Predictive Analytics Training Courses
Local, instructor-led 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 as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in the UAE or in NobleProg corporate training centers in the UAE. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
Client Testimonials
ref material to use later was very good.
PAUL BEALES- Seagate Technology.
Course Name: Applied Machine Learning
What did you like the most about the training?: 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
He was very informative and helpful.
Pratheep Ravy
Course Name: Predictive Modelling with R
ref material to use later was very good
PAUL BEALES
Course Name: Applied Machine Learning
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
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
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 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
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
Course Name: From Data to Decision with Big Data and Predictive Analytics
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group
Course Name: From Data to Decision with Big Data and Predictive Analytics
the scope of material
Maciej Jonczyk
Course Name: From Data to Decision with Big Data and Predictive Analytics
Translated by
systematizing knowledge in the field of ML
Orange Polska
Course Name: From Data to Decision with Big Data and Predictive Analytics
Translated by
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. |
| 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. |
| bigdatar | Programming with Big Data in R | 21 hours | Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry. |
| 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 |
| bigdataanahealth | Big Data Analytics in Health | 21 hours | Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights. The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment. In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises. By the end of this training, participants will be able to: Install and configure big data analytics tools such as Hadoop MapReduce and Spark Understand the characteristics of medical data Apply big data techniques to deal with medical data Study big data systems and algorithms in the context of health applications Audience Developers 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. |
Upcoming Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| Applied Machine Learning - Dubai | Wed, 2018-07-18 09:30 | 4000USD / 5600USD |