Introduction to Data Preparation for Self-Service Analytics Training in Singapore

The once simple world of data preparation—ETL for operational data integration—has become increasingly complex. Terms such as data wrangling and data blending indicate some of the challenges. The exciting work of analytics doesn’t work well until the data is ready for meaningful analysis. The scope of big data, the variety of data uses, and the emergence of business-friendly data visualization and analysis tools all contribute to the complexity.

A recently emerged category of technologies helps to meet the challenges with business-friendly tools for data integration and preparation. When your analytics projects spend more time finding and fixing data than analyzing the data, you really need to make a change. Learn about the tools and techniques that can help individuals and teams—both business and technical—to cleanse, combine, format, and sample data for analytics.

There are no prerequisites for this course.

Business managers, business analysts, data analysts, and data scientists who need to accelerate and simplify data preparation activities; BI and analytics developers who face the daily challenges of complex data preparation; technical managers and architects who need to integrate data preparation technologies into the BI and analytics toolkit; everyone who struggles with getting the right data in the right forms for effective analytics.

• The common challenges of data preparation in the age of big data
• Techniques for data preparation that improve both speed and quality of analytics
• The data management and governance benefits of data preparation technologies
• The landscape of tools and technologies for modern data preparation
• To identify the data that is best suited to your analytic needs
• To understand the data and the challenges inherent in that data
• To define and specify data cleansing, formatting, and blending needs
• To understand and apply principles of iterative and adaptive data preparation

Module 1 – Data Preparation Basics

• Data Preparation Defined
• Historical Perspective – How Did We Get Here?
• The Need for Self-Service Data Preparation
• Historical Perspective
• Introduction to Data Preparation Tools
  • Types of tools
  • Programming/scripting vs. visual interface
  • Standalone vs. integrated into analytics platforms
  • Cloud, on-premises, and hybrid deployment
  • Machine learning and data preparation
• Users of Data Preparation Tools
  • Data scientists
  • Data engineers
  • Business analysts
  • Data analysts
  • oInformation workers
• Data Preparation in Analytics Architecture
• Data Preparation and Analytics Life Cycle
  • Continuous exploration and discovery
  • Iterative and adaptive

Module 2 – Data Discovery

• Data Sources
  • Enterprise databases
  • Local data
  • Desktop data
  • Cloud data
  • Web data
  • Files
  • NoSQL
  • Geospatial
  • Media
• Data Sourcing
  • Choosing data sources
  • Physical data source connections
  • Virtual data source connections
• Data Exploration
  • Understanding content
  • Estimating quality
  • Discovering patterns
  • Discovering data types
  • Discovering data structure
  • Discovering data relationships
  • Data enrichment opportunities
  • Developing data profiles
  • Capturing metadata

Exercise 1 – Data Exploration and Data Sourcing

Module 3 – Data Transformation

• The Scope of Data Preparation
• Improving Data
  • Standardization and conforming
  • Cleansing and quality
  • De-duplication
• Enriching Data
  • Derivation
  • Appending
  • Aggregation
• Formatting Data
  • Aggregation
  • Sorting and sequencing
  • Pivoting / de-pivoting
  • Sampling and filtering
  • Masking sensitive data
  • Constructing records
• Data Blending
  • Blending defined
  • Blend vs. join
  • Blending vs. Warehousing

Exercise 2 – Data Transformation and Data Blending

Module 4 – Data Governance

• Data Validation
  • Visual validation
  • Rules-based validation
  • Data auditing
• Data Protection
  • Activity logging
  • Activity audits
• Data Management
  • Metadata management
  • Data lineage
  • Model on use – “just in time” data models
  • Data curation – what and why

Module 5 – The Technology Landscape

• Data Preparation Platforms
  • Core functions and features
• Product Overviews and Selected Demonstrations