Місцеві, під керівництвом інструктора Live Data Analysis (аналіз даних або аналітика даних) навчальні курси демонструють через дискусію і практичний практиці мови програмування та методики, що використовуються для виконання аналізу даних.
Аналіз даних підготовка доступна як "на місці навчання" або "дистанційне навчання жити". На місці навчання в прямому ефірі може здійснюватися на місцевому рівні в україні або в Неbleprog корпоративні навчальні центри в україні. Дистанційне навчання в прямому ефірі здійснюється шляхом інтерактивного, віддаленого робочого стола.
Nobleпрога-ваш місцевий навчальний провайдер
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Відгуки
Спосіб контакту з оператором-навчений. Гарний підхід як для групи, так і для окремої людини.
Marcin Szcześniak - Credit Agricole Bank P
Курси: BPMN 2.0 dla Analityków Biznesowych
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сьвікценія
Piotr Kubiński
Курси: BPMN 2.0 dla Analityków Biznesowych
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Я віддав перевагу вправі та вивченню про куточки Python
Connor Brierley-Green
Курси: Python Programming
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Джо має інфекційний ентузіазм з приводу програмування. І він був дуже хорошим для адаптації до наших потреб і інтересів на льоту.
Randy Enkin
Курси: Python Programming
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Багато прикладів змусило мене легко зрозуміти.
Lingmin Cao
Курси: Python Programming
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Підсумковий тест імітує питання іспиту, де ми намагалися як групу вирішувати завдання, засновані на підготовці отриманих знань.
Dominik Łubiński
Курси: Business Analysis, BABOK V3.0 and IIBA Certification Preparation
Machine Translated
Я в основному любив знання тренера.
Sripal S
Курси: Understanding Business Process Modeling with BPMN 2.0
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Я взагалі користувався способом доставки мови.
Bhaskar naidu
Курси: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
Всебічна пізнання викладача-всі наші питання дали відповіді на переправляє мої очікування... Лектор має велику дискусію... Йому не вистачає терпіння...
Łukasz Matulewicz
Курси: Programowanie w języku Python
Machine Translated
Високі знання, різноманітність інструментів і практичний підхід до теми
Magdalena Stupak
Курси: Programowanie w języku Python
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Велике знання тренера, спосіб перекладу
Renata Cylejowska
Курси: Programowanie w języku Python
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Факт, що настройка була взята всерйоз.
jurgen linsen
Курси: Python Programming
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Я щиро користувався допомогу тренера.
Urszula Kuza
Курси: Tableau Advanced
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Всі
Grześ
Курси: BPMN 2.0 dla Analityków Biznesowych
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Практичні приклади.
Przemysław Domański
Курси: BPMN 2.0 dla Analityków Biznesowych
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Я отримую відповіді на всі мої питання.
Natalia Gladii
Курси: Data Analytics With R
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Я отримав хороший огляд редакторів процесів, бізнес-правил та ландшафту інструментів BPM S. Я настійно рекомендую практичний курс BPM N для Business аналітиків, навіть для досвідчених аналітиків, завдяки прекрасним сценаріям процесів та практиці моделювання він дозволить вам стати на поріг вище за решту!
Anees Baig
Курси: BPMN 2.0 for Business Analysts
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Дуже інформативно і дав хороший загальний підсумок курсу начерки.
Matthew Steptoe
Курси: Contemporary Development Principles and Practices
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Я користувався весь день 1.
Peter Mahaffey
Курси: Contemporary Development Principles and Practices
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Справа в тому, що ми отримали готові рішення для поставлених завдань + Показати широкий спектр застосувань + надав збірник знань.
Wojciech Michalak
Курси: Power BI
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Я любив вправи, як це єдиний спосіб дізнатися, за повторення.
David Rushe
Курси: Tableau Advanced
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Тренер має дуже широкі знання, вона може передати її. Він engens учасникам взаємодіяти і спільно вирішувати проблеми, що виникають.
Nina Rokita
Курси: Power BI
Machine Translated
Тренер був постійний контакт з одержувачами навчання, Скоригований темп на наші потреби і в актуальному стані вирішувати наші проблеми і відповів на наші запитання
Dominika Jaworska
Курси: Power BI
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Мені подобалося вправи, навчальна кімната, чайна кухня.
Alisher Khaydarov
Курси: BPMN 2 Fundamentals and Workshop
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Темп і прогресія була досконалою, щоб дійсно зрозуміти речі швидко.
Stephen Brewell
Курси: BPMN 2.0 for Business Analysts
Machine Translated
Приклади, наведені дійсно допомогли мені зрозуміти деякі поняття. Тренер зробив впевнений, що ми були наступні навчання і не соромтеся повернутися до попередньої точки було ніяких сумнівів. Тренер адаптувала навчання до наших потреб, приділяючи увагу пунктам, які ми хотіли б мати більш детальну інформацію. Практичні заняття теж були хорошими.
Amanda Moscardini - Campinas Valley
Курси: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
Дебра зрозуміла, щоб зрозуміти мої питання і відповів їм точно. Пропозиції щодо дизайну діаграм за певних умов були дійсно хорошими. Я хотів би зміцнити, як професійна і ефективна Дебра була з нами.
Leandro Antonio
Курси: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
Тренінг був дуже послідовним, я дізнався про багато цікавих функціональних можливостей програми.
Anna Klimczak
Курси: Power BI
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Добре підготовлені, вдумливі і знаючі
Sandra Przewdzing
Курси: Power BI
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Мені подобається вправи зробили.
Nour Assaf
Курси: Data Mining and Analysis
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Практичний вправи і тренер здатності пояснювати складні теми простими термінами.
youssef chamoun
Курси: Data Mining and Analysis
Machine Translated
Надана інформація була цікавою і найкраща частина була до кінця, коли ми були надані дані з Durex і працював над даними, які ми знайомі з і виконувати операції, щоб отримати результати.
Jessica Chaar
Курси: Data Mining and Analysis
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Дуже сподобався персонал.
Natalia Machrowicz
Курси: Python Programming
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Ми робили практичні вправи (скрипти, які ми написали, можуть бути використані в нашій повсякденній роботі). Це зробило курс дуже цікавим. Я також любив, як тренер поділився своїми знаннями. Він зробив це дуже доступним способом.
Malwina Sawa
Курси: Python Programming
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Я думала, що інформація була цікава.
Allison May
Курси: Data Visualization
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Я дійсно оцінив, що Джефф використовував дані і приклади, які були застосовні до освітніх даних. Він зробив це цікавим і інтерактивним.
Carol Wells Bazzichi
Курси: Data Visualization
Machine Translated
Дізнатися про всі типи діаграм і для чого вони використовуються. Вивчення вартості захаращення. Дізнатися про методи відображення даних часу.
Susan Williams
Курси: Data Visualization
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Тренер був ентузіазм.
Diane Lucas
Курси: Data Visualization
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Мені дуже сподобався контент/інструктор.
Craig Roberson
Курси: Data Visualization
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Я практичний учень, і це було те, що він зробив багато.
Lisa Comfort
Курси: Data Visualization
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Мені сподобалися приклади.
Peter Coleman
Курси: Data Visualization
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Мені сподобалися приклади.
Peter Coleman
Курси: Data Visualization
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Мені подобалося хороший реальний прикладів світу, огляди існуючих доповідей.
Ronald Parrish
Курси: Data Visualization
Machine Translated
Мені подобається, як Біроль підганяв деякі частини навчання, щоб задовольнити мою роль Business аналітика. Таким чином він зміг зосередити увагу на важливих темах, а не зупинятися на темах, які я взагалі не буду використовувати у своїй роботі. Він викладає дуже чітко і забезпечує розуміння теми, перш ніж перейти до наступної. Можна сказати, що він досвідчений професор і добре обізнаний зі своєю темою, і вміє аналізувати потреби в навчанні студента, щоб ефективно висвітлювати важливі та актуальні теми. Це робить його сеанси дуже ефективними, оскільки є гнучкими і правильно націленими. Його матеріали дуже вичерпні, і він робить сенс слідувати навчальній програмі, але робить її більш важливою для студента. Я ціную, що він знає галузеві стандарти і здатний дати свою думку про те, що працює. Я ніколи не навчився стільки, скільки робив на 2-денному курсі, і це варто всього часу та зусиль. Навчання пришвидшило моє вивчення BPM N 2.0 і дало мені основи та впевненість, щоб почати практикувати техніку у своїй роботі. Дякую Birol та NobleProg за чудовий досвід навчання, я обов'язково порекомендую цей курс та тренера майбутнім колегам.
Irma Irosido
Курси: BPMN 2.0 for Business Analysts
Machine Translated
Я дійсно була вигода від готовності тренера поділитися більше.
Balaram Chandra Paul
Курси: A practical introduction to Data Analysis and Big Data
Machine Translated
Дуже хороший підхід до запам'ятовування/повторення ключових тем. Дуже приємний «розминка» вправи.
Курси: Python Programming
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* Приємні вправи. * Швидко переїхав в більш просунуті теми. * Тренер був дружнім і легко отримати на с. * Індивідуальний курс на потреби команди.
Matthew Lucas
Курси: Python Programming
Machine Translated
Я дійсно був вигоду від інтерактивності і присвячений тренера.
Pierre Bruwier
Курси: BPMN 2 Fundamentals and Workshop
Machine Translated
Тренер має багато анекдотів і може показати зміст простого життя
Katarzyna Gołda
Курси: Business Analysis
Machine Translated
Пан Богдан є великим фахівцем і вміє передавати знання доступним способом. Розуміє і зазначає, коли аудиторія "вимкнено".
Unit4 Polska sp. z o.o; ALTEN Polska Sp. z o.o.; UBS; Zerochaos Sp. z o.o.
Курси: Business Analysis
Machine Translated
Дуже хороший підхід до запам'ятовування/повторення ключових тем. Дуже приємний «розминка» вправи.
Курси: Python Programming
Machine Translated
Data Analysis Зміст курсу
Назва курсу
Тривалість
Огляд
Назва курсу
Тривалість
Огляд
21 годин
Огляд
This course has been created for business analysts who want to use BPMN 2.0 extensively in their projects.
It focuses on practical aspects of all BPMN 2.0 specification as well as implementations of common patterns.
It is a series of short lectures followed by exercises: the delegates will have a problem described in English, and will have to create a proper diagram for each problem. After that, the diagrams will be discussed and assessed by the group and the trainer.
This course doesn't cover execution part of BPMN, it focuses on analysis and process design aspects of BPMN 2.0.
It focuses on practical aspects of all BPMN 2.0 specification as well as implementations of common patterns.
It is a series of short lectures followed by exercises: the delegates will have a problem described in English, and will have to create a proper diagram for each problem. After that, the diagrams will be discussed and assessed by the group and the trainer.
This course doesn't cover execution part of BPMN, it focuses on analysis and process design aspects of BPMN 2.0.
14 годин
Огляд
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.
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.
28 годин
Огляд
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.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
21 годин
Огляд
[R](http://www.r-project.org/) 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
Developers / data analytics
Duration
3 days
Format
Lectures and Hands-on
7 годин
Огляд
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
14 годин
Огляд
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
21 годин
Огляд
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 годин
Огляд
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 годин
Огляд
Participants who complete this instructor-led, live training in україні 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 годин
Огляд
This instructor-led, live training in україні is aimed at software developers who wish to build search and analytics solutions using Elasticsearch.
The training starts with a discussion of the Elasticsearch architecture, including its distributed model and search API. This is followed by an explanation of Elasticsearch's functionality and how to best integrate it into an existing application.
Hands-on exercises make up an important part of the training, and give participants a chance to put into practice their knowledge while receiving feedback on their implementation and progress.
The training starts with a discussion of the Elasticsearch architecture, including its distributed model and search API. This is followed by an explanation of Elasticsearch's functionality and how to best integrate it into an existing application.
Hands-on exercises make up an important part of the training, and give participants a chance to put into practice their knowledge while receiving feedback on their implementation and progress.
35 годин
Огляд
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 годин
Огляд
In this instructor-led, live training in україні, 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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
In this instructor-led, live training in україні (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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
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 годин
Огляд
This instructor-led, live training in україні (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 годин
Огляд
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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