Erstellung von transformativen und konditionalen Metriken

• Define the goal: Clearly define the goals of the metrics. Understand the specific transformations or conditions that need to be captured.
• Data collection: Collect relevant data to be used for training and validation of the metrics. This data should be representative of the scenarios in which the metrics will be applied.
• Feature engineering: Identifying and creating relevant features from the data that can be used to derive the desired metrics. This may involve pre-processing, normalizing or transforming the data.
• Model selection: Selecting appropriate models or algorithms that can capture the desired transformations or conditions. These may be statistical models, machine learning algorithms or other computational techniques.
• Training: Use the collected data to train the selected models. This includes optimizing the model parameters to minimize the error or loss function.
• Validation: Evaluate the performance of the trained models using a separate data set or cross-validation techniques. This ensures that the models can be generalized to unknown data.
• Fine-tuning: Iterate the model training and validation process to refine the metrics and improve their accuracy and reliability.
• Evaluation: Evaluate the effectiveness of the metrics created in capturing the desired transformations or conditions. This may include comparing the metrics to existing benchmarks or conducting empirical studies.
• Deployment: After validation and evaluation, deploy the metrics in the desired applications or systems, monitor their performance and make adjustments as needed.
• Maintenance and updating: Monitor and update the metrics continuously as new data becomes available or the underlying conditions change. Ensure that the metrics remain relevant and effective over time.

This list is not exhaustive and there are many more topics that can be covered in a seminar on creating transformative and conditional metrics, depending on the specific needs and requirements of the participants.

• Transformation derived metrics: Creating time series analysis
• Transformation derived metrics: Create time series analysis
• Conditional metrics: Filtered metrics
• Conditional derived metrics: Create filtered metrics in dossiers

As a rule, the latest version released by the manufacturer is always used for training.

https://www.microstrategy.com/de

Transformative and conditional metrics training is useful for a variety of professionals and stakeholders involved in data analysis, decision making and performance measurement. In particular, the following individuals or groups may benefit from this training:

Data scientists: professionals who specialize in the analysis and interpretation of complex data sets using statistical, machine learning and computational techniques.

Data Analysts: Individuals responsible for collecting, processing and analyzing data to gain insights and make decisions.

Business Analysts: Professionals who focus on understanding business requirements, processes and performance metrics to drive strategic initiatives and improvements.

Performance Analysts: Individuals who evaluate and measure the performance of systems, processes or organizations using quantitative and qualitative metrics.

Decision makers: Executives, managers and other decision makers who rely on data-driven insights and metrics to make informed decisions and strategic plans.

Research professionals: Researchers and academics involved in the study and development of new methods, techniques or models for measuring and analyzing various phenomena.

Quality assurance professionals: individuals responsible for ensuring the quality and reliability of products, services or processes through effective metrics and performance indicators.

IT professionals: Technologists and IT specialists who design, implement and maintain systems or platforms that rely on metrics and analytics for monitoring and optimization.

Consultants: Professionals who advise and guide organizations in improving performance, efficiency and decision-making through effective metrics and analytics.

Project managers: Individuals who oversee and manage projects, programs or initiatives and need metrics and performance indicators to track progress, identify risks and ensure successful outcomes.

General prerequisites that participants should fulfill in order to get the most out of such a seminar:

Fundamentals of data analysis: An understanding of the basics of data analysis, statistics and data processing is necessary to work effectively with data.

Programming and data processing skills: Basic knowledge of programming languages such as Python, R or SQL and database management systems is helpful for data processing and analysis.

Basic statistical knowledge: A solid understanding of statistical methods, models and procedures is necessary to correctly develop and validate metrics.

Application domain expertise: A deep understanding of the specific application domain in which the metrics will be used (e.g. business analysis, performance monitoring, quality management) is beneficial.

Machine learning and data modeling skills: Knowledge and experience in machine learning, artificial intelligence and data modeling can be useful in developing advanced metrics.

Analytical thinking: The ability to analyze complex problems, recognize patterns and make informed decisions based on data is critical to creating effective metrics.

Communication skills: The ability to clearly communicate the results and insights from metrics, both written and verbal, is important to promote understanding and acceptance.

Project management skills: Basic project management skills can be helpful to efficiently manage and monitor the metrics creation process.

Willingness to learn: With the ever-evolving technologies and methodologies in data analysis and modeling, a willingness to continually learn and develop is important.

There are several certifications in the field of transformative and conditional metrics creation that you can obtain by attending a relevant seminar. The most well-known certifications are

Certified Metrics Analyst (CMA)
Advanced Metrics Designer (AMD)
Conditional Metrics Specialist (CMS)
Transformative Metrics Expert (TME)

The certifications cover various aspects of metrics design, data analysis techniques, statistical modeling and application-specific implementations.

To obtain a certification, you must pass one or more exams that assess your skills and knowledge in the field. The exact requirements and exams depend on the certification in question. It is therefore important to find out in advance what the requirements and exams are so that you can work towards certification.

Please note that our seminars are designed to prepare you for your daily work. Certification is not our goal; the seminars are too rigidly structured for that.

If you as an organization invest in training your employees to create transformative and conditional metrics, there are several ways to ensure that this investment pays off in the long run:

Set clear goals: Together with your employee, set clear goals that you want to achieve by attending the seminar. Make sure that these goals are in line with the company's goals and needs.

Choose the right course: Ensure that the course you choose for your employee provides the skills and knowledge needed to achieve the defined goals.

Provide support and resources: Make sure your employee has all the resources and support they need to successfully complete the course. This could include time for self-study, training materials or technical support.

Plan for implementation: Make sure your employee can put the knowledge and skills they have acquired into practice. For example, plan training courses or projects where they can use and develop their new skills.

Track progress: Make sure you keep track of your employee's progress and provide regular feedback. This will ensure that the time and money invested in quality training pays off in the long run.

We can help you retain your employees after the seminar by giving them the best possible conditions to put what they have learned into practice and stay connected to your company. Please contact us: investitionensichern@scngmbh.de

This seminar has the level "Administration / Basic and Advanced".

If you are not sure whether this course is the right level for you, you can discuss this with the trainer in advance.

• MicroStrategy Analytics Basics - MicroStrategy Analytics Reports
• Basic Data Science - Improving Business Analysis through Data Science
• Advanced MicroStrategy in the Cloud - Managing MicroStrategy in cloud environments
• Basic MicroStrategy Administration - Managing Big Data through MicroStrategy Administration
• Advanced knowledge of IBM Cloud and Data Platform - Getting started with MicroStrategy SDKs

Apache

Apache Administrator: Apache Cassandra Cluster Administrator
Apache CouchDB: Apache CouchDB for developers

Cloudera

Cloudera: Cloudera CDH Setup
Cloudera: Cloudera Search for developers

Mongo DB

MongoDB: MongoDB for Administrators
MongoDB BI: MongoDB BI Connector BI

Rapid Miner

RapidMiner Radoop: RapidMiner Radoop Data Analysis
RapidMiner Data Mining: RapidMiner Data Mining and Predictive Analysis

Big Data

Big Data (AWS): Big Data in Amazon Web Services (AWS)
MS Azure: MS Azure - Big Data and Visualization

Data Integration: Advanced Data Integration
Data Catalog: Data Catalog Advanced

Hybrid utilization: Hybrid use of Hadoop with MongoDB
NoSQL Basics: NoSQL Basics

We offer you seminars with a high degree of practical relevance. The content and exercises are geared to your daily tasks in the company and are completely free of advertising references to other software manufacturer's products.

All of our training courses are vendor-independent. This allows us to offer critical reviews of the products themselves and comparisons with the manufacturer's competitors in the seminar. The course content is our own and is derived from our trainers' practical experience in projects.

Of course, the content of company seminars can be tailored to your individual needs. Please do not hesitate to contact us.

• Guaranteed completion - guaranteed completion with two or more participants
• Mobile classrooms - the ideal complement to corporate seminars
• Free support - for questions after the seminar
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• Clarification of seminar requirements - discuss your knowledge with the trainer
• Assistance with travel costs - we cover part of the cost of hotel accommodation
• Catering - all-day cold and hot drinks and a full lunch in the restaurant
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• Remotelabs - rent our remotelabs for your own seminars or as a supplement to your company seminars