Jupyter for Data Science Teams Training Course

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

The course includes practical examples and exercises using Python.

This instructor-led, live training in Norway (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 Norway (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 Norway (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 Norway (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 a practical, real-world understanding of Data Science, along with its associated technologies, methodologies, and tools.

The training provides opportunities for participants to apply their knowledge through hands-on exercises. Group interaction and feedback from the instructor are integral parts of the course.

The course begins with an introduction to fundamental concepts in Data Science, before delving 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.

The unprecedented explosion of information and data in today’s world is driving rapid innovation and pushing the boundaries of what is possible. The role of a Data Scientist is one of the most sought-after skills across industries today.

We go beyond mere theoretical learning; we provide practical, marketable skills that bridge the gap between academia and industry demands.

This 7-week curriculum can be customized to meet your specific industry requirements. For further information or to visit the Nobleprog Institute website, please contact us.

Audience:

This programme is designed for postgraduate level individuals as well as anyone with the necessary pre-requisite skills, which will be assessed through an evaluation and interview process.

Delivery:

The course delivery combines Instructor-Led Classroom sessions and Instructor-Led Online sessions. Typically, the first week is classroom-based, weeks 2 to 6 are conducted in a virtual classroom, and the final week returns to a classroom setting.

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

This course is designed for Marketing and Sales Professionals who wish to delve deeper into the application of data science in their respective fields. The course offers comprehensive coverage of various data science techniques used for up-selling, 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. Conversely, marketing targets a broader audience or the general public. Marketing encompasses research to identify customer needs, the development of innovative products, and promoting these products through advertisements to create awareness among consumers. Essentially, marketing involves generating leads or prospects. Once the product is available in the market, it is the salesperson's role to persuade customers to make a purchase. While sales focus on converting leads into actual purchases and orders, marketing aims at longer-term goals.

This instructor-led, live training (online or onsite) is aimed at professionals who wish to embark on a career in Data Science.

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

• Install and configure Python and MySQL.
• Understand what Data Science entails and how it can bring value to almost any business.
• Learn the basics of coding in Python.
• Explore supervised and unsupervised Machine Learning techniques, and gain skills to implement them and interpret the results.

Format of the Course

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

Course Customization Options

• For a tailored training experience, please contact us to arrange customizations for this course.

This instructor-led, live training in Norway (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 prominent open-source solution for data-driven innovation, enabling you to uncover the potential within your data, discover new insights, or 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 and business analyst.

This course on KNIME Analytics Platform offers a valuable opportunity for beginners, advanced users, and KNIME experts to become familiar with 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 on-site) is designed for data professionals who wish to leverage KNIME to address complex business challenges.

It is particularly suited for those without programming knowledge who aim to use state-of-the-art tools to implement 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 lecture and discussion.
• Extensive 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 initial part of this training, we delve into the foundational aspects of MATLAB, exploring its role as both a programming language and a comprehensive platform. This section covers an introduction to MATLAB syntax, arrays and matrices, data visualization techniques, script development, and object-oriented programming principles.

In the second segment, 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 to other tools such as spreadsheets, C, C++, and Visual Basic.

The third part of the training focuses on enhancing 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 solid understanding of MATLAB's functionalities and will be equipped to use it for solving real-world data science problems and streamlining their work through automation.

Regular 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 use pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange for customized content.

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