Lokale, instruktorierte Live-Datenanalyse (Analyse von Daten oder Datenanalyse) Trainingskurse demonstrieren durch Diskussion und praktizieren die Programmiersprachen und Methoden, die zur Durchführung der Datenanalyse verwendet werden Data Analysis Training ist als "Live-Training vor Ort" oder "Remote-Live-Training" verfügbar Onsite Live Training kann vor Ort bei Kunden durchgeführt werden Deutschland oder in NobleProg Corporate Trainingszentren in Deutschland Remote-Live-Training wird über einen interaktiven Remote-Desktop durchgeführt NobleProg Ihr lokaler Trainingsanbieter.
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Erfahrungsberichte
Sehr guter Ansatz zum Einprägen / Wiederholen der wichtigsten Themen. Sehr schöne Aufwärmübungen.
Kurs: Python Programming
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Mochte die interaktive Art des Lernens sehr.
Luigi Loiacono
Kurs: Data Analysis with Hive/HiveQL
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Es war ein sehr praktisches Training, ich mochte die praktischen Übungen.
Proximus
Kurs: Data Analysis with Hive/HiveQL
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Ich profitierte von dem guten Überblick, einer guten Balance zwischen Theorie und Übungen.
Proximus
Kurs: Data Analysis with Hive/HiveQL
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Ich genoss die dynamische Interaktion und "hands-on" das Thema, dank der virtuellen Maschine, sehr stimulierend !.
Philippe Job
Kurs: Data Analysis with Hive/HiveQL
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Ich profitierte von der Kompetenz und dem Wissen des Trainers.
Jonathan Puvilland
Kurs: Data Analysis with Hive/HiveQL
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Übungen, zusätzliche Tipps vom Trainer, Flexibilität, um mehr Einblicke auf Anfrage zu geben.
Anna Alechno
Kurs: Power BI
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Ich habe die Flexibilität genossen, bestimmte Themen in den Kurs / Unterricht aufzunehmen.
Marc Ammann
Kurs: Python Programming
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Ich habe generell von der Präsentation von Technologien profitiert.
Continental AG / Abteilung: CF IT Finance
Kurs: A practical introduction to Data Analysis and Big Data
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Insgesamt war der Inhalt gut.
Sameer Rohadia
Kurs: A practical introduction to Data Analysis and Big Data
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Meistens hat mir die Power-BI-Möglichkeit gefallen.
British American Shared Services Europe
Kurs: Power BI
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Ich war im Allgemeinen von der Power Bi Web-Q profitieren.
Cristina Palii
Kurs: Power BI
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Die Kontaktmethode des Praktikanten. Der Ansatz ist gut für die Gruppe und das Individuum.
Marcin Szcześniak - Credit Agricole Bank P
Kurs: BPMN 2.0 dla Analityków Biznesowych
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Eine große Dosis Bewegung.
Piotr Kubiński
Kurs: BPMN 2.0 dla Analityków Biznesowych
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Ich bevorzuge die Übung und lerne etwas über die Ecken und Winkel von Python
Connor Brierley-Green
Kurs: Python Programming
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Joey hat eine ansteckende Begeisterung für das Programmieren. Und er war sehr gut darin, sich spontan auf unsere Bedürfnisse und Interessen einzustellen.
Randy Enkin
Kurs: Python Programming
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Viele Beispiele haben mich leicht verständlich gemacht.
Lingmin Cao
Kurs: Python Programming
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Final Test Simulation Prüfungsfragen, wo wir als Gruppe versucht, Aufgaben auf der Grundlage des Wissens während des Trainings zu lösen.
Dominik Łubiński
Kurs: Business Analysis, BABOK V3.0 and IIBA Certification Preparation
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Ich mochte vor allem das Wissen des Trainers.
Sripal S
Kurs: Understanding Business Process Modeling with BPMN 2.0
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Ich genoss allgemein die Art, Sprache zu sprechen.
Bhaskar naidu
Kurs: Understanding Business Process Modeling with BPMN 2.0
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Die umfassende Kenntnis des Leitfadens zu all unseren Fragen gab Antworten, die meine Erwartungen übertrafen ... Der Dozent führt großartige Diskussionen ... Ihm fehlt es nicht an Geduld ...
Łukasz Matulewicz
Kurs: Programowanie w języku Python
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Große Kenntnisse des Dozenten, Vielfalt der Werkzeuge und praktische Herangehensweise an das Thema
Magdalena Stupak
Kurs: Programowanie w języku Python
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großes Wissen des Trainers, wie man übersetzt
Renata Cylejowska
Kurs: Programowanie w języku Python
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Tatsache, dass die Anpassung ernst genommen wurde.
jurgen linsen
Kurs: Python Programming
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Ich genoss wirklich die Hilfe des Trainers.
Urszula Kuza
Kurs: Tableau Advanced
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alle
Grześ
Kurs: BPMN 2.0 dla Analityków Biznesowych
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Praktische Beispiele.
Przemysław Domański
Kurs: BPMN 2.0 dla Analityków Biznesowych
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Ich bekomme Antworten auf alle meine Fragen.
Natalia Gladii
Kurs: Data Analytics With R
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Ich habe mir einen guten Überblick über die Prozesseditoren, Geschäftsregeln und die BPM S-Toollandschaft verschafft. Ich kann den praktischen BPM N for Business Analysts-Kurs auch erfahrenen Analysten nur wärmstens empfehlen. Mit seinen hervorragenden Prozessszenarien und seiner Modellierungspraxis hebt er Sie von den anderen ab!
Anees Baig
Kurs: BPMN 2.0 for Business Analysts
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Sehr informativ und gab eine schöne Zusammenfassung des Kurses.
Matthew Steptoe
Kurs: Contemporary Development Principles and Practices
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Ich habe den ganzen Tag 1 genossen.
Peter Mahaffey
Kurs: Contemporary Development Principles and Practices
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Die Tatsache, dass wir Lösungen für gegebene Probleme + eine breite Palette von Anwendungen + erhalten haben, haben ein Kompendium des Wissens bestanden.
Wojciech Michalak
Kurs: Power BI
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Ich mochte die Übungen, weil es der einzige Weg ist zu lernen, durch Wiederholung.
David Rushe
Kurs: Tableau Advanced
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Der Trainer hat ein sehr breites Wissen, er kann es vermitteln. Es involviert die Teilnehmer zu interagieren und Probleme gemeinsam zu lösen.
Nina Rokita
Kurs: Power BI
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Der Trainer stand in ständigem Kontakt mit den Empfängern des Trainings, passte das Tempo an unsere Bedürfnisse an und löste unsere Probleme fortlaufend und beantwortete unsere Fragen
Dominika Jaworska
Kurs: Power BI
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Ich genoss die Übungen, den Trainingsraum, Teeküche.
Alisher Khaydarov
Kurs: BPMN 2 Fundamentals and Workshop
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Das Tempo und die Progression waren perfekt, um die Dinge schnell zu verstehen.
Stephen Brewell
Kurs: BPMN 2.0 for Business Analysts
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Die gegebenen Beispiele haben mir geholfen, einige Konzepte zu verstehen. Der Trainer sorgte dafür, dass wir das ganze Training verfolgten und zögerten nicht, zu einem früheren Punkt zurückzukehren, an dem Zweifel bestanden. Der Trainer passte das Training an unsere Bedürfnisse an und konzentrierte sich auf die Dinge, die wir gerne detaillierter hätten. Praktische Übungen waren auch gut.
Amanda Moscardini - Campinas Valley
Kurs: Understanding Business Process Modeling with BPMN 2.0
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Sehr guter Ansatz zum Einprägen / Wiederholen der wichtigsten Themen. Sehr schöne Aufwärmübungen.
Kurs: Python Programming
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Data Analysis Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
21 Stunden
Überblick
Dieser Kurs richtet sich an Business Analysten, welche BPMN 2.0 für Ihr Projekt nutzen möchten.
Praktische Aspekte of BPMN 2.0 sowie Implementation von üblichen Patterns wird fokussiert.
Der Kurs ist so aufgebaut, dass auf kurze Vorlesungen Übungen folgen. Die Teilnehmer werden mit verschiedenen Problemstellungen konfrontiert und müssen daraufhin ein Diagramm erstellen. Anschließend werden diese Diagramme in der Gruppe und mit dem Trainer diskutiert.
Dieser Kurs behandelt nicht die Ausführung von BPMN, sondern Aspekte der Analyse und des Prozessdesigns von BPMN 2.0.
Praktische Aspekte of BPMN 2.0 sowie Implementation von üblichen Patterns wird fokussiert.
Der Kurs ist so aufgebaut, dass auf kurze Vorlesungen Übungen folgen. Die Teilnehmer werden mit verschiedenen Problemstellungen konfrontiert und müssen daraufhin ein Diagramm erstellen. Anschließend werden diese Diagramme in der Gruppe und mit dem Trainer diskutiert.
Dieser Kurs behandelt nicht die Ausführung von BPMN, sondern Aspekte der Analyse und des Prozessdesigns von BPMN 2.0.
14 Stunden
Überblick
Publikum
Analysten, Forscher, Wissenschaftler, Absolventen und Studenten sowie alle, die lernen möchten, wie sich statistische Analysen in Microsoft Excel vereinfachen lassen.
Kursziele
Dieser Kurs wird dazu beitragen, Ihre Kenntnisse in Excel und Statistik zu verbessern und damit die Effektivität und Effizienz Ihrer Arbeit oder Forschung zu steigern.
In diesem Kurs wird beschrieben, wie Sie das Analysis ToolPack in Microsoft Excel , statistische Funktionen ausführen und grundlegende statistische Verfahren durchführen. Es wird erläutert, was Excel Einschränkungen sind und wie Sie diese überwinden können.
Analysten, Forscher, Wissenschaftler, Absolventen und Studenten sowie alle, die lernen möchten, wie sich statistische Analysen in Microsoft Excel vereinfachen lassen.
Kursziele
Dieser Kurs wird dazu beitragen, Ihre Kenntnisse in Excel und Statistik zu verbessern und damit die Effektivität und Effizienz Ihrer Arbeit oder Forschung zu steigern.
In diesem Kurs wird beschrieben, wie Sie das Analysis ToolPack in Microsoft Excel , statistische Funktionen ausführen und grundlegende statistische Verfahren durchführen. Es wird erläutert, was Excel Einschränkungen sind und wie Sie diese überwinden können.
28 Stunden
Überblick
In diesem Kurs können Sie die Programmiersprache Python erlernen. Der Schwerpunkt des Kurses liegt dabei auf den Grundlagen der Sprache und zentralen Programmbibliotheken. Der Kurs besteht zur Hälfte aus Theorie, zur Hälfte aus praktischen Übungen. Er ist sowohl für Programmierer als auch Nichtprogrammierer geeignet.
21 Stunden
Überblick
[R](http://www.r-project.org/) is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data.
Audience
Developers / data analytics
Duration
3 days
Format
Lectures and Hands-on
Audience
Developers / data analytics
Duration
3 days
Format
Lectures and Hands-on
7 Stunden
Überblick
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
14 Stunden
Überblick
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
21 Stunden
Überblick
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing.
In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
Audience
- Data analysts or anyone interested in learning how to interpret data to solve problems
Format of the Course
- After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
Audience
- Data analysts or anyone interested in learning how to interpret data to solve problems
Format of the Course
- After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
21 Stunden
Überblick
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
35 Stunden
Überblick
Participants who complete this instructor-led, live training in Deutschland will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
14 Stunden
Überblick
This instructor-led, live training in Deutschland is aimed at software developers who wish to build search and analytics solutions using Elasticsearch.
The training starts with a discussion of the Elasticsearch architecture, including its distributed model and search API. This is followed by an explanation of Elasticsearch's functionality and how to best integrate it into an existing application.
Hands-on exercises make up an important part of the training, and give participants a chance to put into practice their knowledge while receiving feedback on their implementation and progress.
The training starts with a discussion of the Elasticsearch architecture, including its distributed model and search API. This is followed by an explanation of Elasticsearch's functionality and how to best integrate it into an existing application.
Hands-on exercises make up an important part of the training, and give participants a chance to put into practice their knowledge while receiving feedback on their implementation and progress.
35 Stunden
Überblick
In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform. Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.
In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.
In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.
Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.
Assessments will be conducted throughout the course to gauge progress.
Format of the Course
- Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.
Note
- Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange.
In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.
In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.
Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.
Assessments will be conducted throughout the course to gauge progress.
Format of the Course
- Course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.
Note
- Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange.
28 Stunden
Überblick
In this instructor-led, live training in Deutschland, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, data analysis and visualization, UI programming and maintenance scripting.
42 Stunden
Überblick
Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions:
What has happened?
- processing and analyzing data
- producing informative data visualizations
What will happen?
- forecasting future performance
- evaluating forecasts
What should happen?
- turning data into evidence-based business decisions
- optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
What has happened?
- processing and analyzing data
- producing informative data visualizations
What will happen?
- forecasting future performance
- evaluating forecasts
What should happen?
- turning data into evidence-based business decisions
- optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
21 Stunden
Überblick
The aim of this course is to provide a clear understanding of the use of SQL for different
databases (Oracle, SQL Server, MS Access...). Understanding of analytic functions and the
way how to join different tables in a database will help delegates to move data analysis
operations to the database side, instead of doing this in MS Excel application. This can also
help in creating any IT system, which uses any relational database.
databases (Oracle, SQL Server, MS Access...). Understanding of analytic functions and the
way how to join different tables in a database will help delegates to move data analysis
operations to the database side, instead of doing this in MS Excel application. This can also
help in creating any IT system, which uses any relational database.
14 Stunden
Überblick
Apache Kylin is an extreme, distributed analytics engine for big data.
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 Stunden
Überblick
Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion.
In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
Audience
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
Audience
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 Stunden
Überblick
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 Stunden
Überblick
kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc.
In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.
By the end of this training, participants will be able to:
- Understand the difference between a row-oriented database and a column-oriented database
- Select data, write scripts and create functions to carry out advanced analytics
- Analyze time series data such as stock and commodity exchange data
- Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
- Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
- Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring
Audience
- Developers
- Database engineers
- Data scientists
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.
By the end of this training, participants will be able to:
- Understand the difference between a row-oriented database and a column-oriented database
- Select data, write scripts and create functions to carry out advanced analytics
- Analyze time series data such as stock and commodity exchange data
- Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
- Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
- Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring
Audience
- Developers
- Database engineers
- Data scientists
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 Stunden
Überblick
Data science is the application of statistical analysis, machine learning, data visualization and programming for the purpose of understanding and interpreting real-world data. F# is a well suited programming language for data science as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration.
In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems.
By the end of this training, participants will be able to:
- Use F#'s integrated data science packages
- Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python
- Use the Deedle package to solve time series problems
- Carry out advanced analysis with minimal lines of production-quality code
- Understand how functional programming is a natural fit for scientific and big data computations
- Access and visualize data with F#
- Apply F# for machine learning
Explore solutions for problems in domains such as business intelligence and social gaming
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems.
By the end of this training, participants will be able to:
- Use F#'s integrated data science packages
- Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python
- Use the Deedle package to solve time series problems
- Carry out advanced analysis with minimal lines of production-quality code
- Understand how functional programming is a natural fit for scientific and big data computations
- Access and visualize data with F#
- Apply F# for machine learning
Explore solutions for problems in domains such as business intelligence and social gaming
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 Stunden
Überblick
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
21 Stunden
Überblick
Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery.
In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.
By the end of this training, participants will be able to:
- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel
Audience
- Data analysts
- Data scientists
- SQL programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.
By the end of this training, participants will be able to:
- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel
Audience
- Data analysts
- Data scientists
- SQL programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 Stunden
Überblick
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
- Network engineers
- Network operations personnel
- Telecom technical managers
Format of the course
- Part lecture, part discussion, hands-on exercises
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
- Network engineers
- Network operations personnel
- Telecom technical managers
Format of the course
- Part lecture, part discussion, hands-on exercises
21 Stunden
Überblick
Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries.
In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories.
By the end of this training, participants will be able to:
- Install and configure Dremio
- Execute queries against multiple data sources, regardless of location, size, or structure
- Integrate Dremio with BI and data sources such as Tableau and Elasticsearch
Audience
- Data scientists
- Business analysts
- Data engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories.
By the end of this training, participants will be able to:
- Install and configure Dremio
- Execute queries against multiple data sources, regardless of location, size, or structure
- Integrate Dremio with BI and data sources such as Tableau and Elasticsearch
Audience
- Data scientists
- Business analysts
- Data engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
14 Stunden
Überblick
In this instructor-led, live training in Deutschland (onsite or remote), participants will learn how to combine the capabilities of Python and Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
14 Stunden
Überblick
A business' marketing strategy is one of its most essential tools to ensure success. The rise in technology and advances in data gathering and analysis has led to a continuous transformation of marketing. Data analysis is now considered to be a crucial skill for marketers to learn. Likewise, applying data science to marketing principles is also important in helping data science professionals maximize their contributions.
In this instructor-led, live training, participants will learn how to gain valuable insights from large data sets and how to use these for marketing strategies.
By the end of this training, participants will be able to:
- Install and configure tools for data-driven marketing tools (Tableau, Google Analytics, Zarget, etc)
- Measure, test, and optimize the performance of their marketing strategies using data
- Use data to segment their customers and prospects
- Use predictive modeling to improve response rates
- Implement step-by-step data analytics to drive better marketing decisions
Audience
- Marketers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training, participants will learn how to gain valuable insights from large data sets and how to use these for marketing strategies.
By the end of this training, participants will be able to:
- Install and configure tools for data-driven marketing tools (Tableau, Google Analytics, Zarget, etc)
- Measure, test, and optimize the performance of their marketing strategies using data
- Use data to segment their customers and prospects
- Use predictive modeling to improve response rates
- Implement step-by-step data analytics to drive better marketing decisions
Audience
- Marketers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
35 Stunden
Überblick
Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.
Audience
- Developers
- Technical analysts
- IT consultants
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.
Audience
- Developers
- Technical analysts
- IT consultants
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
14 Stunden
Überblick
Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks.
In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.
By the end of this training, participants will be able to:
- Install and configure Apache Arrow in a distributed clustered environment
- Use Apache Arrow to access data from disparate data sources
- Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
- Analyze data across disparate data sources without having to consolidate it into a centralized repository
Audience
- Data scientists
- Data engineers
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.
By the end of this training, participants will be able to:
- Install and configure Apache Arrow in a distributed clustered environment
- Use Apache Arrow to access data from disparate data sources
- Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
- Analyze data across disparate data sources without having to consolidate it into a centralized repository
Audience
- Data scientists
- Data engineers
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
14 Stunden
Überblick
About the Course:
Microsoft Power BI transforms your company data into rich visuals that facilitate new ways of thinking about and organizing your data, so that you can focus on what is important to achieving your goals. This course covers both Power BI on Line and Power BI desktop.
Audience:
This course is intended for Business Managers, Report Developers, Analysts, Project Managers and Team Leads.
At Course Completion
Upon completing this course, students will have a basic understanding of the topics below, as well as an ability to utilize and implement the concepts learned.
- Power BI
- Power BI Desktop
- Working with CSV, TXT, and Excel Worksheets
- Connecting to Databases
- Merging, Grouping, Summarizing, and Calculating Data
- Reporting
- Power BI Online
Microsoft Power BI transforms your company data into rich visuals that facilitate new ways of thinking about and organizing your data, so that you can focus on what is important to achieving your goals. This course covers both Power BI on Line and Power BI desktop.
Audience:
This course is intended for Business Managers, Report Developers, Analysts, Project Managers and Team Leads.
At Course Completion
Upon completing this course, students will have a basic understanding of the topics below, as well as an ability to utilize and implement the concepts learned.
- Power BI
- Power BI Desktop
- Working with CSV, TXT, and Excel Worksheets
- Connecting to Databases
- Merging, Grouping, Summarizing, and Calculating Data
- Reporting
- Power BI Online
7 Stunden
Überblick
This instructor-led, live training in Deutschland (online or onsite) is aimed at persons who wish to use graphical and numerical techniques to better understand the structure of a dataset before commencing more formal analysis.
By the end of this training, participants will be able to:
- Identify variables that that indicate connections within the data.
- Identify unusual patterns in data.
- Generate visually appealing and easy to read graphics that provide insight into the data.
- Use exploratory analysis to pave the way for more complex statistical modeling.
By the end of this training, participants will be able to:
- Identify variables that that indicate connections within the data.
- Identify unusual patterns in data.
- Generate visually appealing and easy to read graphics that provide insight into the data.
- Use exploratory analysis to pave the way for more complex statistical modeling.
14 Stunden
Überblick
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.
Zukünftige Data Analysis Kurse
08-11-2021 09:30
14 hours
11-11-2021 09:30
14 hours
12-11-2021 09:30
7 hours
17-11-2021 09:30
14 hours
01-12-2021 09:30
14 hours
Data Analysis Schulung, Data Analysis boot camp, Data Analysis Abendkurse, Data Analysis Wochenendkurse, Data Analysis Kurs, Data Analysis Training, Data Analysis Seminar, Data Analysis Seminare, Data Analysis Privatkurs, Data Analysis Coaching, Data Analysis Lehrer
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