what architecture best fits the task

  • Automation vs augmentation
  • Conventional software vs AI-assisted workflows
  • Frontier vs smaller/local models
  • Hosted vs private deployment
  • Capability, quality, cost, latency, privacy, and data-handling trade-offs
  • Using different models for different tasks
  • Comparing model quality, latency, consistency, and cost
  • Model routing and escalation
  • Using the least expensive execution path that still meets the quality requirement
  • Recognizing when conventional code is cheaper and more reliable than an LLM
  • Designing a practical first pilot

Module 3: Giving AI Enterprise Knowledge

Give AI access to current, private, and authoritative information without expecting the base model to know everything.

  • Embeddings and semantic search
  • Retrieval-Augmented Generation (RAG)
  • Context-window trade-offs
  • Chunking and retrieval quality
  • RAG vs fine-tuning
  • When authoritative data should come from an API instead of a document
  • Grounding responses in enterprise information
  • Evaluating whether retrieval actually improved the result

Module 4: From Answers to Actions

Move from an AI system that only talks to one that can safely interact with real systems.

  • Structured output
  • Function and tool calling
  • Connecting models to APIs
  • Tool schemas and validation
  • Errors, retries, and partial failure
  • Idempotency and duplicate actions
  • Human approval for consequential actions
  • Agent loops and when they are appropriate
  • When an agent is unnecessary
  • Model Context Protocol (MCP)
  • MCP clients, servers, tools, and resources
  • Direct API integration vs MCP
  • Designing boundaries around what an AI system is allowed to do

Module 5: Can You Trust It

Design a quality control layer around a probabilistic component

  • Deterministic validation
  • Evaluation datasets
  • Expected vs acceptable outcomes
  • LLM-as-a-judge
  • Designing evaluation rubrics
  • Why the evaluator can also be wrong
  • Retry vs escalation
  • Human-in-the-loop review
  • Using stronger models selectively when lower-cost models are not good enough
  • Measuring the quality/cost trade-off of different routing strategies
  • Measuring quality, latency, and cost over time

Module 6: Security and Production Reality

Understand what must change before an AI prototype can be trusted in front of real users

  • Prompt injection and malicious retrieved content
  • Data leakage and excessive permissions
  • Tool abuse and uncontrolled actions
  • Model and service failures
  • Runaway automation and runaway cost
  • Observability and logging
  • Prompt and model versioning
  • Latency and cost monitoring

●       Graceful degradation

●       Human review and governance boundaries

●       Applying these controls to a realistic enterprise AI implementation

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