Os cursos de treinamento em Análise de Dados (Análise de Dados ou Análise de Dados) ao vivo, local e instruídos, demonstram, através de discussões e prática prática, as linguagens de programação e as metodologias usadas para executar a Análise de Dados O treinamento em Análise de Dados está disponível como "treinamento ao vivo no local" ou "treinamento remoto ao vivo" Treinamento ao vivo no local pode ser realizado localmente nas instalações do cliente em Brasil ou nos centros de treinamento corporativo da NobleProg em Brasil O treinamento ao vivo remoto é realizado por meio de uma área de trabalho remota e interativa NobleProg seu provedor de treinamento local.
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Declaração de Clientes
O método de contato do estagiário. A abordagem é boa para o grupo e para o indivíduo.
Marcin Szcześniak - Credit Agricole Bank P
Curso: BPMN 2.0 dla Analityków Biznesowych
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Uma grande dose de exercício.
Piotr Kubiński
Curso: BPMN 2.0 dla Analityków Biznesowych
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Eu preferi o exercício e aprender sobre os cantos e recantos de Python
Connor Brierley-Green
Curso: Python Programming
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Joey tem um entusiasmo contagiante sobre programação. E ele foi muito bom em se adaptar às nossas necessidades e interesses na hora.
Randy Enkin
Curso: Python Programming
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Muitos exemplos me facilitaram a entender.
Lingmin Cao
Curso: Python Programming
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Teste final simulando questões de exames, onde tentamos como um grupo para resolver tarefas com base no conhecimento adquirido durante o treinamento.
Dominik Łubiński
Curso: Business Analysis, BABOK V3.0 and IIBA Certification Preparation
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Eu gostei principalmente do conhecimento do instrutor.
Sripal S
Curso: Understanding Business Process Modeling with BPMN 2.0
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Eu geralmente gostava da maneira de transmitir a fala.
Bhaskar naidu
Curso: Understanding Business Process Modeling with BPMN 2.0
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O conhecimento abrangente do guia para todas as nossas perguntas deu respostas esmagadoras minhas expectativas ... O palestrante conduz grandes discussões ... Ele não falta paciência ...
Łukasz Matulewicz
Curso: Programowanie w języku Python
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Grande conhecimento do palestrante, uma variedade de ferramentas e uma abordagem prática do tema
Magdalena Stupak
Curso: Programowanie w języku Python
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grande conhecimento do treinador, como traduzir
Renata Cylejowska
Curso: Programowanie w języku Python
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Fato que personalização foi levada a sério.
jurgen linsen
Curso: Python Programming
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Eu realmente gostei da ajuda do treinador.
Urszula Kuza
Curso: Tableau Advanced
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tudo
Grześ
Curso: BPMN 2.0 dla Analityków Biznesowych
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Exemplos práticos.
Przemysław Domański
Curso: BPMN 2.0 dla Analityków Biznesowych
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Recebo respostas sobre todas as minhas perguntas.
Natalia Gladii
Curso: Data Analytics With R
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Ganhei uma boa visão geral dos editores de processos, regras de negócios e panorama das ferramentas do BPM S. Eu recomendo fortemente o curso prático de BPM N for Business Analysts, mesmo para analistas experientes, com seus excelentes cenários de processo e prática de modelagem que posicionarão você acima do resto!
Anees Baig
Curso: BPMN 2.0 for Business Analysts
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Muito informativo e deu um resumo geral agradável do esboço do curso.
Matthew Steptoe
Curso: Contemporary Development Principles and Practices
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Eu gostei de todo o dia 1.
Peter Mahaffey
Curso: Contemporary Development Principles and Practices
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O fato de termos preparado soluções para problemas determinados, mostrando uma ampla gama de aplicações + passou por um compêndio de conhecimento. W
Wojciech Michalak
Curso: Power BI
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Eu gostei dos exercícios, pois é a única maneira de aprender, por repetição.
David Rushe
Curso: Tableau Advanced
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O treinador tem um conhecimento muito amplo, ele pode transmiti-lo. Envolve participantes para interagir e resolver problemas encontrados.
Nina Rokita
Curso: Power BI
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O treinador estava em constante contato com os destinatários do treinamento, ajustou o ritmo às nossas necessidades e resolveu nossos problemas de forma contínua e respondeu às nossas perguntas
Dominika Jaworska
Curso: Power BI
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Gostei dos exercícios, da sala de treinamento, da cozinha de chá.
Alisher Khaydarov
Curso: BPMN 2 Fundamentals and Workshop
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O ritmo e a progressão foram perfeitos para realmente entender as coisas rapidamente.
Stephen Brewell
Curso: BPMN 2.0 for Business Analysts
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Os exemplos dados realmente me ajudaram a entender alguns conceitos. O treinador certificou-se de que estávamos seguindo todo o treinamento e não hesitou em voltar a um ponto anterior de qualquer dúvida. O treinador adaptou o treinamento às nossas necessidades, dando foco aos itens que gostaríamos de ter mais detalhes. Exercícios práticos eram bons também.
Amanda Moscardini - Campinas Valley
Curso: Understanding Business Process Modeling with BPMN 2.0
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Debra fez questão de entender minhas perguntas e as respondeu com precisão. As sugestões sobre como desenhar diagramas sob certas condições eram realmente boas. Eu gostaria de reforçar o quão profissional e eficiente Debra estava conosco.
Leandro Antonio
Curso: Understanding Business Process Modeling with BPMN 2.0
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O treinamento foi muito consistente, eu aprendi sobre muitas funcionalidades interessantes do programa.
Anna Klimczak
Curso: Power BI
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Bem preparado, exercícios praticados e conhecimento do professor
Sandra Przewdzing
Curso: Power BI
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Eu gosto dos exercícios feitos.
Nour Assaf
Curso: Data Mining and Analysis
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O exercício prático e a capacidade do instrutor de explicar tópicos complexos em termos simples.
youssef chamoun
Curso: Data Mining and Analysis
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As informações fornecidas foram interessantes e a melhor parte foi no final quando recebemos os dados da Durex e trabalhamos na Data com a qual estamos familiarizados e realizamos operações para obter resultados.
Jessica Chaar
Curso: Data Mining and Analysis
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Eu gostei do útil e muito gentil.
Natalia Machrowicz
Curso: Python Programming
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Nós fizemos exercícios práticos (os roteiros que escrevemos podem ser usados em nosso trabalho diário). Isso tornou o curso muito interessante. Eu também gostei da maneira como o treinador compartilhou seu conhecimento. Ele fez isso de uma maneira muito acessível.
Malwina Sawa
Curso: Python Programming
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Eu pensei que a informação era interessante.
Allison May
Curso: Data Visualization
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Eu realmente gostei que Jeff utilizou dados e exemplos que eram aplicáveis aos dados educacionais. Ele tornou interessante e interativo.
Carol Wells Bazzichi
Curso: Data Visualization
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Aprendendo sobre todos os tipos de gráficos e para que eles são usados. Aprendendo o valor da desordem. Aprendendo sobre os métodos para mostrar dados de tempo.
Susan Williams
Curso: Data Visualization
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Treinador estava entusiasmado.
Diane Lucas
Curso: Data Visualization
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Eu realmente gostei do conteúdo / instrutor.
Craig Roberson
Curso: Data Visualization
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Eu sou um aluno prático e isso foi algo que ele fez muito.
Lisa Comfort
Curso: Data Visualization
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Eu gostei dos exemplos.
Peter Coleman
Curso: Data Visualization
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Eu geralmente me beneficiava dos exemplos.
Peter Coleman
Curso: Data Visualization
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Gostei dos bons exemplos do mundo real, revisões de relatórios existentes.
Ronald Parrish
Curso: Data Visualization
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Eu gosto da forma como o Birol personalizou algumas partes do treinamento para atender ao meu papel como Analista de Business . Desta forma, ele foi capaz de se concentrar nos tópicos que são importantes e não se debruçar sobre tópicos que eu geralmente não usarei no meu trabalho. Ele ensina muito claro e garante que você entenda um assunto antes de ir para o próximo. Você pode dizer que ele é um professor experiente e muito conhecedor de seu assunto, e sabe como analisar as necessidades de treinamento do aluno para efetivamente cobrir tópicos que são essenciais e relevantes. Isso faz com que suas sessões sejam muito eficazes, pois são flexíveis e corretas. Seus materiais são muito abrangentes e ele faz questão de seguir o currículo, mas torná-lo mais relevante para o aluno. Eu aprecio que ele conhece os padrões da indústria e é capaz de dar seus insights sobre o que funciona. Eu nunca aprendi tanto quanto eu em um curso de dois dias e vale a pena todo o tempo e esforço. O treinamento acelerou meu aprendizado do BPM N 2.0 e me deu o básico e a confiança para começar a praticar a técnica no meu trabalho. Obrigado Birol e NobleProg por uma maravilhosa experiência de aprendizado Eu certamente recomendarei o curso e o instrutor para futuros colegas.
Irma Irosido
Curso: BPMN 2.0 for Business Analysts
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Eu realmente me beneficiei da disposição do treinador em compartilhar mais.
Balaram Chandra Paul
Curso: A practical introduction to Data Analysis and Big Data
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Muito boa abordagem para memorizar / repetir os tópicos principais. Muito bom "warm-up" exercícios.
Curso: Python Programming
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* Exercícios agradáveis. * Rapidamente mudou-se para tópicos mais avançados. * Trainer foi amigável e fácil de se levar. * Curso personalizado para necessidades de equipe.
Matthew Lucas
Curso: Python Programming
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Eu realmente me beneficiei da interatividade e do instrutor dedicado.
Pierre Bruwier
Curso: BPMN 2 Fundamentals and Workshop
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O treinador tem muitas anedotas e pode mostrar conteúdo em simples palestras de vida
Katarzyna Gołda
Curso: Business Analysis
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O Sr. Bogdan é um excelente especialista e sabe como passar o conhecimento de forma acessível. Ele entende e percebe quando o público "desliga".
Unit4 Polska sp. z o.o; ALTEN Polska Sp. z o.o.; UBS; Zerochaos Sp. z o.o.
Curso: Business Analysis
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Muito boa abordagem para memorizar / repetir os tópicos principais. Muito bom "warm-up" exercícios.
Curso: Python Programming
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Nossos Clientes
.png "iberoamericana de hidrocarburos (ihsa)")
.jpg "european southern observatory (eso)")
_ireland.gif "environmental protection agency (epa) ireland")
Programas do curso Análise de Dados
Nome do Curso
Duração
Visão geral
Nome do Curso
Duração
Visão geral
21 horas
Visão geral
Este curso foi criado para analistas de negócios que desejam usar o BPMN 20 extensivamente em seus projetos Ele se concentra nos aspectos práticos de todas as especificações do BPMN 20, bem como na implementação de padrões comuns É uma série de pequenas palestras seguidas de exercícios: os delegados terão um problema descrito em inglês e terão que criar um diagrama adequado para cada problema Depois disso, os diagramas serão discutidos e avaliados pelo grupo e pelo treinador Este curso não cobre parte da execução do BPMN, ele se concentra em aspectos de análise e projeto de processo do BPMN 20 .
14 horas
Visão geral
Público
Analistas, pesquisadores, cientistas, graduados e estudantes e qualquer pessoa interessada em aprender como facilitar a análise estatística no Microsoft Excel .
Objetivos do Curso
Este curso ajudará a melhorar sua familiaridade com o Excel e as estatísticas e, como resultado, aumentará a eficácia e a eficiência do seu trabalho ou pesquisa.
Este curso descreve como usar o Analysis ToolPack no Microsoft Excel , funções estatísticas e como executar procedimentos estatísticos básicos. Explicará o que são as limitações do Excel e como superá-las.
Analistas, pesquisadores, cientistas, graduados e estudantes e qualquer pessoa interessada em aprender como facilitar a análise estatística no Microsoft Excel .
Objetivos do Curso
Este curso ajudará a melhorar sua familiaridade com o Excel e as estatísticas e, como resultado, aumentará a eficácia e a eficiência do seu trabalho ou pesquisa.
Este curso descreve como usar o Analysis ToolPack no Microsoft Excel , funções estatísticas e como executar procedimentos estatísticos básicos. Explicará o que são as limitações do Excel e como superá-las.
28 horas
Visão geral
Este curso foi concebido para aqueles que desejam aprender a linguagem de programação Python. A ênfase está na linguagem Python, nas bibliotecas principais, bem como na seleção das melhores e mais úteis bibliotecas desenvolvidas pela comunidade Python. Python dirige negócios e é usado por cientistas em todo o mundo - é uma das linguagens de programação mais populares.
O curso pode ser entregue usando Python 2.7.x ou 3.x, com exercícios práticos fazendo uso do poder total de ambas as versões do idioma. Este curso pode ser entregue em qualquer sistema operacional (todos os sabores do UNIX, incluindo Linux e Mac OS X, bem como o Microsoft Windows).
Os exercícios práticos constituem cerca de 70% do tempo do curso e cerca de 30% são demonstrações e apresentações. Podem ser feitas discussões e perguntas ao longo do curso.
Nota: o treinamento pode ser adaptado a necessidades específicas mediante solicitação prévia antes da data do curso proposto.
O curso pode ser entregue usando Python 2.7.x ou 3.x, com exercícios práticos fazendo uso do poder total de ambas as versões do idioma. Este curso pode ser entregue em qualquer sistema operacional (todos os sabores do UNIX, incluindo Linux e Mac OS X, bem como o Microsoft Windows).
Os exercícios práticos constituem cerca de 70% do tempo do curso e cerca de 30% são demonstrações e apresentações. Podem ser feitas discussões e perguntas ao longo do curso.
Nota: o treinamento pode ser adaptado a necessidades específicas mediante solicitação prévia antes da data do curso proposto.
21 horas
Visão geral
R é um ambiente muito popular de código aberto para computação estatística, análise de dados e gráficos. Este curso introduz a linguagem de programação R para estudantes, cobre os fundamentos dessa linguagem, bibliotecas e conceitos avançados. Análise de dados e graficos avançados com dados reais.
7 horas
Visão geral
Este curso cobre o básico sobre como utilizar a linguagem Hive SQL, para todas aquelas pessoas que querem extraír dados do Hive. O objetivo deste curso é proporcionar todas as ferramentas necessárias para que os participantes possam analizar os dados de forma clara e precisa.
14 horas
Visão geral
Pandas é um pacote Python que fornece estruturas de dados para trabalhar com dados estruturados (tabulares, multidimensionais, potencialmente heterogêneos) e séries temporais.
21 horas
Visão geral
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 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.
21 horas
Visão geral
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 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
35 horas
Visão geral
Participants who complete this instructor-led, live training in Brasil will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
14 horas
Visão geral
O Elasticsearch é um mecanismo de pesquisa distribuído e de código aberto. É comumente usado junto com o Logstash (mecanismo de coleta de dados e análise de log) e Kibana (plataforma de análise e visualização) para compor a "pilha ELK".
Esse treinamento é destinado a desenvolvedores de software que desejam criar soluções de pesquisa e análise usando o Elasticsearch.
O treinamento começa com uma discussão sobre a arquitetura do Elasticksearch, incluindo seu modelo distribuído e a API de pesquisa. Isto é seguido por uma explicação da funcionalidade do Elasticsearch e como melhor integrá-lo em um aplicativo existente.
Exercícios práticos constituem uma parte importante do treinamento e dão aos participantes a chance de colocar em prática seus conhecimentos enquanto recebem feedback sobre sua implementação e progresso.
Público
Desenvolvedores de software
Formato do curso
Forte ênfase na prática ao vivo. A maioria dos conceitos é aprendida através de amostras, exercícios e desenvolvimento prático.
Esse treinamento é destinado a desenvolvedores de software que desejam criar soluções de pesquisa e análise usando o Elasticsearch.
O treinamento começa com uma discussão sobre a arquitetura do Elasticksearch, incluindo seu modelo distribuído e a API de pesquisa. Isto é seguido por uma explicação da funcionalidade do Elasticsearch e como melhor integrá-lo em um aplicativo existente.
Exercícios práticos constituem uma parte importante do treinamento e dão aos participantes a chance de colocar em prática seus conhecimentos enquanto recebem feedback sobre sua implementação e progresso.
Público
Desenvolvedores de software
Formato do curso
Forte ênfase na prática ao vivo. A maioria dos conceitos é aprendida através de amostras, exercícios e desenvolvimento prático.
35 horas
Visão geral
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.
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.
28 horas
Visão geral
In this instructor-led, live training in Brasil, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, data analysis and visualization, UI programming and maintenance scripting.
42 horas
Visão geral
Data analytics is a crucial tool in business today. We will focus throughout on developing skills for practical hands on data analysis. The aim is to help delegates to give evidence-based answers to questions:
What has happened?
- processing and analyzing data
- producing informative data visualizations
What will happen?
- forecasting future performance
- evaluating forecasts
What should happen?
- turning data into evidence-based business decisions
- optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
What has happened?
- processing and analyzing data
- producing informative data visualizations
What will happen?
- forecasting future performance
- evaluating forecasts
What should happen?
- turning data into evidence-based business decisions
- optimizing processes
The course itself can be delivered either as a 6 day classroom course or [remotely](https://www.nobleprog.co.uk/instructor-led-online-training-courses) over a period of weeks if preferred. We can work with you to deliver the course to best suit your needs.
21 horas
Visão geral
The aim of this course is to provide a clear understanding of the use of SQL for different
databases (Oracle, SQL Server, MS Access...). Understanding of analytic functions and the
way how to join different tables in a database will help delegates to move data analysis
operations to the database side, instead of doing this in MS Excel application. This can also
help in creating any IT system, which uses any relational database.
databases (Oracle, SQL Server, MS Access...). Understanding of analytic functions and the
way how to join different tables in a database will help delegates to move data analysis
operations to the database side, instead of doing this in MS Excel application. This can also
help in creating any IT system, which uses any relational database.
14 horas
Visão geral
Apache Kylin is an extreme, distributed analytics engine for big data.
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion.
In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
Audience
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
Audience
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.
By the end of this training, participants will be able to:
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
kdb+ is an in-memory, column-oriented database and q is its built-in, interpreted vector-based language. In kdb+, tables are columns of vectors and q is used to perform operations on the table data as if it was a list. kdb+ and q are commonly used in high frequency trading and are popular with the major financial institutions, including Goldman Sachs, Morgan Stanley, Merrill Lynch, JP Morgan, etc.
In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.
By the end of this training, participants will be able to:
- Understand the difference between a row-oriented database and a column-oriented database
- Select data, write scripts and create functions to carry out advanced analytics
- Analyze time series data such as stock and commodity exchange data
- Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
- Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
- Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring
Audience
- Developers
- Database engineers
- Data scientists
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create a time series data application using kdb+ and q.
By the end of this training, participants will be able to:
- Understand the difference between a row-oriented database and a column-oriented database
- Select data, write scripts and create functions to carry out advanced analytics
- Analyze time series data such as stock and commodity exchange data
- Use kdb+'s in-memory capabilities to store, analyze, process and retrieve large data sets at high speed
- Think of functions and data at a higher level than the standard function(arguments) approach common in non-vector languages
- Explore other time-sensitive applications for kdb+, including energy trading, telecommunications, sensor data, log data, and machine and network usage monitoring
Audience
- Developers
- Database engineers
- Data scientists
- Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
Data science is the application of statistical analysis, machine learning, data visualization and programming for the purpose of understanding and interpreting real-world data. F# is a well suited programming language for data science as it combines efficient execution, REPL-scripting, powerful libraries and scalable data integration.
In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems.
By the end of this training, participants will be able to:
- Use F#'s integrated data science packages
- Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python
- Use the Deedle package to solve time series problems
- Carry out advanced analysis with minimal lines of production-quality code
- Understand how functional programming is a natural fit for scientific and big data computations
- Access and visualize data with F#
- Apply F# for machine learning
Explore solutions for problems in domains such as business intelligence and social gaming
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use F# to solve a series of real-world data science problems.
By the end of this training, participants will be able to:
- Use F#'s integrated data science packages
- Use F# to interoperate with other languages and platforms, including Excel, R, Matlab, and Python
- Use the Deedle package to solve time series problems
- Carry out advanced analysis with minimal lines of production-quality code
- Understand how functional programming is a natural fit for scientific and big data computations
- Access and visualize data with F#
- Apply F# for machine learning
Explore solutions for problems in domains such as business intelligence and social gaming
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 horas
Visão geral
R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the R programming language
- Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate deploy and optimize an R application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
21 horas
Visão geral
Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery.
In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.
By the end of this training, participants will be able to:
- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel
Audience
- Data analysts
- Data scientists
- SQL programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.
By the end of this training, participants will be able to:
- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel
Audience
- Data analysts
- Data scientists
- SQL programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
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
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
21 horas
Visão geral
Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries.
In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories.
By the end of this training, participants will be able to:
- Install and configure Dremio
- Execute queries against multiple data sources, regardless of location, size, or structure
- Integrate Dremio with BI and data sources such as Tableau and Elasticsearch
Audience
- Data scientists
- Business analysts
- Data engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories.
By the end of this training, participants will be able to:
- Install and configure Dremio
- Execute queries against multiple data sources, regardless of location, size, or structure
- Integrate Dremio with BI and data sources such as Tableau and Elasticsearch
Audience
- Data scientists
- Business analysts
- Data engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
14 horas
Visão geral
In this instructor-led, live training in Brasil (onsite or remote), participants will learn how to combine the capabilities of Python and Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
14 horas
Visão geral
A business' marketing strategy is one of its most essential tools to ensure success. The rise in technology and advances in data gathering and analysis has led to a continuous transformation of marketing. Data analysis is now considered to be a crucial skill for marketers to learn. Likewise, applying data science to marketing principles is also important in helping data science professionals maximize their contributions.
In this instructor-led, live training, participants will learn how to gain valuable insights from large data sets and how to use these for marketing strategies.
By the end of this training, participants will be able to:
- Install and configure tools for data-driven marketing tools (Tableau, Google Analytics, Zarget, etc)
- Measure, test, and optimize the performance of their marketing strategies using data
- Use data to segment their customers and prospects
- Use predictive modeling to improve response rates
- Implement step-by-step data analytics to drive better marketing decisions
Audience
- Marketers
- 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.
In this instructor-led, live training, participants will learn how to gain valuable insights from large data sets and how to use these for marketing strategies.
By the end of this training, participants will be able to:
- Install and configure tools for data-driven marketing tools (Tableau, Google Analytics, Zarget, etc)
- Measure, test, and optimize the performance of their marketing strategies using data
- Use data to segment their customers and prospects
- Use predictive modeling to improve response rates
- Implement step-by-step data analytics to drive better marketing decisions
Audience
- Marketers
- 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.
35 horas
Visão geral
Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.
Audience
- Developers
- Technical analysts
- IT consultants
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.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.
Audience
- Developers
- Technical analysts
- IT consultants
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.
14 horas
Visão geral
Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks.
In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.
By the end of this training, participants will be able to:
- Install and configure Apache Arrow in a distributed clustered environment
- Use Apache Arrow to access data from disparate data sources
- Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
- Analyze data across disparate data sources without having to consolidate it into a centralized repository
Audience
- Data scientists
- Data engineers
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.
In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.
By the end of this training, participants will be able to:
- Install and configure Apache Arrow in a distributed clustered environment
- Use Apache Arrow to access data from disparate data sources
- Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
- Analyze data across disparate data sources without having to consolidate it into a centralized repository
Audience
- Data scientists
- Data engineers
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.
14 horas
Visão geral
About the Course:
Microsoft Power BI transforms your company data into rich visuals that facilitate new ways of thinking about and organizing your data, so that you can focus on what is important to achieving your goals. This course covers both Power BI on Line and Power BI desktop.
Audience:
This course is intended for Business Managers, Report Developers, Analysts, Project Managers and Team Leads.
At Course Completion
Upon completing this course, students will have a basic understanding of the topics below, as well as an ability to utilize and implement the concepts learned.
- Power BI
- Power BI Desktop
- Working with CSV, TXT, and Excel Worksheets
- Connecting to Databases
- Merging, Grouping, Summarizing, and Calculating Data
- Reporting
- Power BI Online
Microsoft Power BI transforms your company data into rich visuals that facilitate new ways of thinking about and organizing your data, so that you can focus on what is important to achieving your goals. This course covers both Power BI on Line and Power BI desktop.
Audience:
This course is intended for Business Managers, Report Developers, Analysts, Project Managers and Team Leads.
At Course Completion
Upon completing this course, students will have a basic understanding of the topics below, as well as an ability to utilize and implement the concepts learned.
- Power BI
- Power BI Desktop
- Working with CSV, TXT, and Excel Worksheets
- Connecting to Databases
- Merging, Grouping, Summarizing, and Calculating Data
- Reporting
- Power BI Online
7 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at persons who wish to use graphical and numerical techniques to better understand the structure of a dataset before commencing more formal analysis.
By the end of this training, participants will be able to:
- Identify variables that that indicate connections within the data.
- Identify unusual patterns in data.
- Generate visually appealing and easy to read graphics that provide insight into the data.
- Use exploratory analysis to pave the way for more complex statistical modeling.
By the end of this training, participants will be able to:
- Identify variables that that indicate connections within the data.
- Identify unusual patterns in data.
- Generate visually appealing and easy to read graphics that provide insight into the data.
- Use exploratory analysis to pave the way for more complex statistical modeling.
14 horas
Visão geral
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.
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