Predictive Modelling with R Training Course

Overview

Communications service providers (CSP) are under pressure to cut costs and boost average revenue per user (ARPU), while maintaining a top-notch customer experience, but data volumes continue to rise. Global mobile data traffic is projected to grow at an annual compound growth rate of 78 percent by 2016, reaching 10.8 exabytes monthly.

Simultaneously, CSPs are generating vast amounts of data, including call detail records (CDR), network information, and customer details. Companies that fully leverage this data gain a competitive advantage. A recent survey by The Economist Intelligence Unit found that companies using data-driven decision-making experience a 5-6% productivity boost. However, only half of the valuable data is utilized by 53% of businesses, with one-fourth noting that significant amounts of useful data are overlooked. The sheer volume of data makes manual analysis impractical and most legacy software systems struggle to keep up, leading to valuable data being discarded or ignored.

With Big Data & Analytics’ high-speed, scalable big data software, CSPs can analyze all their data for improved decision-making in less time. Various Big Data products and techniques offer an end-to-end platform for collecting, preparing, analyzing, and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn prevention, and credit risk analysis. These Big Data & Analytics solutions can handle terabytes of data, but implementing such tools requires a new type of cloud-based database system like Hadoop or massive-scale parallel computing processors (KPU, etc.).

This course on Big Data BI for Telco covers all the emerging areas where CSPs are investing to enhance productivity and open up new revenue streams. It provides a comprehensive 360-degree overview of Big Data BI in the telecommunications sector so that decision-makers and managers can gain a broad understanding of the potential benefits of Big Data BI for productivity and revenue growth.

Course Objectives

The primary goal of this course is to introduce new Big Data business intelligence techniques across four sectors of Telecom Business (Marketing/Sales, Network Operations, Financial Operations, and Customer Relationship Management). Students will be introduced to the following:

• An introduction to Big Data, including the 4Vs (volume, velocity, variety, veracity) from a Telco perspective
• The differences between Big Data analytics and legacy data analytics
• Justifying the use of Big Data within a Telco context
• Familiarity with the Hadoop ecosystem and its tools like Hive, Pig, SPARC – understanding when and how they are used to address Big Data challenges
• The process of extracting data for analysis using an integrated Hadoop dashboard approach to ease business analysts' pain points in collecting and analyzing data
• Basic insights into analytics, visualization, and predictive analytics specific to Telco
• Customer churn analytics and how Big Data can reduce customer churn and dissatisfaction – case studies included
• Analyzing network failures and service issues using Network metadata and IPDR
• Financial analysis for fraud detection, waste reduction, and ROI estimation from sales and operational data
• Solving customer acquisition challenges through targeted marketing, customer segmentation, and cross-selling based on sales data
• A summary of all Big Data analytic products and their roles in the Telco analytics landscape
• Steps to introduce Big Data Business Intelligence into your organization

Target Audience

• Network Operations, Financial Managers, CRM managers, and top IT managers within the Telco CIO office.
• Telco business analysts
• CFO office managers/analysts
• Operational managers
• QA managers

In this instructor-led, live training in the UAE, 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

This course is ideal for you if you are trying to interpret the data at your disposal or wish to analyze unstructured data available online (such as from Twitter and LinkedIn).

It primarily targets decision-makers and individuals who must determine which data is valuable to collect and analyze.

While it is not specifically designed for those configuring solutions, they can still gain a comprehensive understanding of the big picture.

Delivery Mode

Throughout the course, participants will be shown practical examples using mostly open-source technologies.

Brief lectures will be complemented by presentations and straightforward exercises led by the attendees.

Content and Software Used

The software utilized in this course is updated with each session to ensure we are working with the latest versions available.

The curriculum encompasses the entire process from acquiring, formatting, processing, and analyzing data, to automating decision-making processes through machine learning.

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

By the end of this training, participants will be able to:

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

By the end of this training, participants will be able to:

• Understand the fundamentals of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Utilize the ‘forecast’ package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

By the end of this training, participants will be able to:

• Understand the fundamentals of predictive AI and generative models.
• Utilize AI-powered tools for predictive coding, forecasting, and automation.
• Implement LLMs (Large Language Models) and transformers for text and code generation.
• Apply time-series forecasting and AI-based recommendations.
• Develop and fine-tune AI models for real-world applications.
• Evaluate ethical considerations and best practices in AI deployment.

This instructor-led, live training in the UAE (online or onsite) is aimed at beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.

By the end of this training, participants will be able to:

• Understand the core concepts of Predictive AI and its applications.
• Collect, clean, and preprocess data for predictive analysis.
• Explore and visualize data to uncover insights.
• Build basic statistical models to make predictions.
• Evaluate the performance of predictive models.
• Apply Predictive AI concepts to real-world scenarios.

R is an open-source, freely available programming language designed for statistical computing, data analysis, and graphical representation. It is increasingly adopted by managers and data analysts within both corporate environments and academic institutions. R offers a broad range of packages specifically tailored for data mining.

Predictive analytics involves utilizing data analytics to forecast future trends. This method employs data mining, statistical analysis, and machine learning techniques to develop models that can predict upcoming events.

This instructor-led live training will guide participants on how to use Matlab to construct predictive models and apply them to extensive datasets for forecasting based on historical information.

By the conclusion of this course, attendees will be able to:

• Develop predictive models to examine patterns in past and transactional data
• Leverage predictive modeling to pinpoint risks and opportunities
• Create mathematical models that reflect significant trends
• Utilize data from devices and business systems to minimize waste, save time, or reduce costs

Audience

• Developers
• Engineers
• Domain experts

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

By the end of this training, participants will be able to:

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level DevOps professionals who wish to integrate predictive AI into their DevOps practices.

By the end of this training, participants will be able to:

• Implement predictive analytics models to forecast and solve challenges in the DevOps pipeline.
• Utilize AI-driven tools for enhanced monitoring and operations.
• Apply machine learning techniques to improve software delivery workflows.
• Design AI strategies for proactive issue resolution and optimization.
• Navigate the ethical considerations of using AI in DevOps.

RapidMiner is an open-source data science software platform designed for quick application prototyping and development. It offers an integrated environment for tasks such as data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training session, participants will learn how to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

By the end of this training, participants will be able to:

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Mash up data and create predictive models
• Integrate predictive analytics into business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Note

• To request a customized training for this course, please contact us to arrange.

Audience

• Managers
• Developers
• Scientists
• Students

Course Format

Online instruction and discussion OR in-person workshops

R is an open-source programming language designed for statistical computing, data analysis, and graphics. It is increasingly utilized by managers and data analysts within corporations and academic institutions. R has also gained popularity among statisticians, engineers, and scientists who may not have extensive computer programming experience but find it user-friendly. Its widespread adoption can be attributed to the growing importance of data mining for various purposes such as setting ad prices, accelerating drug discovery, or refining financial models. R offers a broad range of packages specifically tailored for data mining.

This course focuses on handling objects in R, including importing data, utilizing R packages, creating R functions, and generating insightful graphs. It also covers the analysis of data using common statistical models. The course provides instruction on how to use the R software (https://www.r-project.org) both through command-line operations and within a graphical user interface (GUI).

The Tidyverse is a suite of flexible R packages designed for data cleaning, processing, modeling, and visualization. Key components include ggplot2, dplyr, tidyr, readr, purrr, and tibble.

During this instructor-led live training session, participants will learn how to manipulate and visualize data using the tools provided by the Tidyverse.

By the end of the course, participants will be able to:

• Analyze data and generate compelling visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Create informative line plots, bar charts, and histograms from processed data
• Import and filter data from a variety of sources such as Excel, CSV, and SPSS files

Audience

• Beginners to the R programming language
• Newcomers to data analysis and visualization

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.