Helyi, oktatott élő adatelemzés (Adat- vagy adatelemzési elemzés) tanfolyamok demonstrálják az elemzéshez és a kéziszámítógépes gyakorlathoz az analízishez használt programozási nyelveket és módszereket Az adatelemzés képzése "helyszíni élő képzés" vagy "távoli élő képzés" formájában érhető el A helyszíni élő képzés helyi szinten valósulhat meg az ügyfél telephelyén Magyarország vagy a NobleProg vállalati oktatóközpontjaiban Magyarország A távoli élőképzés interaktív, távoli asztal segítségével történik NobleProg Az Ön Helyi Képzési Szolgáltatója.
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Vélemények
A gyakornok kapcsolatfelvételi módszere. A megközelítés jó mind a csoport, mind az egyén számára. Marci
Marcin Szcześniak - Credit Agricole Bank P
Kurzus: BPMN 2.0 dla Analityków Biznesowych
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Nagy mennyiségű testmozgás.
Piotr Kubiński
Kurzus: BPMN 2.0 dla Analityków Biznesowych
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Inkább a testmozgást és a Python zümmögéseinek és áttöréseinek megismerését részesítettem előnyben
Connor Brierley-Green
Kurzus: Python Programming
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Joey fertőző lelkesedéssel rendelkezik a programozással kapcsolatban. És nagyon jól tudta igazítani igényeinket és érdekeinket.
Randy Enkin
Kurzus: Python Programming
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Számos példa könnyű megérteni.
Lingmin Cao
Kurzus: Python Programming
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A vizsgakérdéseket szimuláló záróvizsga, ahol csoportonként próbáltunk feladatokat megoldani a képzés során szerzett ismeretek alapján. Ł
Dominik Łubiński
Kurzus: Business Analysis, BABOK V3.0 and IIBA Certification Preparation
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Leginkább a Trainer tudását szerettem.
Sripal S
Kurzus: Understanding Business Process Modeling with BPMN 2.0
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Általában élveztem a beszédet.
Bhaskar naidu
Kurzus: Understanding Business Process Modeling with BPMN 2.0
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A kérdésekre vonatkozó útmutató átfogó ismerete mindenre kiterjedő választ adott a várakozásaimnak ... Az előadó nagy vitákat folytat ... Nem hiányzik a türelme ... Matul
Łukasz Matulewicz
Kurzus: Programowanie w języku Python
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A tanár nagy tudása, az eszközök sokfélesége és a téma gyakorlati megközelítése Magdale
Magdalena Stupak
Kurzus: Programowanie w języku Python
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a tréner nagyszerű ismerete, a fordítás
Renata Cylejowska
Kurzus: Programowanie w języku Python
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Tény, hogy a testreszabást komolyan vették.
jurgen linsen
Kurzus: Python Programming
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Nagyon élveztem a tréner segítségét.
Urszula Kuza
Kurzus: Tableau Advanced
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minden
Grześ
Kurzus: BPMN 2.0 dla Analityków Biznesowych
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Gyakorlati példák.
Przemysław Domański
Kurzus: BPMN 2.0 dla Analityków Biznesowych
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Minden kérdésemre választ kapok.
Natalia Gladii
Kurzus: Data Analytics With R
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Jó áttekintést kaptunk a folyamat-szerkesztőkről, az üzleti szabályokról és a BPM S eszközökről. Nagyon ajánlom a gyakorlati BPM N tanfolyamot Business elemzők számára, még a tapasztalt elemzők számára is, kiváló folyamatforgatókönyvekkel és modellezési gyakorlattal a kivágást a többi fölé helyezve!
Anees Baig
Kurzus: BPMN 2.0 for Business Analysts
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Nagyon informatív, és szép áttekintést adott a kurzusról.
Matthew Steptoe
Kurzus: Contemporary Development Principles and Practices
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Élveztem az 1. napon.
Peter Mahaffey
Kurzus: Contemporary Development Principles and Practices
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Az a tény, hogy megoldásokat találtunk az adott problémákra + + széleskörű alkalmazásokat mutattunk be + egy ismeretlevelet kapott. Wojci
Wojciech Michalak
Kurzus: Power BI
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Tetszett a gyakorlatok, mivel ez az egyetlen módja annak, hogy megismételjem, ismétléssel.
David Rushe
Kurzus: Tableau Advanced
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Az edző nagyon széles tudással rendelkezik, tudomására hozza. Ez magában foglalja a résztvevőket a közösen tapasztalt problémák kölcsönhatására és megoldására. - U
Nina Rokita
Kurzus: Power BI
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A tréner állandó kapcsolatban állt a képzésben részesültekkel, kiigazította az igényeinkhez igazodó lépést, és folyamatosan megoldotta problémáinkat, és megválaszolta kérdéseinket Jaworsk
Dominika Jaworska
Kurzus: Power BI
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Élveztem a gyakorlatokat, a tréninget, a teakonyhát.
Alisher Khaydarov
Kurzus: BPMN 2 Fundamentals and Workshop
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A tempó és a progresszió tökéletes volt ahhoz, hogy gyorsan megértsük a dolgokat.
Stephen Brewell
Kurzus: BPMN 2.0 for Business Analysts
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A megadott példák valóban segítenek nekem néhány fogalom megértésében. A tréner megbizonyosodott arról, hogy az egész edzést követjük, és nem habozott visszatérni egy korábbi pontra, hogy kétség merült fel. A tréner a képzést a szükségleteinkhez igazította, és a részletekre összpontosította a részleteket. A gyakorlati gyakorlatok is jóak voltak.
Amanda Moscardini - Campinas Valley
Kurzus: Understanding Business Process Modeling with BPMN 2.0
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Debra megértette a kérdéseimet, és pontosan megválaszolta őket. A javaslatok arra vonatkozólag, hogy hogyan lehet diagramokat tervezni bizonyos körülmények között, nagyon jó volt. Szeretném megerősíteni, hogy a profi és hatékony Debra velünk volt.
Leandro Antonio
Kurzus: Understanding Business Process Modeling with BPMN 2.0
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A képzés nagyon következetes volt, a program számos érdekes funkcióját ismerem. Ann
Anna Klimczak
Kurzus: Power BI
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Jól felkészült, gyakorolt gyakorlatok és a tanár tudása
Sandra Przewdzing
Kurzus: Power BI
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Szeretem a gyakorlatokat.
Nour Assaf
Kurzus: Data Mining and Analysis
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A gyakorlati edzés és a tréner képessége egyszerűsített összetett témák megmagyarázására.
youssef chamoun
Kurzus: Data Mining and Analysis
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Az adott információ érdekes volt, és a legjobb rész a végén volt, amikor a Durex adatokkal rendelkeztünk, és olyan adatokkal dolgoztunk, amelyekről ismeretesek és műveletek végeztek, hogy eredményeket érjenek el.
Jessica Chaar
Kurzus: Data Mining and Analysis
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Tetszett a segítőkész és nagyon kedves.
Natalia Machrowicz
Kurzus: Python Programming
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Gyakorlati gyakorlatokat végeztünk (az általunk írt szkripteket mindennapi munkánkban felhasználhatjuk). Nagyon érdekes volt a tanfolyam. Szerettem úgy is, ahogy az edző megosztotta tudását. Nagyon könnyen elérhető módon csinálta.
Malwina Sawa
Kurzus: Python Programming
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Úgy gondoltam, hogy az információ érdekes.
Allison May
Kurzus: Data Visualization
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Nagyra értékelem, hogy Jeff olyan adatokat és példákat használt fel, amelyek az oktatási adatokra vonatkoztak. Érdekes és interaktívvá tette.
Carol Wells Bazzichi
Kurzus: Data Visualization
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Tanulni az összes grafikon típusról és arról, hogy mire használják őket. Tanulás a zűrzavar értékéről. Tanulni az időadatok megjelenítési módszereiről.
Susan Williams
Kurzus: Data Visualization
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A tréner lelkes volt.
Diane Lucas
Kurzus: Data Visualization
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Nagyon tetszett a tartalom / oktató.
Craig Roberson
Kurzus: Data Visualization
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Praktikus tanuló vagyok, és ezt sok mindent megtett.
Lisa Comfort
Kurzus: Data Visualization
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Tetszett a példák.
Peter Coleman
Kurzus: Data Visualization
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Általában hasznot húzott a példák.
Peter Coleman
Kurzus: Data Visualization
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Élveztem a jó valós példákat, a meglévő jelentések áttekintését.
Ronald Parrish
Kurzus: Data Visualization
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Tetszik az, ahogyan Birol testreszabta a képzés egyes részeit, hogy megfeleljen az Business elemzői szerepemnek. Ily módon a fontos témákra tudott összpontosítani, és nem foglalkozott olyan témákkal, amelyeket általában nem fogok használni a munkámban. Nagyon egyértelműen tanít, és gondoskodik arról, hogy megértse a témát, mielőtt a következőre menne. Azt mondhatja, hogy tapasztalt tanár és nagyon jól ismeri a témáját, és tudja, hogyan elemezheti a hallgató képzési igényeit, hogy hatékonyan lefedje az alapvető és releváns témákat. Ez rendkívül hatékonnyá teszi az üléseit, mivel rugalmas és célhoz igazodik. Anyagai nagyon átfogóak, és rámutat arra, hogy kövesse a tantervet, de ez relevánsabbá teszi a hallgatót. Nagyra értékelem, hogy ismeri az ipari szabványokat és be tudja mutatni, mi működik. Soha nem tanultak annyit, mint egy 2 napos tanfolyamon, és megéri minden időt és erőfeszítést. A képzés meggyorsította a BPM N 2.0 tanulását, és alapokat és magabiztosságot adott a munkám során a technika gyakorlásának megkezdéséhez. Köszönöm Birolnak és a NobleProg-nak a csodálatos tanulási élményt. Biztosan ajánlom a kurzust és az oktatót a jövőbeli kollégáknak.
Irma Irosido
Kurzus: BPMN 2.0 for Business Analysts
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Nagyon hasznos voltam az edző hajlandósága, hogy többet tudjak megosztani.
Balaram Chandra Paul
Kurzus: A practical introduction to Data Analysis and Big Data
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Nagyon jó megközelítés a legfontosabb témák memorizálására / megismételésére. Nagyon szép "bemelegítő" gyakorlatok.
Kurzus: Python Programming
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* Élvezetes gyakorlatok. * Gyorsan bekerült a fejlettebb témákba. * A tréner barátságos és könnyen kezelhető. * Testre szabott kurzus a csapat igényeihez.
Matthew Lucas
Kurzus: Python Programming
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Nagyon hasznot húzott az interaktivitás és az elkötelezett edző.
Pierre Bruwier
Kurzus: BPMN 2 Fundamentals and Workshop
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Az edzőnek sok anekdotája van, és tartalma egyszerű életelőadásokról szól Katar
Katarzyna Gołda
Kurzus: Business Analysis
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Mr. Bogdan egy nagy szakértő, és tudja, hogyan lehet átadni tudást hozzáférhető módon. Megérti és észreveszi, amikor a közönség "kikapcsol". Anonymous -
Unit4 Polska sp. z o.o; ALTEN Polska Sp. z o.o.; UBS; Zerochaos Sp. z o.o.
Kurzus: Business Analysis
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Nagyon jó megközelítés a legfontosabb témák memorizálására / megismételésére. Nagyon szép "bemelegítő" gyakorlatok.
Kurzus: Python Programming
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Data Analysis Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
21 hours
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
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
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
[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
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
21 hours
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
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
Participants who complete this instructor-led, live training in Magyarország 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
This instructor-led, live training in Magyarország 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
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.
42 hours
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
This instructor-led, live training in Magyarország (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
This instructor-led, live training (online or onsite) is aimed at HR professionals who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
14 hours
This instructor-led, live training (online or onsite) is aimed at HR professionals and recruitment specialists who wish to use analytical methods improve organisational performance. This course covers qualitative as well as quantitative, empirical and statistical approaches.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
35 hours
Data analysis refers to the process of obtaining, cleaning, and analyzing data, often times in a visual manner, to derive insights for better decision making. More and more data is being stored in databases in the cloud so it is important to have a strategy for understanding, managing and accessing such data. One popular tool for accessing and visualizing data is Power Bi.
This instructor-led, live training (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.
By the end of this training, participants will be able to:
- Install and configure Power BI.
- Evaluate the various data solutions offered by cloud providers such as Azure.
- Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
- Apply tools and techniques to clean data in preparation for analysis.
- Build reporting and analytics solutions based on on-premise as well as cloud data.
- Integrate data analytics solutions with a data warehouse.
- Mitigate data security risks and ensure data privacy.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
This instructor-led, live training (online or onsite) is aimed at technical persons who wish to gain a practical understanding of available cloud solutions, the data analysis processes needed to work with data in the cloud, and the hands-on practice to apply tools such as Power BI to analyze data.
By the end of this training, participants will be able to:
- Install and configure Power BI.
- Evaluate the various data solutions offered by cloud providers such as Azure.
- Gain an understanding of the different structures, modeling approaches, and data warehouse designs used to store, manage and access Big Data.
- Apply tools and techniques to clean data in preparation for analysis.
- Build reporting and analytics solutions based on on-premise as well as cloud data.
- Integrate data analytics solutions with a data warehouse.
- Mitigate data security risks and ensure data privacy.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
14 hours
This instructor-led, live training in Magyarország (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
By the end of this training, participants will be able to:
- Install and configure Cloud Pak for Data.
- Unify the collection, organization and analysis of data.
- Integrate Cloud Pak for Data with a variety of services to solve common business problems.
- Implement workflows for collaborating with team members on the development of an AI solution.
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