Data Science Training in Hsinchu

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

Upon completing this training, participants will be capable of:

• Configuring Apache Airflow to orchestrate machine learning workflows.
• Automating tasks such as data preprocessing, model training, and validation.
• Integrating Airflow with various machine learning frameworks and tools.
• Deploying machine learning models through automated pipelines.
• Monitoring and optimizing machine learning workflows in production environments.

This instructor-led live training in Hsinchu (online or onsite) targets beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

By the end 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.

Python has surged in popularity within the financial sector, becoming a staple for leading investment banks and hedge funds. It is widely utilized to construct a diverse array of financial solutions, from core trading platforms to sophisticated risk management systems.

Through this instructor-led live training, participants will learn how to leverage Python to develop practical applications that address specific challenges in the finance industry.

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

• Grasp the core fundamentals of Python programming
• Download, install, and maintain optimal development tools for building financial applications in Python
• Select and employ the most effective Python packages and programming techniques to organize, visualize, and analyze financial data from diverse sources (CSV, Excel, databases, web, etc.)
• Develop applications to solve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Course Format

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

Note

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

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

The KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation. It empowers users to uncover hidden potential within their data, extract fresh insights, and predict future outcomes. Featuring over 1,000 modules, numerous ready-to-run examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, the KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts alike. Participants will be introduced to KNIME, learn how to utilize it more efficiently, and master the creation of clear, detailed reports based on KNIME workflows.

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

The program targets individuals who may not have programming knowledge but wish to utilize cutting-edge tools to implement analytics scenarios.

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.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practice 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 arrange it.

This instructor-led, live training in Hsinchu (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.