Introduction to Data Science Training Course

This five-day course provides an introduction to Data Science and Artificial Intelligence (AI).

Instruction is delivered through practical examples and exercises using Python.

This instructor-led live training, offered South Korea (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, available in South Korea (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.

This instructor-led, live training in South Korea (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 in South Korea (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.

Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.

Audience

• Developers
• Technical analysts
• IT consultants

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

Big data refers to datasets so vast and complex that conventional data processing software becomes insufficient. Challenges associated with big data encompass data capture, storage, analysis, searching, sharing, transfer, visualization, querying, updating, and ensuring information privacy.

This course is designed for marketing and sales professionals looking to deepen their understanding of applying data science within these fields. It offers a comprehensive exploration of various data science techniques applied to upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Distinguishing Marketing from Sales - What sets sales and marketing apart?

Simply put, sales focuses on individuals or small groups, whereas marketing targets a broader audience or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative offerings), and promotion (via advertisements to raise consumer awareness). Essentially, marketing is about generating leads or prospects. Once a product is launched, the sales team's role is to persuade those prospects to make a purchase. Sales aims to convert leads into orders and purchases, typically focusing on short-term goals, while marketing concentrates on long-term objectives.

This instructor-led, live training in South Korea (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 South Korea (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.

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 training is divided into three parts. The first section introduces MATLAB's core concepts, covering its role as both a programming language and a computing platform. Topics include MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented programming principles.

The second section demonstrates how to leverage MATLAB for data mining, machine learning, and predictive analytics. To highlight MATLAB's effectiveness and versatility, we compare its approach with other common tools such as spreadsheets, C, C++, and Visual Basic.

In the final section, participants learn to streamline their workflows by automating data processing and report generation.

Throughout the course, participants will apply concepts through hands-on exercises in a lab environment. By the end of the training, they will have a comprehensive understanding of MATLAB's capabilities and be equipped to solve real-world data science problems and automate their work processes.

Progress assessments will be conducted throughout the course.

Course Format

• The course combines theoretical instruction with practical exercises, including case discussions, code review, and hands-on implementation.

Note

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

This instructor-led, live training in South Korea (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 South Korea (online or onsite) targets data scientists and developers who aim to use Modin to build and implement parallel computations with Pandas for faster data analysis.

Upon completion of this training, participants will be capable of:

• Configuring the required environment to begin developing scalable Pandas workflows using Modin.
• Gaining insight into Modin's features, architecture, and key benefits.
• Understanding the distinctions between Modin, Dask, and Ray.
• Executing Pandas operations more efficiently with Modin.
• Implementing the full range of Pandas API functionalities.

This instructor-led live training in South Korea (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.