Python Programming for Finance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.

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、Daskを使用してPythonエコシステムで大規模データセットの構築、スケーリング、分析を行うことを目指すデータサイエンティストやソフトウェアエンジニアを対象としています。

このトレーニングの終了時、参加者は以下のことができるようになります：

• DaskとPythonを使用して大規模データ処理の環境をセットアップする。
• Daskで利用可能な機能、ライブラリ、ツール、APIを探索する。
• DaskがどのようにPythonでの並列計算を加速するかを理解する。
• Numpy、SciPy、Pandasを使用したPythonエコシステムのスケーリング方法を学ぶ。
• 大規模データセットの処理においてDask環境を最適化して高性能を維持する。

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

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、FARM (FastAPI, React, MongoDB)スタックを使用してダイナミックで高性能かつスケーラブルなウェブアプリケーションを構築したい開発者向けです。

このトレーニングの終了時、参加者は以下のことができます：

• FastAPI、React、MongoDBを統合した開発環境を設定する。
• FARMスタックの主要な概念、特徴、および利点を理解する。
• FastAPIを使用してREST APIを構築する方法を学ぶ。
• Reactを使用してインタラクティブなアプリケーションを設計する方法を学ぶ。
• FARMスタックを使用してアプリケーション（フロントエンドとバックエンド）を開発、テスト、デプロイする。

This instructor-led, live training in 日本 (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

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

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led, live training in 日本 (online or onsite) is aimed at cloud administrators, cloud architects, technology heads, and financial analysts who wish to record, manage, monitor, and process financial assets of an organization in the cloud.

By the end of this training, participants will be able to use FinOps practices in an organization to forecast costs, optimize processes, and perform financial management operations in the cloud.

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level finance professionals who wish to gain a comprehensive knowledge of FinOps principles and methodologies, including cloud financial management, optimization strategies, and collaboration among finance, engineering, and business teams.

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

• Understand the FinOps framework, principles, and phases.
• Manage cloud costs effectively through data analysis and governance.
• Collaborate between finance, engineering, and business units to align cloud spend.
• Use FinOps tools for cost allocation, forecasting, and optimization.
• Prepare for the FinOps Certified FOCUS Analyst exam.

This instructor-led, live training in 日本 (online or onsite) is aimed at advanced-level cloud financial management professionals who wish to validate their expertise in financial operations (FinOps) related to cloud cost management.

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

• Gain advanced knowledge required for the FinOps Certified Professional exam.
• Understand complex FinOps practices, including cost optimization, budget management, and reporting.
• Develop practical skills in applying FinOps strategies in real-world scenarios.
• Prepare for a successful completion of the FinOps Certified Professional exam.

GnuCash is an open-source, cross-platform, double-entry accounting system designed for personal and small business finance management. It supports comprehensive accounting features including invoicing, expense tracking, reconciliation, and financial reporting. GnuCash integrates with online banking, supports multiple currencies, and allows exporting and importing in various data formats such as QIF, OFX, and CSV.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level business professionals who wish to manage and automate accounting, billing, and reporting processes using GnuCash effectively.

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

• Install, configure, and customize GnuCash for business use.
• Set up business accounts and manage transactions using double-entry principles.
• Generate financial statements and reports for analysis and auditing.
• Integrate GnuCash with banks, external systems, and online data sources.
• Automate recurring transactions and manage multi-currency operations.

Format of the Course

• Interactive lecture and discussion.
• Practical exercises and real-world business examples.
• Hands-on configuration and data entry 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 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.

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、簿記にXeroを使用したい会計士や経理担当者向けです。

このトレーニング終了時には、参加者は以下のことができます：

• リアルタイムでキャッシュフローを把握する。
• 銀行口座をXeroにリンクして残高照合を行う。
• Xeroで付加価値税（VAT）の申告書を作成し、確認する。
• チームメンバー間での共有用のレポートを作成する。

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 developers who wish to use Open Bank Project API to develop and test decentralized applications that can be integrated easily with digital banking systems.

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

• Set up the necessary development environment to start developing applications with OBP API.
• Understand the Open Bank Project architecture, technology, and concepts.
• Learn how to use and customize OBP API to develop and build applications for banking.
• Explore the features, tools, APIs, and sandboxes in OBP.
• Implement security and integrations in OBP API connectors and adapters.

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.