Jupyter for Data Science Teams Training Course

This is a 5-day introductory course on Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python

This instructor-led, live training in Slovakia (online or onsite) is designed for intermediate-level participants who wish to automate and manage machine learning workflows. The course includes 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 live training session, facilitated by an instructor in Slovakia (online or onsite), targets data scientists who aim 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.
• Grasp the fundamental concepts, features, and advantages of Anaconda.
• Handle packages, environments, and channels via Anaconda Navigator.
• Utilize Conda, R, and Python packages for data science and machine learning tasks.
• Explore practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in Slovakia (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 Slovakia (available online or onsite) targets beginner-level data scientists and IT professionals who want 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 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 that are so voluminous and complex that traditional data processing application software cannot effectively manage them. Challenges associated with big data encompass capturing, storing, analyzing, searching, sharing, transferring, visualizing, querying, updating, and ensuring information privacy.

This course is designed for Marketing and Sales Professionals who wish to deepen their understanding of applying data science within these fields. The curriculum offers a comprehensive overview of various data science techniques utilized for 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 broader audiences or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative solutions), and promotion (advertising to build consumer awareness), effectively generating leads or prospects. Once a product reaches the market, it becomes the salesperson's role to persuade customers to make a purchase. While sales aims to convert leads into immediate orders, marketing focuses on long-term objectives, whereas sales addresses shorter-term goals.

This instructor-led, live training (available online or onsite) is designed for 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 Slovakia (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.

The KNIME Analytics Platform stands as a premier open-source solution for data-driven innovation. It empowers you to uncover hidden potential within your data, extract fresh insights, or predict future trends. Boasting over 1,000 modules, hundreds of ready-to-run examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, the KNIME Analytics Platform serves as the ideal toolkit for every data scientist and business analyst.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to familiarize themselves with the platform, learn to utilize it more effectively, and master the creation of clear, comprehensive reports based on 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.

The course targets participants who may not have a programming background but intend to use cutting-edge tools to implement analytical 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.

Course Format

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

Course Customization Options

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

The initial segment of this training explores the core principles of MATLAB, emphasizing its role as both a programming language and a computing platform. This section covers essential topics such as MATLAB syntax, arrays and matrices, data visualization, script creation, and object-oriented programming concepts.

In the second phase, we illustrate how to leverage MATLAB for data mining, machine learning, and predictive analytics. To highlight the efficiency and power of MATLAB, we compare its functionality with other common tools like spreadsheets, C, C++, and Visual Basic.

During the final segment, participants will learn to optimize their workflow by automating data processing and report generation tasks.

Throughout the course, participants will apply learned concepts through practical exercises in a lab setting. By the conclusion of the training, participants will possess a comprehensive understanding of MATLAB's capabilities and will be equipped to solve real-world data science challenges and streamline workflows via automation.

Progress assessments will be conducted throughout the course to monitor advancement.

Course Format

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

Note

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

This instructor-led, live training in Slovakia (online or onsite) targets intermediate-level data analysts, developers, or aspiring data scientists eager to leverage machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

Upon completing this course, participants will be capable of:

• Understanding and distinguishing between key machine learning paradigms.
• Exploring data preprocessing techniques and model evaluation metrics.
• Applying machine learning algorithms to resolve real-world data challenges.
• Utilizing Python libraries and Jupyter notebooks for practical development.
• Building models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Slovakia (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 Slovakia (online or onsite) is designed for data scientists and developers who want 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.