Jupyter for Data Science Teams Training Course

This five-day course offers a comprehensive introduction to Data Science and Artificial Intelligence (AI).

The training is delivered through practical examples and exercises utilizing Python.

This instructor-led, live training in Greece (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.

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 Greece (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 within 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 Greece (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 Greece (online or in-person) is designed for novice data scientists and IT specialists seeking to grasp the fundamentals of data science via Google Colab.

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

• Configuring and navigating Google Colab.
• Writing and running fundamental Python code.
• Importing and managing datasets.
• Generating visualisations using Python libraries.

Upon completing this training, participants will acquire a practical, real-world comprehension of Data Science, alongside its associated technologies, methodologies, and tools.

Learners will have the chance to apply this knowledge through hands-on exercises. Interactive group discussions and feedback from the instructor are key elements of the course.

The course begins with an overview of fundamental Data Science concepts before advancing to the specific tools and methodologies employed within the field.

Target Audience

• Developers
• Technical analysts
• IT consultants

Course Format

• A blend of lectures, discussions, exercises, and extensive practical application

Note

• To arrange a customized training session for this course, please get in touch with us.

Big data refers to datasets that are excessively large and complex, rendering traditional data processing application software inadequate for handling them. Key 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 eager to deepen their understanding of how to apply data science within these fields. It offers a comprehensive exploration of various data science techniques utilized for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV) analysis.

Distinguishing Marketing from Sales - How do sales and marketing differ?

In simple terms, sales focuses on individuals or small groups, while marketing targets a broader audience or the general public. Marketing involves researching customer needs, developing innovative products, promoting them through advertising, and building awareness among consumers. Essentially, marketing generates leads or prospects. Once a product is available, the salesperson's role is to persuade customers to make a purchase. Sales focuses on converting leads into orders, whereas marketing is geared toward long-term goals, while sales targets shorter-term objectives.

This instructor-led, live training (online or onsite) is aimed at professionals who wish to start 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 is and how it can add value to virtually any business. 
• Learn the fundamentals of coding in Python
• Learn supervised and unsupervised Machine Learning techniques, and how to implement them and interpret the results.  

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led live training in Greece (online or onsite) is designed for 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 stands as a premier open-source solution for driving data-based innovation, enabling you to uncover hidden potential within your data, extract new insights, or forecast future trends. Boasting over 1,000 modules, numerous ready-to-run examples, a broad suite of integrated tools, and the most 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 entry point for beginners, advanced users, and KNIME experts to familiarize themselves with KNIME, enhance their proficiency, and learn to generate clear, comprehensive reports using KNIME workflows.

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

It is specifically tailored for participants without programming experience who 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.

Format of the Course

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

Course Customization Options

• For requests regarding customized training for this course or to learn more about the program, please contact us to arrange.

The initial segment of this training establishes the core concepts of MATLAB, exploring its dual role as both a programming language and a comprehensive platform. This section introduces essential MATLAB syntax, array and matrix operations, data visualization techniques, script development methodologies, and the principles of object-oriented programming.

During the second phase, we illustrate how MATLAB can be leveraged for data mining, machine learning, and predictive analytics. To highlight MATLAB’s unique advantages and power, we compare its functionality against other common tools such as spreadsheets, C, C++, and Visual Basic, offering participants a practical and clear perspective.

In the final segment, attendees learn strategies to streamline their workflow by automating data processing and report generation tasks.

Throughout the course, participants will apply theoretical knowledge through practical exercises in a dedicated lab environment. Upon completion, participants will possess a robust understanding of MATLAB’s capabilities, enabling them to solve real-world data science challenges and automate their professional tasks effectively.

Progress will be monitored through continuous assessments conducted throughout the course.

Course Format

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

Note

• Practice sessions utilize pre-arranged sample data report templates. For specific customization requirements, please contact us to arrange alternatives.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists looking 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 Greece (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 Greece (online or on-site) is designed for data scientists and developers who wish to leverage RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms such as XGBoost, cuML, and others.

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.