Predictive Analytics Training Courses

Predictive Analytics Training

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.

NobleProg onsite live Predictive Analytics training courses demonstrate through hands-on practice how to use different tools to build predictive models and apply them to large sample data sets to predict future events based on the data.

Predictive Analytics training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Predictive Analytics training can be carried out live on customer premises or in NobleProg local training centers.

Client Testimonials

Predictive Analytics Course Outlines

Code Name Duration Overview
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.
appliedml Applied Machine Learning 14 hours This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.
bigdatar Programming with Big Data in R 21 hours
apachemdev Apache Mahout for Developers 14 hours Audience Developers involved in projects that use machine learning with Apache Mahout. Format Hands on introduction to machine learning. The course is delivered in a lab format based on real world practical use cases.
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.
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.
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

Upcoming Courses

CourseCourse DateCourse Price [Remote / Classroom]
Apache Mahout for Developers - UppsalaMon, 2018-03-12 09:303730EUR / 5030EUR
Applied Machine Learning - Malmö, StadskärnaThu, 2018-03-22 09:303730EUR / 3930EUR
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