Jupyter for Data Science Teams Training Course

 This course explores practical methods for Data Science and AI using Python, equipping professionals with the skills to explore data, build machine learning models, and deploy AI-driven applications in business contexts; It 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.

This instructor-led, live training in Malaysia (available online or onsite) targets 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 equipped to:

• Configure Apache Airflow for orchestrating machine learning workflows.
• Automate tasks related to data preprocessing, model training, and validation.
• Seamlessly integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows within production environments.

This instructor-led live training in Malaysia (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 Malaysia (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 Malaysia (online or onsite) is designed for beginner-level data scientists and IT professionals seeking to grasp the fundamentals of data science via Google Colab.

Upon completing this training, participants will be able to:

• Configure and navigate Google Colab.
• Compose and run fundamental Python code.
• Import and manage datasets.
• Produce visualizations leveraging Python libraries.

This course is designed for Marketing and Sales Professionals who wish to deepen their understanding of how to apply data science within these fields. It provides comprehensive coverage of various data science techniques utilized for 'upselling', 'cross-selling', market segmentation, branding, and Customer Lifetime Value (CLV).

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

In simple terms, sales is a process that focuses on targeting individuals or small groups. Marketing, on the other hand, targets a broader audience or the general public. Marketing involves research (identifying customer needs), product development (creating innovative offerings), and promotion (through advertisements to create awareness among consumers). Essentially, marketing is about generating leads or prospects. Once a product is launched, the salesperson's role is to persuade customers to make a purchase. Sales focuses on converting these leads or prospects into purchases and orders, whereas marketing is geared toward long-term goals, while sales addresses shorter-term objectives.

This instructor-led live training, conducted Malaysia (online or onsite), targets data scientists and developers aiming to learn and develop their careers in Data Science using Kaggle.

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

• Understand data science and machine learning.
• Explore data analytics.
• Learn about Kaggle and how it works.

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

This course offers an excellent pathway for beginners, advanced users, and seasoned KNIME experts to familiarize themselves with the platform, enhance their proficiency, and learn how to generate clear, detailed reports using KNIME workflows.

Delivered by an instructor, this live training (available online or on-site) is designed for data professionals aiming to leverage KNIME to address complex business requirements.

The programme is specifically tailored for individuals who may not have programming expertise but wish to utilise cutting-edge tools to execute analytics scenarios.

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

• Installing and configuring KNIME.
• Developing Data Science scenarios.
• Training, testing, and validating models.
• Implementing the end-to-end data science model value chain.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Practical implementation within a live-lab environment.

Customisation Options

• For personalised training or further details on this programme, please contact us to make arrangements.

This instructor-led, live training in Malaysia (online or on-site) targets intermediate-level data analysts, developers, and aspiring data scientists who aim to utilise 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:

• Comprehend and distinguish key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to resolve real-world data problems.
• Utilise Python libraries and Jupyter notebooks for practical development.
• Construct models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Malaysia (online or on-site) is designed for 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.

Upon completing this training, participants will be able to:

• Comprehend the concept and advantages of pre-trained models.
• Examine various pre-trained model architectures and their respective applications.
• Fine-tune a pre-trained model to suit specific tasks.
• Integrate pre-trained models into basic machine learning projects.

Python has surged in popularity within the financial sector, becoming the preferred tool for major investment banks and hedge funds. It is extensively utilized 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 solutions for various finance-specific challenges.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and configure the most effective development tools for building financial applications in Python
• Select and apply appropriate Python libraries and programming techniques to organize, visualize, and analyze financial data from diverse sources (such as CSV, Excel, databases, and web APIs)
• Develop applications that address issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Target Audience

• Developers
• Analysts
• Quants

Course Format

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

Note

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

This instructor-led live training in Malaysia (online or onsite) targets data scientists and developers who want to use RAPIDS to develop GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms like 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.