Statistics Training in Quito

This instructor-led, live training in Quito (online or onsite) is aimed at intermediate-level engineers and designers who wish to use DFSS to ensure that quality is built into the product or process from the start of the design phase.

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

• Understand the principles and concepts of Design Six Sigma (DFSS).
• Learn the DMAIC (Define, Measure, Analyze, Improve, Control) and DMADV (Define, Measure, Analyze, Design, Verify) methodologies.
• Apply DFSS tools and techniques to design and optimize processes.
• Develop skills to manage and lead DFSS projects effectively.

This instructor-led, live training in Quito (online or onsite) is aimed at advanced-level professionals who wish to gain an in-depth understanding of the Six Sigma DMAIC methodology at a Black Belt level.

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

• Develop a comprehensive understanding of the Define, Measure, Analyze, Improve, and Control phases with a focus on complex projects.
• Gain expertise in advanced statistical analysis techniques for data-driven decision-making.
• Lead large-scale Six Sigma projects and manage project teams effectively.
• Implement significant process improvements that align with organizational goals and strategies.

This instructor-led, live training in Quito (online or onsite) is aimed at intermediate-level professionals who wish to have a comprehensive understanding of the DMAIC methodology and prepare for the role of an effective Six Sigma Green Belts.

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

• Gain a thorough knowledge of the Define, Measure, Analyze, Improve, and Control phases of Six Sigma.
• Apply a wide range of Six Sigma tools and techniques effectively within each phase of DMAIC.
• Perform and interpret statistical analyses for process improvement.
• Plan, execute, and manage a Six Sigma project.

This instructor-led, live training in Quito (online or onsite) is aimed at beginner-level professionals who wish to have a fundamental understanding of Six Sigma and Lean principles to integrate them and improve business processes.

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

• Obtain a solid understanding of Lean Six Sigma principles.
• Use Six Sigma methodologies to improve process quality by removing the causes of defects and minimizing variability.
• Integrate Lean and Six Sigma methodologies to achieve more efficient and effective process improvements.
• Teach basic Lean Six Sigma tools and techniques that Yellow Belts can apply in process improvement projects, such as 5S, Kaizen, and process mapping.

This instructor-led, live training in Quito (online or onsite) is aimed at intermediate-level professionals who wish to understand both Six Sigma and Lean principles in order to lead significant process improvement projects in their organizations.

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

• Develop a comprehensive understanding of how Lean and Six Sigma methodologies can be integrated for process improvement.
• Gain in-depth knowledge and application skills in the Define, Measure, Analyze, Improve, and Control phases.
• Apply advanced statistical tools for data-driven decision-making and process analysis.
• Lead and manage Lean Six Sigma projects effectively.

This instructor-led, live training in Quito (online or onsite) is aimed at advanced-level professionals who wish to gain the skills to handle complex Six Sigma projects and take a leadership role in Six Sigma and Lean implementations.

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

• Develop an in-depth understanding of advanced Six Sigma and Lean methodologies and tools.
• Lead complex process improvement projects that align with organizational strategies.
• Perform complex statistical analyses and make data-driven decisions.
• Effectively lead change initiatives and build a culture of continuous improvement.

This instructor-led, live training in Quito (online or onsite) is aimed at beginner-level professionals who wish to have a deeper understanding of the Six Sigma methodology and its principles and master the DMAIC process.

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

• Gain knowledge beyond the basic principles covered in the White Belt course.
• Master the DMAIC process and learn advanced Six Sigma tools.
• Learn how to effectively contribute to a Six Sigma project team and collaborate with Green and Black Belts.
• Apply Six Sigma methodologies to real-world business processes for improvement.

This instructor-led, live training in Quito (online or onsite) is aimed at beginner-level professionals who wish to have a basic understanding of the Six Sigma methodology and its principles.

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

• Understand the basics of Six Sigma and learn the DMAIC process.
• Understand the specific roles and responsibilities of a White Belt in the context of a Six Sigma project.
• Learn how to apply Six Sigma principles to identify improvement opportunities and support Six Sigma projects within an organization.

This instructor-led, live training in Quito (online or onsite) is aimed at scientists who wish to learn and use the Design of Experiments (DoE) to understand the cause-and-effect relationship between multiple factors.

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

• Understand the advantages of designed experiments over other approaches.
• Understand the cause-and-effect relationships and interactions between factors.
• Learn the best practices and guidelines for conducting successful experimentation.

This instructor-led, live training in Quito (online or onsite) is aimed at anyone who wishes to learn and master the fundamentals of econometric analysis and modeling.

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

• Learn and understand the fundamentals of econometrics.
• Utilize Eviews and risk simulators.

R is a very popular, open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students.  It covers language fundamentals, libraries and advanced concepts.  Advanced data analytics and graphing with real world data.

Audience

Developers / data analytics

Duration

3 days

Format

Lectures and Hands-on

Audience

Analysts, researchers, scientists, graduates and students and anyone who is interested in learning how to facilitate statistical analysis in Microsoft Excel.

Course Objectives

This course will help improve your familiarity with Excel and statistics and as a result increase the effectiveness and efficiency of your work or research.

This course describes how to use the Analysis ToolPack in Microsoft Excel, statistical functions and how to perform basic statistical procedures. It will explain what Excel limitation are and how to overcome them.

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

This instructor-led, live training (online or onsite) is aimed at HR professionals and recruitment specialists who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

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

This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.

Course Customization Options

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

This course covers advanced topics in R programming.

Goal:

Mastering the skill work independently with the program SPSS for advanced use, dialog boxes, and command language syntax for the selected analytical techniques.

The addressees:

Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and advanced level and learn the selected statistical models. Training takes universal analysis problems and it is dedicated to a specific industry

Audience:

The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment

Goal:

Deep knowledge on Hadoop cluster administration.

This instructor-led, live training in Quito (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

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

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

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.

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 also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

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.

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.

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.

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.

This course is intended for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.

The "Mathematica - introduction, visualization and presentation of data" training is aimed at participants with basic mathematical knowledge. Mathematica, as a powerful mathematical engine, enables effective calculations, creating text documents, building charts and presenting data. Participants will gain practical skills in working with Mathematica, used, among others, in mathematics, physics, biology, financial analysis and other fields.

Audience

Business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals.

Overview

The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why. We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech.

Format

Instructor-led training over the course of five half-day sessions with in-class exercises as well as homework. It can be delivered as a classroom or distance (online) course.

The course is aimed at anyone interested in statistical analysis. It provides familiarity with Minitab and will increase the effectiveness and efficiency of your data analysis and improve your knowledge of statistics.

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.

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 also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

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 also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

This course is an introduction to applying neural networks in real world problems using R-project software.

This course is for data scientists and statisticians that already have basic R & C++ coding skills and R code and need advanced R coding skills.

The purpose is to give a practical advanced R programming course to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience

This course is part of the Data Scientist skill set (Domain: Data and Technology)

Organization – Lab work and practical examples on real data

Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems.

In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.

By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.

Audience

• Data scientists and engineers, especially with interest in image processing and facial recognition

Format of the course

• Part lecture, part discussion, exercises and intensive hands-on practice, with a final project

This instructor-led, live training in Quito (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to perform statistical analysis using SPSS to interpret data accurately, run complex statistical tests, and generate meaningful insights.

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

• Navigate the SPSS interface and manage datasets efficiently.
• Perform descriptive and inferential statistical analyses.
• Conduct t-tests, ANOVA, MANOVA, regression, and correlation analyses.
• Apply non-parametric tests, principal component analysis, and factor analysis for advanced data interpretation.

Goal:

Learning to work with SPSS at the level of independence

The addressees:

Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.

This course has been created for decision makers whose primary goal is not to do the calculation and the analysis, but to understand them and be able to choose what kind of statistical methods are relevant in strategic planning of the organization.

For example, a prospect participant needs to make decision how many samples needs to be collected before they can make the decision whether the product is going to be launched or not.

If you need longer course which covers the very basics of statistical thinking have a look at 5 day "Statistics for Managers" training.

This course has been created for people who require general statistics skills. This course can be tailored to a specific area of expertise like market research, biology, manufacturing, public sector research, etc...

This training course covers advanced statistics. It explains most of the tools commonly used in research, analysis and forecasting. It provides short explanations of the theory behind the formulas.

This course does not relate to any specific field of knowledge, but can be tailored if all the delegates have the same background and goals.

Some basic computer tools are used during this course (notably Excel and OpenOffice)

This course aims to give researchers an understanding of the principles of statistical design and analysis and their relevance to research in a range of scientific disciplines.

It covers some probability and statistical methods, mainly through examples. This training contains around 30% of lectures, 70% of guided quizzes and labs.

In the case of closed course we can tailor the examples and materials to a specific branch (like psychology tests, public sector, biology, genetics, etc...)

In the case of public courses, mixed examples are used.

Though various software is used during this course (Microsoft Excel to SPSS, Statgraphics, etc...) its main focus is on understanding principles and processes guiding research, reasoning and conclusion.

This course can be delivered as a blended course i.e. with homework and assignments.

Tableau helps people see and understand data.

Tableau helps people see and understand data.

The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.

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

• Perform data analysis and create appealing visualizations
• Draw useful conclusions from various datasets of sample data
• Filter, sort and summarize data to answer exploratory questions
• Turn processed data into informative line plots, bar plots, histograms
• Import and filter data from diverse data sources, including Excel, CSV, and SPSS files

Audience

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

Format of the course

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

Description: 

This is a course designed to teach R users how to create web apps without needing to learn cross-browser HTML, Javascript, and CSS.

Objective:

Covers the basics of how Shiny apps work.

Covers all commonly used input/output/rendering/paneling functions from the Shiny library.

The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.

This instructor-led, live training in Quito (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.

Público Objetivo

• gerentes
• desarrolladores
• cienctíficos
• estudiantes

Formato del curso

instrucción y discusión en línea O talleres presenciales

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 also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

This course covers the manipulation of objects in R including reading data, accessing R packages, writing R functions, and making informative graphs. It includes analyzing data using common statistical models. The course teaches how to use the R software (https://www.r-project.org) both on a command line and in a graphical user interface (GUI).