Introduction to Data Preparation for Self-Service Analytics, Germany

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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 Germany

The Federal Republic of Germany is the second most populous country in Europe and is located in Central Europe. The official language of the country is German. Germany is one of the richest countries in the world. The main exports of the country include motor vehicles and iron and steel products.

Here are some fun facts about Germany:
The fairy tale writer, the Brothers Grimm, came from Germany and wrote many famous stories such as Cinderella, Snow White, and Sleeping Beauty.
Germany is home to the largest theme park in Europe, the Europa-Park.
The famous composer Ludwig van Beethoven was born in Germany.
The Autobahn, the German highway system, is known for having no general speed limit.

Berlin was divided by the Berlin Wall from 1961 to 1989. Known for its street art, Berlin has many colorful murals and graffiti throughout the city. Also, Berlin is home to many famous museums, such as the Pergamon Museum and the Museum Island. Many clubs and bars stay open until the early hours of the morning in this big city.

Another popular city is Munich, which is famous for its Oktoberfest beer festival that attracts millions of visitors every year. Munich is also home to many historic buildings, including Nymphenburg Palace and the Marienplatz town square.

The country's capital and largest city is Berlin, however Frankfurt is considered to be the business and financial center of Germany. It is home to the Frankfurt Stock Exchange, the European Central Bank, and many other financial institutions. Because of its central location within Europe and its status as a major financial hub, Frankfurt is often referred to as the "Mainhattan," a play on the city's name and its association with the Manhattan financial district in New York City.

Frankfurt is also a major transportation hub, with the largest airport in Germany and one of the largest in Europe, Frankfurt Airport. Additionally, it is a popular destination for tourists, with its historic city center, beautiful parks, and vibrant cultural scene.

Some of the top German technology companies like Siemens AG, Bosch, SAP SE, Deutsche Telekom, Daimler AG and Volkswagen has business centers in Frankfurt. The country has a strong tradition of engineering and innovation, and is home to many other world-class technology companies and research institutions.

Tailored to meet the specific needs of Germany, Bilginç IT Academy combines cutting-edge training methodologies with our comprehensive range of Certification Exam preparation courses and accredited corporate training programs. Experience a transformative approach to IT training that will redefine your expectations.

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

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

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