Introduction to Data Science Training Course

This five-day program introduces participants to Data Science and Artificial Intelligence (AI).

Instruction is delivered through practical examples and exercises conducted in Python.

This instructor-led live training in Taiwan (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, conducted in Taiwan (online or onsite), targets 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.
• Gain insight into practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in Taiwan (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 Taiwan (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.

Upon completing this training, participants will develop a practical, real-world grasp of Data Science, including its associated technologies, methodologies, and tools.

Attendees will apply their knowledge through interactive, hands-on exercises. The course places significant emphasis on group collaboration and direct feedback from the instructor.

The curriculum begins by covering the foundational concepts of Data Science before advancing to the specific tools and methodologies employed in the field.

Target Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Important Note

• To arrange customized training for this course, please contact us.

Big data refers to datasets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

This course is designed for Marketing and Sales professionals seeking to deepen their understanding of applying data science within these fields. It offers a comprehensive overview of various data science techniques utilized for "upselling," "cross-selling," market segmentation, branding, and Customer Lifetime Value (CLV).

The Distinction Between Marketing and Sales - What sets sales and marketing apart?

In simple terms, sales is a process that targets individuals or small groups. Marketing, on the other hand, aims at a broader audience or the general public. Marketing involves research (identifying customer needs), product development (creating innovative offerings), and promotion (via advertisements) to build consumer awareness. Essentially, marketing is about generating leads or prospects. Once a product reaches the market, it becomes the salesperson's role to persuade customers to make a purchase. Sales focuses on converting those leads into orders, whereas marketing is oriented toward long-term goals, while sales pertains to shorter-term objectives.

This instructor-led, live training in Taiwan (online or onsite) introduces the concept of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea". It guides participants through the creation of a sample data science project built on the Jupyter ecosystem.

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

• Install and configure Jupyter, including setting up and integrating a team repository on Git.
• Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from Scala, Python, or R to write and execute code against big data systems like Apache Spark, all within the Jupyter interface.

This instructor-led live training in Taiwan (online or on-site) is designed for data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.

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

• Gain a comprehensive understanding of data science and machine learning.
• Explore the intricacies of data analytics.
• Understand Kaggle and its operational mechanisms.

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.

The initial segment of this training establishes a solid foundation in MATLAB, exploring its dual role as both a programming language and a computational platform. Key topics include MATLAB syntax, arrays and matrices, data visualization techniques, script development, and the core principles of object-oriented programming.

In the second segment, the course demonstrates MATLAB's capabilities in data mining, machine learning, and predictive analytics. To illustrate MATLAB's unique advantages and power, we draw comparisons between using MATLAB and other common tools such as spreadsheets, C, C++, and Visual Basic.

The final segment focuses on streamlining workflows by automating data processing and report generation tasks.

Throughout the course, participants will reinforce their learning through hands-on exercises within a lab environment. By the conclusion of the training, participants will possess a comprehensive understanding of MATLAB's capabilities, enabling them to solve real-world data science problems and automate routine tasks for greater efficiency.

Progress will be evaluated through assessments conducted throughout the course.

Course Format

• The course combines theoretical instruction with practical exercises, including case studies, sample code analysis, and hands-on implementation.

Note

• Practice sessions utilize pre-arranged sample data report templates. If you have specific requirements, please contact us to make arrangements.

This instructor-led, live training in Taiwan (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 Taiwan (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.

This instructor-led, live training in Taiwan (online or onsite) is tailored 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.