Data Science Training in Hanoi

This instructor-led, live training in Hanoi (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.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

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

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led, live training in Hanoi (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.

This instructor-led live training in Hanoi (online or onsite) is designed for beginner-level data scientists and IT professionals who wish to grasp the essentials of data science through Google Colab.

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

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

This instructor-led live training in Hanoi (available online or onsite) is designed for data scientists and developers aiming to establish or enhance their careers in Data Science using Kaggle.

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

• Understand the fundamentals of data science and machine learning.
• Perform data analytics.
• Comprehend Kaggle's functionality and ecosystem.

Python has become a highly popular programming language within the financial sector. It is widely adopted by major investment banks and hedge funds to develop diverse financial applications, from core trading systems to risk management tools.

Through this instructor-led live training, participants will learn how to leverage Python to create practical solutions for various finance-specific challenges.

Upon completion of this course, participants will be able to:

• Grasp the fundamental concepts of the Python programming language
• Download, install, and configure optimal development tools for building financial applications in Python
• Choose and apply appropriate Python packages and techniques to organize, visualize, and analyze financial data from diverse sources (CSV, Excel, databases, web APIs, etc.)
• Develop applications that address issues such as asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training focuses on providing solutions to key challenges faced by finance professionals. If you have a specific topic, tool, or technique you wish to include or explore in greater depth, please contact us to arrange for customization.

This course provides an in-depth exploration of practical Data Science and AI techniques leveraging Python, empowering professionals with the capabilities to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. The curriculum encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning methodologies, deep learning with TensorFlow, natural language processing, big data handling via Spark, and the art of data-driven storytelling. It is ideally suited for beginners aiming to obtain a Python data science certification and acquire career-ready analytics training.

This instructor-led live training in Hanoi (available online or on-site) is designed for data scientists who wish to utilize the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows within 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.

The KNIME Analytics Platform is a premier open-source solution for driving data-led innovation. It empowers you to uncover hidden potential within your data, extract fresh insights, or forecast future trends. Featuring over 1,000 modules, numerous ready-to-execute examples, a broad suite of integrated tools, and the most extensive selection of advanced algorithms available, KNIME Analytics Platform serves as the ideal toolbox for any data scientist or business analyst.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts alike to become familiar with KNIME, learn how to utilize it more effectively, and master the creation of clear, comprehensive reports based on KNIME workflows.

This instructor-led live training (available online or onsite) is designed for data professionals seeking to leverage KNIME to address complex business challenges.

The course targets participants who may not have programming experience but intend to use cutting-edge tools to implement analytics scenarios.

Upon completion of this training, participants will be able to:

• Install and configure KNIME.
• Construct Data Science scenarios
• Train, test, and validate models
• Implement the end-to-end value chain for data science models

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course or to learn more about this program, please contact us to make arrangements.

This instructor-led, live training in Hanoi (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

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

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

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

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.