オンラインまたはオンサイトのインストラクター主導のライブデータ分析トレーニングコースでは、データ分析の実行に使用されるプログラミング言語と方法論について、ディスカッションと実践的な演習を通じて実演します。
データ分析トレーニングは、「オンラインライブトレーニング」または「オンサイトライブトレーニング」として利用できます。オンラインライブトレーニング（別名「リモートライブトレーニング」）は、インタラクティブなリモートデスクトップで行われます。現地でのライブトレーニングは、日本のお客様のオフィスまたは日本のNobleProg提携の企業トレーニングセンターにて実施が可能です。
NobleProg - 現地のトレーニングプロバイダー
お客様の声
連絡方法の指導 - 訓練を受けた。このアプローチは、グループと個人の両方に適しています。
Marcin Szcześniak - Credit Agricole Bank P
コース: BPMN 2.0 dla Analityków Biznesowych
Machine Translated
świczenia
Piotr Kubiński
コース: BPMN 2.0 dla Analityków Biznesowych
Machine Translated
私は演習を好み、 Python隅々について学びました
Connor Brierley-Green
コース: Python Programming
Machine Translated
ジョーイはプログラミングに関する熱狂的な熱意を持っています。そして、彼は、私たちのニーズと興味をその場で適応させることに非常に恵まれました。
Randy Enkin
コース: Python Programming
Machine Translated
多くの例がわかりやすくなりました。
Lingmin Cao
コース: Python Programming
Machine Translated
試験問題をシミュレートした最終テスト。ここでは、トレーニング中に得た知識に基づいてグループとしてタスクを解決することを試みました。
Dominik Łubiński
コース: Business Analysis, BABOK V3.0 and IIBA Certification Preparation
Machine Translated
私は主にトレーナーの知識を気に入っていました。
Sripal S
コース: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
私は一般的にスピーチを提供する方法を楽しんだ。
Bhaskar naidu
コース: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
オペレータの総合的な知識 - 私たちの質問のすべてが私の期待を覆す答えを与えました...講師は偉大な議論を行います...彼は忍耐に欠けていません...
Łukasz Matulewicz
コース: Programowanie w języku Python
Machine Translated
講師に関する豊富な知識、ツールの多様性、およびトピックへの実践的なアプローチ
Magdalena Stupak
コース: Programowanie w języku Python
Machine Translated
トレーナーの優れた知識、翻訳方法
Renata Cylejowska
コース: Programowanie w języku Python
Machine Translated
カスタマイズが真剣に行われたという事実。
jurgen linsen
コース: Python Programming
Machine Translated
私は真剣にトレーナーの助けを楽しんだ。
Urszula Kuza
コース: Tableau Advanced
Machine Translated
全て
Grześ
コース: BPMN 2.0 dla Analityków Biznesowych
Machine Translated
実用的な例
Przemysław Domański
コース: BPMN 2.0 dla Analityków Biznesowych
Machine Translated
私はすべての質問に答えています。
Natalia Gladii
コース: Data Analytics With R
Machine Translated
プロセスエディター、ビジネスルール、およびBPM Sツールの全体像の概要を把握しました。 Businessアナリスト向けの実践的なBPM Nコースを強くお勧めします。経験豊富なアナリストであっても、その優れたプロセスシナリオとモデリングの実践により、他のコースよりも優れています。
Anees Baig
コース: BPMN 2.0 for Business Analysts
Machine Translated
非常に有益で、コースのアウトラインの全体的な要約が素敵でした。
Matthew Steptoe
コース: Contemporary Development Principles and Practices
Machine Translated
私は1日目のすべてを楽しんだ。
Peter Mahaffey
コース: Contemporary Development Principles and Practices
Machine Translated
与えられた問題に対する解決策をすぐに手に入れたという事実+幅広いアプリケーションを示した+という事実は、知識の大要に合格しました。
Wojciech Michalak
コース: Power BI
Machine Translated
私は練習が練習を好きだったのは、それが練習を繰り返すための唯一の方法だからです。
David Rushe
コース: Tableau Advanced
Machine Translated
トレーナーは非常に幅広い知識を持っています、彼はそれを伝えることができます。参加者は、一緒に遭遇した問題と相互作用して解決することを含みます。
Nina Rokita
コース: Power BI
Machine Translated
トレーナーは常にトレーニングを受ける側と連絡を取り合い、ニーズに合わせてペースを調整し、継続的に問題を解決し、質問に答えました。
Dominika Jaworska
コース: Power BI
Machine Translated
私は練習、訓練室、ティーキッチンを楽しんだ。
Alisher Khaydarov
コース: BPMN 2 Fundamentals and Workshop
Machine Translated
ペースとプログレッションは本当に物事をすばやく理解するのに完璧でした。
Stephen Brewell
コース: BPMN 2.0 for Business Analysts
Machine Translated
与えられた例は、私が実際にいくつかの概念を理解するのを助けました。トレーナーは、私たちがトレーニング全体に従っていることを確認し、前のポイントに戻って躊躇しなかったことは間違いありませんでした。トレーナーは私たちのニーズに合わせてトレーニングを取り入れ、詳細を知りたい項目に焦点を当てました。実践的なエクササイズも良かった。
Amanda Moscardini - Campinas Valley
コース: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
デブラは私の質問を理解し、正確に答えました。特定の条件下でのダイアグラムの設計方法に関する提案は本当に良好でした。私はプロと効率的なデブラがどのように私たちと共にいたのかを強調したいと思います。
Leandro Antonio
コース: Understanding Business Process Modeling with BPMN 2.0
Machine Translated
トレーニングは非常に一貫していた、私はプログラムの多くの興味深い機能について学びました。
Anna Klimczak
コース: Power BI
Machine Translated
よく準備され練習された練習と教師の知識
Sandra Przewdzing
コース: Power BI
Machine Translated
私は行なわれた練習が好きです。
Nour Assaf
コース: Data Mining and Analysis
Machine Translated
実践的なエクササイズとトレーナーは、複雑なトピックを簡単に説明することができます。
youssef chamoun
コース: Data Mining and Analysis
Machine Translated
提供された情報は面白かったし、デュレックスのデータが提供され、私たちがよく知っているデータを処理し、結果を得るための操作を実行したときに、最も重要な部分が終わりに向かっていた。
Jessica Chaar
コース: Data Mining and Analysis
Machine Translated
私は助けてとても親切だった。
Natalia Machrowicz
コース: Python Programming
Machine Translated
私たちは実践的な演習を行いました（私たちが書いたスクリプトは日々の仕事に使用できます）。コースをとても面白くしました。私はトレーナーが自分の知識を共有する方法も好きでした。彼は非常にアクセス可能な方法でそれをやった。
Malwina Sawa
コース: Python Programming
Machine Translated
私はその情報が面白かったと思った。
Allison May
コース: Data Visualization
Machine Translated
Jeffは教育データに適用できるデータと例を利用してくれて本当に感謝しています。彼はそれを面白くインタラクティブにしました。
Carol Wells Bazzichi
コース: Data Visualization
Machine Translated
すべてのグラフの種類とそのグラフの種類について学習します。混乱の価値を学ぶ。時間データを表示する方法について学習する。
Susan Williams
コース: Data Visualization
Machine Translated
トレーナーは熱狂的でした。
Diane Lucas
コース: Data Visualization
Machine Translated
私はコンテンツ/インストラクターが本当に好きだった。
Craig Roberson
コース: Data Visualization
Machine Translated
私は実践的な学習者であり、これは彼がたくさん行ったことでした。
Lisa Comfort
コース: Data Visualization
Machine Translated
私は例を気に入った。
Peter Coleman
コース: Data Visualization
Machine Translated
私は一般的に例の恩恵を受けていました。
Peter Coleman
コース: Data Visualization
Machine Translated
私は良い現実世界の例、既存のレポートのレビューを楽しんだ。
Ronald Parrish
コース: Data Visualization
Machine Translated
私はBirolがトレーニングの一部をカスタマイズして、 Businessアナリストとしての役割に対応する方法が気に入っています。このようにして、彼は重要なトピックに焦点を当てることができ、私の仕事では一般的に使用しないトピックにこだわることはできませんでした。彼は非常に明確に教えており、次のトピックに進む前にトピックを理解できるようにします。彼はベテランの教授であり、彼の主題について非常に精通しており、学生のトレーニングニーズを分析して、不可欠で関連性のあるトピックを効果的にカバーする方法を知っていると言えます。これにより、柔軟で的確なセッションが実現し、セッションが非常に効果的になります。彼の教材は非常に包括的であり、カリキュラムに従うことを強調しますが、生徒にとってより関連性の高いものにします。彼は業界標準を知っており、何が機能するかについて洞察を与えることができることを感謝しています。 2日間のコースで学んだほど多くのことを学んだことはなく、時間と労力を費やす価値があります。トレーニングはBPM N 2.0の学習を促進し、仕事でテクニックを実践するための基礎と自信を与えてくれました。 BirolとNobleProgにすばらしい学習体験をありがとう。今後の同僚にコースとトレーナーをお勧めします。
Irma Irosido
コース: BPMN 2.0 for Business Analysts
Machine Translated
私は本当にトレーナーのより多くを分かち合う意欲から恩恵を受けました。
Balaram Chandra Paul
コース: A practical introduction to Data Analysis and Big Data
Machine Translated
重要なトピックを記憶/再現するための非常に良いアプローチ。非常に良い "ウォームアップ"演習。
コース: Python Programming
Machine Translated
*楽しい練習。 *すばやくより高度なトピックに移動しました。 *トレーナーはフレンドリーで、乗りやすい*チームのニーズに合わせたカスタマイズコース。
Matthew Lucas
コース: Python Programming
Machine Translated
私はインタラクティブ性と献身的なトレーナーから本当に利益を得ました。
Pierre Bruwier
コース: BPMN 2 Fundamentals and Workshop
Machine Translated
コーチは多くの逸話を持っていて、簡単な人生の講義で内容を見せることができます
Katarzyna Gołda
コース: Business Analysis
Machine Translated
ボグダン氏は素晴らしいスペシャリストであり、知識を身近な方法で伝える方法を知っています。彼は聴衆が「消える」ことを理解して気付きます。
Unit4 Polska sp. z o.o; ALTEN Polska Sp. z o.o.; UBS; Zerochaos Sp. z o.o.
コース: Business Analysis
Machine Translated
重要なトピックを記憶/再現するための非常に良いアプローチ。非常に良い "ウォームアップ"演習。
コース: Python Programming
Machine Translated
データ分析コース概要
コース名
期間
概要
コース名
期間
概要
21 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
[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 時間
概要
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
14 時間
概要
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
21 時間
概要
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 時間
概要
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 時間
概要
Participants who complete this instructor-led, live training in 日本 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 時間
概要
This instructor-led, live training in 日本 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 時間
概要
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 時間
概要
In this instructor-led, live training in 日本, 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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
In this instructor-led, live training in 日本 (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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
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 時間
概要
This instructor-led, live training in 日本 (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 時間
概要
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.
今後のデータ分析コース
08-11-2021 10:00
21 時間
13-12-2021 10:00
28 時間
23-12-2021 10:00
14 時間
23-12-2021 10:00
14 時間
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週末データ分析コース, 夜のデータ分析トレーニング, データ分析ブートキャンプ, データ分析 インストラクターよる, 週末データ分析トレーニング, 夜のデータ分析コース, データ分析指導, データ分析インストラクター, データ分析レーナー, データ分析レーナーコース, データ分析クラス, データ分析オンサイト, データ分析プライベートコース, データ分析1対1のトレーニング
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