Introduction to Data Preparation for Self-Service Analytics, Norway

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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

Introduction to Data Preparation for Self-Service Analytics Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.

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

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

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