AWS Cloud9 for Data Scienceのトレーニングコース

This instructor-led, live training in 日本 (online or onsite) is aimed at data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.

By the end of this training, participants will be able to:

• Install and configure Anaconda components and libraries.
• Understand the core concepts, features, and benefits of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Use Conda, R, and Python packages for data science and machine learning.
• Get to know some practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in 日本 (onsite or remote) is aimed at engineers who wish to deploy and manage IoT devices on AWS.

By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led, live training in 日本 (online or onsite) is aimed at developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices.

By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

このインストラクター主導のライブトレーニング（オンサイトまたはリモート）は、開発者がAWS Lambdaを使用してクラウドにサービスやアプリケーションを構築およびデプロイする方法を学ぶことを目的としています。実行環境のプロビジョニング（サーバー、VM、コンテナ、可用性、スケーラビリティ、ストレージなど）について心配することなく、クラウドにサービスやアプリケーションを構築およびデプロイできます。

このトレーニングの終了時には、参加者は以下のことが Able to できるようになります：

• AWS Lambdaで関数を実行するように設定します。
• FaaS（Function as a Service）とサーバーレス開発の利点を理解します。
• AWS Lambda 関数を作成、アップロード、および実行します。
• Lambda関数を異なるイベントソースと統合します。
• Lambdaベースのアプリケーションをパッケージ化、デプロイ、監視、トラブルシューティングします。

今日の世界では、情報とデータの爆発的な増加は前例のないものです。イノベーションを推進し、可能な限りの境界を拡張する能力はかつてないほど急速に成長しています。データサイエンティストの役割は、現在産業界で最も需要が高まっているスキルの一つです。

当プログラムでは理論的な学習だけでなく、実践的で市場性のあるスキルを提供します。これは学術界と産業界の要求との間にあるギャップを埋めます。

この7週間のカリキュラムは、特定の業界要件に合わせてカスタマイズできます。詳細情報やお問い合わせについては、当社までご連絡くださいか、Nobleprog Institute のウェブサイトをご覧ください。

対象者:

このプログラムは大学院レベル以上の卒業者や、必要な前提条件のスキルを有する方々を対象としています。参加者は事前に評価と面接が行われます。

カリキュラム配布方法:

コースの配布は講師主導の教室での学習とオンラインでの学習の組み合わせとなります。通常、1週目は「教室主導」で、2〜6週目は「仮想教室」、7週目は再び「教室主導」となります。

This instructor-led, live training in 日本 (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

By the end of this training, participants will be able to:

• Set up and configure AWS Cloud9 for DevOps workflows.
• Implement continuous integration and continuous delivery (CI/CD) pipelines.
• Automate testing, monitoring, and deployment processes using AWS Cloud9.
• Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilize source control systems like GitHub or GitLab within AWS Cloud9.

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda.

By the end of this training, participants will be able to:

• Understand the fundamentals of serverless architecture.
• Set up AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications using AWS Lambda.
• Integrate AWS Lambda with other AWS services such as API Gateway and S3.
• Optimize serverless applications for performance and cost efficiency.

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

Summery:

• Basics of IoT architecture and functions
• “Things”, “Sensors”, Internet and the mapping between business functions of IoT
• Essential of all IoT software components- hardware, firmware, middleware, cloud and mobile app
• IoT functions- Fleet manager, Data visualization, SaaS based FM and DV, alert/alarm, sensor onboarding, “thing” onboarding, geo-fencing
• Basics of IoT device communication with cloud with MQTT.
• Connecting IoT devices to AWS with MQTT (AWS IoT Core).
• Connecting AWS IoT core with AWS Lambda function for computation and data storage.
• Connecting Raspberry PI with AWS IoT core and simple data communication.
• Alerts and events
• Sensor calibration

Summary:

• Basics of IoT architecture and functions
• “Things”, “Sensors”, Internet and the mapping between business functions of IoT
• Essential of all IoT software components- hardware, firmware, middleware, cloud and mobile app
• IoT functions- Fleet manager, Data visualization, SaaS based FM and DV, alert/alarm, sensor onboarding, “thing” onboarding, geo-fencing
• Basics of IoT device communication with cloud with MQTT.
• Connecting IoT devices to AWS with MQTT (AWS IoT Core).
• Connecting AWS IoT core with AWS Lambda function for computation and data storage using DynamoDB.
• Connecting Raspberry PI with AWS IoT core and simple data communication.
• Hands on with Raspberry PI and AWS IoT Core to build a smart device.
• Sensor data visualization and communication with web interface.

This instructor-led, live training in 日本 (online or onsite) introduces the idea of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea".  It walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem.

By the end of this training, participants will be able to:

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton.
• Create, share and organize Jupyter Notebooks with team members.
• Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

This instructor-led, live training in 日本 (online or onsite) is aimed at data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.

By the end of this training, participants will be able to:

• Learn about data science and machine learning.
• Explore data analytics.
• Learn about Kaggle and how it works.

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.

この講師主導のライブトレーニング（オンラインまたはオンサイト）は、Modinを使用して並列計算を構築および実装し、高速なデータ分析を行うことを目指すデータサイエンティストや開発者向けです。

このトレーニング終了時には、参加者は以下のことが Able to:

• 必要な環境を設定して、Modinを使用してスケールアウトするPandasワークフローの開発を開始します。
• Modinの機能、アーキテクチャ、および優位性を理解します。
• Modin、Dask、およびRayの違いを知ります。
• Modinを使用してPandas操作を高速に行います。
• 全Pandas APIと関数を実装します。

This instructor-led, live training in 日本 (online or onsite) is aimed at data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms, such as XGBoost, cuML, etc.

By the end of this training, participants will be able to:

• Set up the necessary development environment to build data models with NVIDIA RAPIDS.
• Understand the features, components, and advantages of RAPIDS.
• Leverage GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
• Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
• Build data visualizations and execute graph analysis with cuXfilter and cuGraph.