Predictive Analytics Schulungen
Predictive Analytics ist der Prozess der Verwendung von Datenanalysen, um Vorhersagen über die Zukunft zu treffen. Dieser Prozess verwendet Daten zusammen mit Data-Mining-, Statistik- und maschinellen Lerntechniken, um ein Vorhersagemodell für die Vorhersage zukünftiger Ereignisse zu erstellen. NobleProg Onsite Live Predictive Analytics-Trainingskurse demonstrieren anhand praktischer Übungen, wie verschiedene Tools verwendet werden können, um Vorhersagemodelle zu erstellen und sie auf große Beispieldatensätze anzuwenden, um zukünftige Ereignisse basierend auf den Daten vorherzusagen. Predictive Analytics Training ist in verschiedenen Formaten verfügbar, einschließlich Live-Training vor Ort und Live-Training in einer interaktiven Remote-Desktop-Konfiguration. Lokale Predictive Analytics-Schulungen können live in den Räumlichkeiten des Kunden oder in lokalen Schulungszentren von NobleProg durchgeführt werden.
Erfahrungsberichte
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
The tutor, Mr. Michael Yan, interacted with the audience very well, the instruction was clear. The tutor also go extent to add more information based on the requests from the students during the training.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
the introduction of new packages
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training to meet clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course Name: Programming with Big Data in R
ref material to use later was very good
PAUL BEALES - Seagate Technology
Course Name: Applied Machine Learning
He was very informative and helpful.
Pratheep Ravy - UPC Schweiz GmbH
Course Name: Predictive Modelling with R
ref material to use later was very good.
PAUL BEALES- Seagate Technology.
Course Name: Applied Machine Learning
Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away.
This was an excellent course. One of the best I have had.
Matthew Thomas - British Telecom
Course Name: Applied Machine Learning
Systematisierung von Wissen im Bereich ML
Orange Polska
.Course Name: From Data to Decision with Big Data and Predictive Analytics
Translated by
usystematyzowanie wiedzy z dziedziny ML
- Orange Polska
Bereich des Materials
Maciej Jonczyk Orange Polska
.Course Name: From Data to Decision with Big Data and Predictive Analytics
Translated by
zakres materialu
Maciej Jonczyk - Orange Polska
Predictive Analytics Schulungsübersicht
| Code | Name | Dauer | Übersicht |
|---|---|---|---|
| appliedml | Angewandtes Maschinelles Lernen | 14 hours | Der Übungskurs ist für alle diejenigen gedacht, die "Machine Learning" in praktischen Applikationen anwenden möchten Teilnehmer Dieser Kurs ist für Data Scientists und Statistiker, die Grundkenntnisse in Statistik haben und wissen, wie man R programmiert. Der Schwerpunkt des Kurses liegt auf dem praktischen Aspekt von Daten/Modell-Vorbereitung, Execution, post hoc Analyse und Visualisierung. Das Ziel ist es, den Teilnehmern praktische Kenntnisse im Maschinellen Lernen zu vermitteln. Bereichsspezifische Beispiele erhöhen die Relevanz der Schulung für die Teilnehmer. |
| apachemdev | Apache Mahout für Entwickler | 14 hours | Teilnehmer Entwickler, die in ihren Projekten Apache Mahout für maschinelles Lernen nutzen möchten. Inhalt Praktische EInführung in maschinelles Lernen. Der Kurs wird in Form eines Workshops durchgeführt und beinhaltet Anwendungsfälle zu realen Problemen. |
| d2dbdpa | From Data to Decision with Big Data and Predictive Analytics | 21 hours | Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning. |
| bigdatar | Programming with Big Data in R | 21 hours | |
| predmodr | Predictive Modelling with R | 14 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| intror | Introduction to R with Time Series Analysis | 21 hours | R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining. |
| Piwik | Getting started with Piwik | 21 hours | Audience Web analysist Data analysists Market researchers Marketing and sales professionals System administrators Format of course Part lecture, part discussion, heavy hands-on practice |
| datamodeling | Pattern Recognition | 35 hours | This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners |
| kdd | Knowledge Discover in Databases (KDD) | 21 hours | 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 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. |
| matlabdsandreporting | MATLAB Fundamentals, Data Science & Report Generation | 126 hours | 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. |
| matlabpredanalytics | Matlab for Predictive Analytics | 21 hours | Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events. In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data. By the end of this training, participants will be able to: Create predictive models to analyze patterns in historical and transactional data Use predictive modeling to identify risks and opportunities Build mathematical models that capture important trends Use data to from devices and business systems to reduce waste, save time, or cut costs Audience Developers Engineers Domain experts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| bigdatabicriminal | Big Data Business Intelligence for Criminal Intelligence Analysis | 35 hours | Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another. In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results. By the end of this training, participants will be able to: Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation Implement industrial big data storage and processing solutions for data analysis Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation Audience Law Enforcement specialists with a technical background Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| aifortelecom | AI Awareness for Telecom | 14 hours | 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 |
Zukünftige Kurse
| Course | Schulungsdatum | Kurspreis (Fernkurs / Schulungsraum) |
|---|---|---|
| Predictive Modelling with R - Zürich | Di, 2018-05-08 09:30 | 3500EUR / 4000EUR |
| Angewandtes Maschinelles Lernen - Bern | Mi, 2018-05-09 09:30 | 3500EUR / 4000EUR |
| Programming with Big Data in R - Basel | Di, 2018-06-19 09:30 | 5250EUR / 5900EUR |