Process Mining Training Course

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 training in Guatemala, participants will gain an understanding of the technologies, methods and mindset needed to implement a Fintech strategy.

This training is aimed at managers who need to gain a "big picture" understanding of Fintech, break down the hype and jargon, and take tangible first steps towards the adoption of new technologies applicable to financial business and services.

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

• Present viable Fintech strategies for their organization.
• Understand and explain the role and functionality of key technologies.
• Draft an action plan for introducing new technologies in step-by-step fashion.

Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making.

In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data.

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

• Understand the key concepts and frameworks used in prescriptive analytics
• Use MATLAB and its toolboxes to acquire, clean and explore data
• Use rules-based techniques including inference engines, scorecards, and decision trees to make decisions based on different business scenarios
• Use Monte Carlo simulation to analyze uncertainties and ensure sound decision making
• Deploy predictive and prescriptive models to enterprise systems

Audience

• Business analysts
• Operations planners
• Functional managers
• BI (Business Intelligence) team members

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Model Based Development (MBD) is a software development methodology that enables faster, more cost-effective development of dynamic systems such as control systems, signal processing and communication systems. It relies on graphic modeling rather than the traditional text based programming.

In this instructor-led, live training participants will learn how to apply MBD methodologies to reduce development costs and accelerate the time to market of their embedded software products.

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

• Select and utilize the right tools for implementing MBD.
• Use MBD to carry out rapid development in the early stages of their embedded software project.
• Shorten the release of their embedded software into the market.

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Guatemala (online or onsite) is aimed at persons who wish to understand requirements analysis and conduct requirements analysis efficiently and accurately using analysis techniques for their projects.

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

• identify different types of requirements.
• understand the overall concepts of, and activities within, requirements analysis.
• be familiar with the requirements analysis methodology.
• use different requirements analysis techniques to their advantage.
• structure requirements in order to communicate efficiently with architects and developers through an iterative requirement gathering process.

This course demonstrates through hands-on practice the fundamentals and applications of software engineering, requirements engineering and testing.

This instructor-led, live training in Guatemala (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis.

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

• Use cluster analysis for data mining
• Master R syntax for clustering solutions.
• Implement hierarchical and non-hierarchical clustering.
• Make data-driven decisions to help to improve business operations.

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.

Objective:

Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results.

Course can be provided with any tools, including free open-source data mining software and applications

This instructor-led, live training (online or onsite) is aimed at data analysts and data scientists who wish to implement more advanced data analytics techniques for data mining using Python.

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

• Understand important areas of data mining, including association rule mining, text sentiment analysis, automatic text summarization, and data anomaly detection.
• Compare and implement various strategies for solving real-world data mining problems.
• Understand and interpret the results. 

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

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.

In this instructor-led, live training in Guatemala, participants will learn how to build a Data Vault.

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

• Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
• Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
• Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
• Build and deploy highly scalable and repeatable warehouses.

This course is intended for engineers and decision makers working in data mining and knoweldge discovery.

You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information.

This instructor-led, live training in Guatemala (online or onsite) is aimed at data scientists who wish to use Excel for data mining.

• By the end of this training, participants will be able to:
• Explore data with Excel to perform data mining and analysis.
• Use Microsoft algorithms for data mining.
• Understand concepts in Excel data mining.