Programele de instruire locală, instrucționate live (analiză date sau date analitice) demonstrează prin discuții și practică limbile de programare și metodologiile folosite pentru a efectua analiza datelor Analiza datelor este disponibilă ca "formare live la fața locului" sau "instruire live la distanță" Training-ul live la fața locului poate fi efectuat la fața locului la sediul clientului România sau în centrele de formare corporativa NobleProg din România Instruirea live la distanță este realizată printr-un desktop interactiv, la distanță NobleProg Furnizorul dvs de formare locală.
Machine Translated
Mărturii
Metoda de contact a stagiarului. Abordarea este bună atât pentru grup cât și pentru individ.
Marcin Szcześniak - Credit Agricole Bank P
Curs: BPMN 2.0 dla Analityków Biznesowych
Machine Translated
O doză mare de exerciții fizice.
Piotr Kubiński
Curs: BPMN 2.0 dla Analityków Biznesowych
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Am preferat exercițiul și învățarea despre unghiurile și curățile lui Python
Connor Brierley-Green
Curs: Python Programming
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Joey are un entuziasm infecțios în ceea ce privește programarea. Și el a fost foarte bun în adaptarea la nevoile și interesele noastre în zbor.
Randy Enkin
Curs: Python Programming
Machine Translated
Multe exemple mă făceau ușor de înțeles.
Lingmin Cao
Curs: Python Programming
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Testul final care simulează întrebările la examen, unde am încercat ca grup să rezolvăm sarcini pe baza cunoștințelor dobândite în timpul formării.
Dominik Łubiński
Curs: Business Analysis, BABOK V3.0 and IIBA Certification Preparation
Machine Translated
Mi-a plăcut mai mult cunoștințele Trainerului.
Sripal S
Curs: Understanding Business Process Modeling with BPMN 2.0
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M-am bucurat, în general, de modul de a vorbi.
Bhaskar naidu
Curs: Understanding Business Process Modeling with BPMN 2.0
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Cunoașterea cuprinzătoare a ghidului pentru toate întrebările noastre a dat răspunsuri copleșitoare așteptărilor mele ... Lectorul face mari discuții ... Nu-i lipsit răbdarea ...
Łukasz Matulewicz
Curs: Programowanie w języku Python
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Cunoașterea lectorului, diversitatea instrumentelor și abordarea practică a subiectului
Magdalena Stupak
Curs: Programowanie w języku Python
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cunostinte foarte bune ale trainerului, cum sa traducem
Renata Cylejowska
Curs: Programowanie w języku Python
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Faptul că personalizarea a fost luată în serios.
jurgen linsen
Curs: Python Programming
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M-am bucurat cu adevărat de ajutorul oferit de antrenor.
Urszula Kuza
Curs: Tableau Advanced
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toate
Grześ
Curs: BPMN 2.0 dla Analityków Biznesowych
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Exemple practice.
Przemysław Domański
Curs: BPMN 2.0 dla Analityków Biznesowych
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Am răspunsuri la toate întrebările mele.
Natalia Gladii
Curs: Data Analytics With R
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Am obținut o imagine de ansamblu bună asupra editorilor de procese, a regulilor de afaceri și a peisajului instrumentelor BPM S. Recomand cu mare drag cursul practic BPM N pentru analistii de Business , chiar și pentru analiștii cu experiență, cu scenariile sale excelente de proces și practica de modelare, vă va poziționa cu o reducere peste restul!
Anees Baig
Curs: BPMN 2.0 for Business Analysts
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Foarte informativ și a dat un sumar frumos general al schiță curs.
Matthew Steptoe
Curs: Contemporary Development Principles and Practices
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Mi-a plăcut toată ziua 1.
Peter Mahaffey
Curs: Contemporary Development Principles and Practices
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Faptul că am pregătit soluții pentru probleme date + arătând o gamă largă de aplicații + a trecut printr-un compendiu de cunoștințe. Wojcie
Wojciech Michalak
Curs: Power BI
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Mi-au plăcut exercițiile deoarece este singura modalitate de a învăța, prin repetare.
David Rushe
Curs: Tableau Advanced
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Formatorul are o cunoaștere foarte largă, el o poate transmite. Aceasta implică participanții să interacționeze și să rezolve problemele întâlnite împreună. Rok
Nina Rokita
Curs: Power BI
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Formatorul a fost în contact permanent cu destinatarii formării, a adaptat ritmul la nevoile noastre și a rezolvat problemele noastre în mod continuu și a răspuns la întrebările noastre Domi
Dominika Jaworska
Curs: Power BI
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Mi-a placut exercitiile, sala de training, bucatarie de ceai.
Alisher Khaydarov
Curs: BPMN 2 Fundamentals and Workshop
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Ritmul și progresul au fost perfecte pentru a înțelege cu adevărat lucrurile repede.
Stephen Brewell
Curs: BPMN 2.0 for Business Analysts
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Exemplele oferite chiar m-au ajutat să înțeleg unele concepte. Formatorul a asigurat că am urmat întregul antrenament și nu am ezitat să ne întoarcem la un punct anterior, în care existau îndoieli. Formatorul a adaptat formarea la nevoile noastre, concentrându-ne pe elementele pe care dorim să le prezentăm mai multe detalii. Exerciții practice au fost de asemenea bune.
Amanda Moscardini - Campinas Valley
Curs: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
Debra sa asigurat că mi-a înțeles întrebările și le-a răspuns precis. Sugestiile cu privire la modul de proiectare a diagramelor în anumite condiții au fost foarte bune. Aș dori să mă întăresc cu cât Debra a fost profesionist și eficient cu noi.
Leandro Antonio
Curs: Understanding Business Process Modeling with BPMN 2.0
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Formarea a fost foarte consistentă, am învățat despre multe funcționalități interesante ale programului. Ann
Anna Klimczak
Curs: Power BI
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Exerciții bine pregătite, practicate și cunoștințe ale profesorului S
Sandra Przewdzing
Curs: Power BI
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Îmi plac exercițiile făcute.
Nour Assaf
Curs: Data Mining and Analysis
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Exercițiul hands-on și capacitatea formatorului de a explica subiecte complexe în termeni simpli.
youssef chamoun
Curs: Data Mining and Analysis
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Informațiile furnizate au fost interesante și cea mai bună parte a fost spre sfârșitul anului când am fost furnizați date de la Durex și am lucrat la datele pe care le cunoaștem și le efectuăm pentru a obține rezultate.
Jessica Chaar
Curs: Data Mining and Analysis
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Mi-a plăcut util și foarte bun.
Natalia Machrowicz
Curs: Python Programming
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Am făcut exerciții practice (scenariile pe care le-am scris pot fi folosite în munca noastră zilnică). A făcut cursul foarte interesant. Mi-a plăcut, de asemenea, modul în care instructorul și-a împărtășit cunoștințele. A făcut-o într-un mod foarte accesibil.
Malwina Sawa
Curs: Python Programming
Machine Translated
Am crezut că informațiile erau interesante.
Allison May
Curs: Data Visualization
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Apreciez foarte mult faptul că Jeff a folosit date și exemple care erau aplicabile datelor educaționale. El a făcut-o interesantă și interactivă.
Carol Wells Bazzichi
Curs: Data Visualization
Machine Translated
Învățați despre toate tipurile de diagrame și despre ce sunt folosite. Învățând valoarea aglomerării. Învățați despre metodele de afișare a datelor de timp.
Susan Williams
Curs: Data Visualization
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Antrenorul a fost entuziast.
Diane Lucas
Curs: Data Visualization
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Mi-a plăcut cu adevărat conținutul / instructor.
Craig Roberson
Curs: Data Visualization
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Sunt un cursant și acest lucru a făcut foarte mult.
Lisa Comfort
Curs: Data Visualization
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Mi-au plăcut exemplele.
Peter Coleman
Curs: Data Visualization
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În general, am beneficiat de exemple.
Peter Coleman
Curs: Data Visualization
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Mi-a plăcut exemplele bune din lumea reală, recenzii ale rapoartelor existente.
Ronald Parrish
Curs: Data Visualization
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Îmi place modul în care Birol a personalizat anumite părți ale instruirii pentru a răspunde rolului meu de Business Analyst. În acest fel, el a fost capabil să se concentreze pe subiecte care sunt importante și nu se bazează pe subiecte pe care eu, în general, nu le voi folosi în munca mea. El învață foarte clar și se asigură că înțelegeți un subiect înainte de a trece la următorul. Puteți spune că este un profesor experimentat și foarte cunoscător pe subiectul său și știe să analizeze nevoile de pregătire ale studentului pentru a acoperi eficient subiecte esențiale și relevante. Acest lucru face sesiunile sale foarte eficiente, deoarece este flexibil și corect pe țintă. Materialele sale sunt foarte cuprinzătoare și își propune să urmeze programa, făcându-l mai relevant pentru student. Apreciez că cunoaște standardele industriei și este capabil să-și dea cunoștințe despre ceea ce funcționează. Nu am învățat niciodată la fel de mult ca la un curs de 2 zile și merită tot timpul și efortul. Pregătirea mi-a accelerat învățarea BPM N 2.0 și mi-a oferit elementele de bază și încrederea pentru a începe să practic tehnica în munca mea. Vă mulțumim Birol și NobleProg pentru o experiență de învățare minunată, cu siguranță vă recomand cursul și formatorul viitorilor colegi.
Irma Irosido
Curs: BPMN 2.0 for Business Analysts
Machine Translated
Chiar am beneficiat de disponibilitatea formatorului de a împărți mai mult.
Balaram Chandra Paul
Curs: A practical introduction to Data Analysis and Big Data
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Abordare foarte bună pentru memorarea / repetarea subiectelor cheie. Foarte frumoase exerciții de "warm-up".
Curs: Python Programming
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* Exerciții plăcute. * Sa mutat rapid în subiecte mai avansate. * Formatorul a fost prietenos și ușor de înțeles. * Curs personalizat pentru nevoile echipei.
Matthew Lucas
Curs: Python Programming
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Chiar am beneficiat de interactivitate și instructor dedicat.
Pierre Bruwier
Curs: BPMN 2 Fundamentals and Workshop
Machine Translated
Antrenorul are o mulțime de anecdote și poate afișa conținut pe cursuri simple de viață Ka
Katarzyna Gołda
Curs: Business Analysis
Machine Translated
Bogdan este un mare specialist și știe cum să treacă cunoștințele într-un mod accesibil. El înțelege și observă când publicul "se oprește". Anonymous
Unit4 Polska sp. z o.o; ALTEN Polska Sp. z o.o.; UBS; Zerochaos Sp. z o.o.
Curs: Business Analysis
Machine Translated
Abordare foarte bună pentru memorarea / repetarea subiectelor cheie. Foarte frumoase exerciții de "warm-up".
Curs: Python Programming
Machine Translated
Data Analysis Course Outlines
Numele cursului
Durata
Sinoptic
Numele cursului
Durata
Sinoptic
21 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
[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 hours
Sinoptic
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
14 hours
Sinoptic
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
21 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
Participants who complete this instructor-led, live training in România 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 hours
Sinoptic
This instructor-led, live training in România 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 hours
Sinoptic
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 hours
Sinoptic
In this instructor-led, live training in România, 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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
In this instructor-led, live training in România (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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
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 hours
Sinoptic
This instructor-led, live training in România (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 hours
Sinoptic
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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