日本のデータサイエンストレーニング

This instructor-led, live training in 日本 (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

このインストラクター主導の実践的なトレーニング（オンラインまたはオンサイト）は、AWS Cloud9を使用して効率的なデータサイエンスワークフローを構築したい中級レベルのデータサイエンティストとアナリスト向けです。

このトレーニング終了時には、参加者は以下のことをできるようになります：

• AWS Cloud9でのデータサイエンス環境の設定
• Python、R、およびJupyter Notebookを使用してデータ分析を行う
• S3、RDS、RedshiftなどのAWSデータサービスとの統合
• AWS Cloud9を使用した機械学習モデルの開発と展開
• データ分析と処理のためのクラウドベースワークフローの最適化

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

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

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

このインストラクター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、Google Colab を使用してデータサイエンスの基本を学びたい初心者向けのデータサイエンティストと IT 専門家を対象としています。

このトレーニング終了時には、参加者は以下のことができるようになります：

• Google Colab の設定とナビゲーションを行う。
• 基本的な Python コードを記述および実行する。
• データセットのインポートと操作を行う。
• Python のライブラリを使用して可視化を作成する。

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.

The training course will help the participants prepare for Web Application Development using Python Programming with Data Analytics. Such data visualization is a great tool for Top Management in decision making.

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.

Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to use Python 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 Python programming language
• Download, install and maintain the best development tools for creating financial applications in Python
• Select and utilize the most suitable Python packages and programming 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 a Python 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.

このコースは、データサイエンスと人工知能（AI）の5日間の入門です。

Python を使用した例題や演習を交えて展開します。

This instructor-led, live training (online or onsite) is aimed at professionals who wish to start a career in Data Science.

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

• Install and configure Python and MySql.
• Understand what Data Science is and how it can add value to virtually any business. 
• Learn the fundamentals of coding in Python
• Learn supervised and unsupervised Machine Learning techniques, and how to implement them and interpret the results.  

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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.

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。

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.

This course is meant for Marketing Sales Professionals who are intending to get deeper into application of data science in Marketing/ Sales. The course provides
detailed coverage of different data science techniques used for “upsale”, “cross-sale”, market segmentation, branding and CLV.

Difference of Marketing and Sales - How is that sales and marketing are different?

In very simplewords, sales can be termed as a process which focuses or targets on individuals or small groups. Marketing on the other hand targets a larger group or the general public. Marketing includes research (identifying needs of the customer), development of products (producing innovative products) and promoting the product (through advertisements) and create awareness about the product among the consumers. As such marketing means generating leads or prospects. Once the product is out in the market, it is the task of the sales person to persuade the customer to buy the product. Sales means converting the leads or prospects into purchases and orders, while marketing is aimed at longer terms, sales pertain to shorter goals.

KNIME Analytics Platform is a leading open source option for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist and business analyst.

This course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows

This instructor-led, live training (online or onsite) is aimed at data professionals who wish to use KNIME to solve complex business needs.

It is targeted for the audience that doesn't know programming and intends to use cutting edge tools to implement analytics scenarios

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

• Install and configure KNIME.
• Build Data Science scenarios
• Train, test and validate models
• Implement end to end value chain of data science models

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course or to know more on this program, 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、中級レベルのデータアナリスト、開発者、またはデータサイエンティストを目指している人々向けです。参加者はPythonを使用して機械学習技術を適用し、洞察を得たり、予測を行ったり、データ駆動型の意思決定を自動化する方法を学びます。

このコース終了時には、参加者は以下のことをできるようになります：

• 主要な機械学習パラダイムを理解し、区別することができます。
• データ前処理技術とモデル評価指標を探索できます。
• 実世界のデータ問題に機械学習アルゴリズムを適用できます。
• PythonライブラリやJupyterノートブックを使用して手動開発を行えます。
• 予測、分類、推薦、クラスタリング用のモデルを作成できます。

This instructor-led, live training in 日本 (online or onsite) is aimed at data analysts and web developers who wish to develop associative models in Qlik Sense.

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

• Apply Qlik Sense in data science.
• Use and navigate the Qlik Sense interface.
• Build a data literate workforce with AI interaction.
• Create a data-driven enterprise with Qlik Sense.

この没入型トレーニングを修了後、あなたはエントリーレベルの量子コンピューティング開発者として仕事を始めることができます。

本講座終了時には、参加者は以下のことをすることができます：

• 統合されたIBM Qを使用して量子プログラムを実行およびテストする
• Qiskitを使用して、量子コンピューティングプログラムを作成、コンパイル、および実行する
• QAOAなどの実践的かつ高度な量子アルゴリズムを扱う
• 現実世界の問題を適切な量子コンピューティング言語に変換する

コース形式

• 双方向の講義とディスカッション。
• 多数の演習と実践。
• ライブラボ環境での手動実装。

コースカスタマイズオプション

• このコースのカスタマイズ版を希望される場合は、お問い合わせください。

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.

This instructor-led, live training in 日本 (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.

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

• Implement a data pipeline architecture for processing big data.
• Develop a cluster infrastructure with Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

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。