Python in Data Scienceのトレーニングコース

Kaggle is a crowd-sourced platform for data scientists. It provides a platform for users to find and publish high-quality datasets, explore and build models in a web-based data-science environment, and work with other data scientists and machine learning engineers.

This instructor-led, live training (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.

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.

Modin is a parallel data frame system designed to speed up Pandas workflows. It can be used to handle large datasets, leveraging Ray or Dask as the backend framework for distributed computing in Python.

This instructor-led, live training (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

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

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

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.

RAPIDS is a suite of open source software libraries built to accelerate GPU-driven data science and analytics pipelines. It is based on Python and includes a DataFrame API that integrates with a variety of machine learning algorithms.

This instructor-led, live training (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.

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.

Anaconda is a free distribution of Python and R programming languages for data science. It provides an easy-to-use platform that simplifies package management and deployment.

This instructor-led, live training (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.

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.

Python is a versatile programming language known for its simplicity and readability. Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. Numpy provides fundamental support for numerical computing with its array operations. Together, they form a robust ecosystem for efficient data handling and analysis in Python.

This instructor-led, live training (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.

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.

Pandas is a Python library for data manipulation and analysis. Using Pandas, users can perform predictive analysis through machine learning.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Pandas to preform predictive analysis with machine learning.

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

• Perform data wrangling in Python.
• Conduct ETL operations for machine learning.
• Create data visualizations with Pandas

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.

Dask is a flexible and high-performance Python library for parallel computing. It scales and accelerates big data processing with other Python-based data science libraries, such as Pandas, Numpy, and Scikit-Learn.

This instructor-led, live training (online or onsite) is aimed at data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

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

• Set up the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

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.

FARM (FastAPI, React, and MongoDB) is similar to MERN, but performs faster with Python and FastAPI replacing Node.js and Express as the backend. FastAPI is a high-performance Python web framework used by top companies, such as Microsoft, Uber, and Netflix. React is an open source front-end JavaScript library, while MongoDB is a fully scalable NoSQL database.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

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

FastAPI is an open source, high-performance web framework for building APIs with Python. It is used by many large companies, such as Uber, Netflix, and Microsoft.

This instructor-led, live training (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.

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.

SciPy is an open source Python library for scientific, mathematical, and technical computing. It is built on the NumPy extension, providing a wide range of functionalities for performing complex numerical operations.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use SciPy to create advanced scientific computing functions with Python.

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

• Set up the necessary development environment to start creating scientific computing functions.
• Get the full benefit of SciPy features by performing practical examples of complex operations.
• Implement and optimize mathematical algorithms and functions to solve scientific problems.
• Design data structures and interpolation methods for visualization, processing, and analysis.

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.

PyGame is an open source library of Python modules for developing game applications and programs. It is lightweight, easy to use, and compatible with any operating system or platform.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use PyGame to create and build games using Python programming.

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

• Set up the necessary development environment to start creating game applications with PyGame and Python.
• Learn how to create interactive PyGame applications integrated with animations and multimedia features.
• Run and test game programs with PyGame test suite and convert them into executable files.

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 practical course is addressed to Python developers that want to create and maintain their first web applications. It is also addressed to people who are already familiar with other web frameworks such as Django or Web2py, and want to learn how using a microframework (i.e. a framework which glues together third-party libraries instead of providing a self-contained universal solution) changes the process.

A significant part of the course is devoted not to Flask itself (it's tiny), but to third-party libraries and tools often used in Flask projects.

Flask is a micro-framework for developing web applications in Python. Unlike other frameworks, Flask does not have any dependencies on external libraries, making it lightweight and fast.

This instructor-led, live training (online or onsite) is aimed at developers who wish to use the advanced features of Flask to build scalable web applications on top of MongoDB.

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

• Set up the necessary development environment to start developing web applications with Flask.
• Get to know the advanced concepts and techniques for real-world Flask projects.
• Build a RESTful API server on top of MongoDB.
• Learn how to containerize, test, and deploy microservices with Flask, Docker, and Amazon EC2.
• Gain some insights on the advanced Flask integrations for scaling web applications.

Format of the Course

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

Course Customization Options

• This course is focused on integrating Flask with MongoDB, but other software tools (Docker, Amazon EC2, etc.) may be used for advanced learning. Please let us know if you have any specific requirements or preferences.
• To request a customized training for this course, please contact us to arrange.

Flask is a micro-framework for developing web services in Python. Flask, unlike other frameworks, does not have any dependencies on external libraries, making it lightweight and fast.

This instructor-led, live training (online or onsite) is aimed at backend developers who wish to build REST APIs with Python and Flask.

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

• Implement a REST API to allow a Flask web application to read and write to a database in the backend.
• Develop advanced authentication features like refresh tokens.
• Build a reusable backend for future Python projects.
• Simplify storage of data with SQLAlchemy.
• Deploy REST APIs onto a cloud based server.

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.