Data Science Training in Seoul

This instructor-led, live training in Seoul (online or onsite) is designed for beginner-level professionals who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without developing models from the ground up.

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

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

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

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

• Establish a data science environment in AWS Cloud9.
• Conduct data analysis using Python, R, and Jupyter Notebook within Cloud9.
• Connect AWS Cloud9 with AWS data services such as S3, RDS, and Redshift.
• Leverage AWS Cloud9 for developing and deploying machine learning models.
• Enhance cloud-based workflows for efficient data analysis and processing.

This instructor-led live training, offered Seoul (online or onsite), is designed for intermediate-level participants who aim 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 orchestrating machine learning workflows.
• 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 Seoul (online or onsite) is designed for beginner-level data scientists and IT professionals seeking to grasp the fundamentals of data science using Google Colab.

Upon completing this training, participants will be able to:

• Configure and navigate Google Colab.
• Compose and execute fundamental Python code.
• Import and manage datasets.
• Generate visualizations using Python libraries.

This instructor-led live training in Seoul (online or on-site) is tailored for data scientists and developers aiming to launch or enhance their careers in Data Science using Kaggle.

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

• Gain foundational knowledge of data science and machine learning.
• Explore the field of data analytics.
• Understand the Kaggle ecosystem and its operational mechanisms.

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 a diverse array of financial applications, from core trading systems to sophisticated risk management platforms.

In this instructor-led live training, participants will learn how to leverage Python to create practical applications that address specific finance-related challenges.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and maintain the optimal development tools for building financial applications in Python
• Choose and employ the most appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web APIs, etc.)
• Develop applications that solve problems related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Format of the course

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

Note

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

This course explores practical methodologies for Data Science and AI utilizing Python. It empowers professionals with the necessary skills to analyze data, construct machine learning models, and deploy AI-driven solutions within business environments. The curriculum covers CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data handling with Spark, and data-driven storytelling. It is ideal for beginners pursuing a Python data science certification and career-focused analytics training.

This instructor-led live training, available in Seoul (online or onsite), is designed for data scientists who wish to utilize the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows on 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.
• Learn practical use cases and techniques for managing multiple data environments.

KNIME Analytics Platform stands out as a premier open-source solution for driving data-led innovation. It empowers you to uncover hidden potential within your data, extract fresh insights, and forecast future trends. With over 1,000 modules, hundreds of pre-built examples, a robust suite of integrated tools, and an extensive selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course offers beginners, intermediate users, and KNIME specialists a valuable opportunity to familiarize themselves with KNIME, enhance their proficiency, and learn how to generate clear, comprehensive reports through KNIME workflows.

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

The program is specifically tailored for individuals without programming backgrounds who aim to utilize state-of-the-art tools to implement analytics solutions.

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

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

Format of the Course

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

Course Customization Options

• For customized training requests or further information about this program, please contact us to arrange details.

This instructor-led, live training in Seoul (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 Seoul (online or onsite) is designed for 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 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.