Module 1. Introduction to Crisp DM (21 min)

• The Data Science Process
• Supervised Learning
• Unsupervised Learning
• Define the Problem or Opportunity
• Define the Data Sources

Module 2. Data Sources Identification (20 min)

• Data Understanding
• Data Sources and the Problem Statement
• Data Source Inventory
• Preparing for Exploratory Data Analysis
• Data Modeling and Data Science
• Data Pipelines and Data Stores
• Work with the End in Mind

Module 3. Exploratory Data Analysis (37 min)

• Data Understanding
• Exploratory Data Analysis
• Data Understanding – Data Profiling
• Sampling Size
• Data Profiling – EDA Methods
• Sample Quality
• Statistics Basics: Attributes
• Summary Statistics
• Distribution
• Data Relationships
• Data Relationships: Correlation Matrix
• Data Relationships: Outliers and Anomolies
• Results of Data Profiling
• Findings – Important Variables
• Outcomes and Interpretations
• EDA Checklist

Module 4. Data Preparation for Modeling (30 min)

• Data Preparation
• Feature Selection
• Data Quality Report
• Feature Scaling and Standardization
• Subset Selection
• Feature Selection Wrappers
• Feature Selection Filters
• Feature Selection Embedded Methods
• Transform for Data Modeling
• Data Ready State
• Modeling – Create and Train a Model
• Cross Validation

Module 5. Data Pipelines (10 min)

• What are Data Pipelines?
• Why are Data Pipelines Important?
• Data Pipelines and Data Science
• Data Pipelines – Repeatable Assets
• Data Pipelines – Existing

Module 6. Visualization Techniques (33 min)

• Data Description
• Data Description Report
• Data Evaluation
• Data Quality Report
• Data Cleansing
• Data Quality Scorecards and Dashboards
• Feature Ranking

Module 7. Data Quality and Integrity (36 min)

• Data Quality and Machine Learning
• Accurate Data
• Consistent and Complete Data
• Algorithms and Data Quality
• Algorithm Requirements
• Categorical Data and Algorithms
• Bins and Ranges
• High Cardinality
• Reduce Cardinality
• Dealing with Outliers
• Missing Data
• Time of Event

Click –here- to download a more detailed outline of this course.