Introduction to Data Science Training Course

This is a five-day introductory course on Data Science and Artificial Intelligence (AI).

The course includes examples and exercises that are conducted using Python.

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

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) is aimed at 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.

Participants who complete this training will acquire practical, real-world insights into Data Science, along with its associated technologies, methodologies, and tools.

The training includes hands-on exercises that allow participants to apply their knowledge. Group interaction and instructor feedback are integral parts of the course.

The course begins with an introduction to fundamental concepts in Data Science, then delves into the tools and methodologies used in the field.

Audience

• Developers
• Technical Analysts
• IT Consultants

Format of the Course

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

Note

• For a customized training session tailored to your specific needs, please contact us to arrange.

Big data refers to extremely large and intricate data sets that surpass the capabilities of conventional data processing applications. The challenges associated with big data encompass various aspects such as data capture, storage, analysis, searching, sharing, transferring, visualization, querying, updating, and ensuring information privacy.

This course is designed for Marketing Sales Professionals who are looking to delve deeper into the application of data science in their field. The curriculum offers comprehensive coverage of various data science techniques used for upselling, cross-selling, market segmentation, branding, and customer lifetime value (CLV).

Difference Between Marketing and Sales - How do sales and marketing differ?

In simple terms, sales can be described as a process that focuses on individuals or small groups. On the other hand, marketing targets a broader audience or the general public. Marketing encompasses research to identify customer needs, product development to create innovative solutions, and promotion through advertisements to raise awareness among consumers. Essentially, marketing is about generating leads or prospects. Once the product is launched, it is the sales team's responsibility to persuade customers to make a purchase. Sales involves converting leads into actual purchases and orders, while marketing focuses on long-term strategies, whereas sales are more geared towards short-term goals.

This instructor-led, live training in Taiwan (online or onsite) introduces the idea 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 walks participants through the creation of a sample data science project based on top of the Jupyter ecosystem.

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

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton.
• Create, share and organize Jupyter Notebooks with team members.
• Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

This instructor-led, live training in Taiwan (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.

KNIME Analytics Platform is a top-tier open-source solution for data-driven innovation, enabling you to uncover the hidden potential in your data, discover new insights, and predict future outcomes. With over 1,000 modules, hundreds of ready-to-use examples, a wide array of integrated tools, and an extensive selection of advanced algorithms, KNIME Analytics Platform is the ideal toolkit for any data scientist or business analyst.

This course on KNIME Analytics Platform offers a great opportunity for beginners, advanced users, and KNIME experts to get introduced to KNIME, learn how to use it more effectively, and create clear, comprehensive reports based on KNIME workflows.

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

It is particularly suitable for those who are not familiar with programming but wish to use cutting-edge tools to implement advanced analytics scenarios.

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

• Install and configure KNIME.
• Develop data science scenarios.
• Train, test, and validate models.
• Implement the full value chain of data science models from start to finish.

Format of the Course

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

Course Customization Options

• To request a customized training for this course or to learn more about the program, please contact us to arrange.

In the first part of this training, we delve into the foundational aspects of MATLAB, exploring its role as both a programming language and a versatile platform. This section covers an introduction to MATLAB syntax, arrays and matrices, data visualization techniques, script development, and object-oriented principles.

In the second part, we showcase how MATLAB can be utilized for data mining, machine learning, and predictive analytics. To provide participants with a clear and practical understanding of MATLAB's capabilities, we compare its usage with other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants will learn techniques to enhance their efficiency by automating data processing and report generation processes.

Throughout the course, participants will apply the concepts learned through hands-on exercises in a laboratory setting. By the end of the training, participants will have a comprehensive understanding of MATLAB's functionalities and will be equipped to use it for solving real-world data science problems as well as streamlining their work through automation.

Assessments will be conducted throughout the course to monitor progress.

Format of the Course

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

Note

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

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 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.