From Raw Data To Machine Learning Training For Executives And Managers

Data.

It’s always been the key to making good decisions. Whether you are a commander on a battlefield in 1000 BC or a CEO in a boardroom in 2000.

Having critical information at the right time can help make million and billion dollar decisions.

But, just having the data is not enough.

I could give you random stock prices, the temperature it will be for the next 3 weeks in Vegas and the number of kids being born tomorrow. This will be useless to most people. But in the current world we live today this is what it can feel like being a business executive.

There are hundreds of metrics, reports, dashboard and models being thrown around every day in a single company.

Some provide a lot of value to companies by having clear stories and goals. While others are as useless as the random facts stated above.

So how do you take data from just random numbers on a screen to actually valuable?

That is what I am going to discuss in our next series of articles and videos .

How you can take data from raw, useless information and turn it into valuable insights.

We will be walking through the whole process. That means talking about concepts like ETLs, data warehouses and automation to analyzing data and deploying machine learning models.

Our goal will be to provide the reader with the skills and understanding to master the flows of data that go through both million and billion dollar companies.

We will be using a combination of technical diagrams, anecdotes and real life examples to help the reader understand and grasp every step required to make a successful data product.

This set of articles and videos will be for people who are looking to gain an understanding of the data life-cycle.

Whether you are looking to manage a team of data engineers and data scientists, or want to learn how to deploy machine learning models, data warehouses or ETLs, this will be a series for you.

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Course Outline

  • Data Sources And Raw Data
    • Where Can You Get Data
      • Examples Of Internal Data Sources
      • Examples Of Public Data Sources
  • Data Warehouses
    • What Is A Data Warehouse
    • Why Do We Build Data Warehouses
    • Data Warehouse High Level Design Concepts 
    • Designing Data Warehouses Walk Through
      • Define Your Workflows
      • Define Your Entities And Questions
      • Design Your Data Warehouse
  • Cloud Data Warehouses
    • What Makes Cloud Data Warehouses Different
      • What Are The Benefits Of Cloud Data Warehouses
    • Cloud Data Warehouse Examples
      • Redshift, Snowflake, BigQuery, Oracle Autonomous Data Warehouse, SAP Data Warehouse
  • ETLS And ELTs
    • What Is An ETL and ELT
    • Why Do We Use ETLs and ELTs
    • ETL Design
    • ETL Tools
      • Airflow, Informatica, Fivetran, Talend, SSIS, Stich, AWS Glue, CloudSpanner
  • Now You Have Your Data, Now What?
  • Data Analytics
    • Designing Metrics
      • What Are Metrics?
      • How Do You Decide What To Measure?
      • Creating A Metric An Example
        • Defining Your Metrics Purpose
        • Finding A Trustworthy Data Set
        • Picking A Population
        • Picking What You Want To Measure
        • Writing Out Your Metric
        • Creating Your Metric
        • Now What?
    • From Analysis To Metrics From Metrics To Dashboards
      • What Do We Do With Metrics?
      • Different Dashboarding Tools
        • Tableau, Power BI, Looker
      • Dashboard Design Introduction
        • Start With A Purpose
        • Design Principles
        • Dashboard Design Best Practices
      • Walking Through Designing A Dashboard
  • Data Science
    • What Is Data Science Anyways?
    • Data Science Models
      • Clustering
      • Classifications
    • Failures and Gotchas Of Data Science 
    • Important Data Science Concepts
      • Sampling Theory
      • Significance Test
      • Dimensional Reduction
      • Confidence Intervals
      • Probability Distributions
      • Unbalanced Data
  • Machine Learning
    • Machine Learning Types
      • Supervised
      • Unsupervised
      • Feature Engineering
      • Machine Learning Models
    • Deploying Machine Learning Models
      • Batch Model Deployment
      • Serverless Deployment
      • Streaming Deployment
    • MLOps
      • What Is MLOps
      • Why Use MLOps
      • MLOps Tools
        • Algorithmia, MLFlow, Pachyderm, Kubeflow
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