Jupyter for Data Science Teams Training Course

This course offers a hands-on exploration of Data Science and AI methodologies using Python. It is designed to empower professionals with the essential skills to analyze data, develop machine learning models, and implement AI-powered solutions within business environments. The curriculum spans the CRISP-DM framework, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data processing with Spark, and the art of data-driven storytelling. It is particularly well-suited for beginners aiming to obtain a Python data science certification and acquire career-ready analytics training.

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.

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 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.

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) targets 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 completion of this training, participants will be able to:

• Grasp the concept and advantages of pre-trained models.
• Examine different pre-trained model architectures and their respective use cases.
• Fine-tune a pre-trained model for specific objectives.
• Integrate pre-trained models into basic machine learning projects.

Python has become increasingly popular within the financial sector. Major investment banks and hedge funds have adopted it to develop a diverse array of financial applications, from core trading systems to risk management tools.

During this instructor-led live training, participants will learn how to leverage Python to create practical applications that address specific financial challenges.

By the conclusion of this training, participants will be capable of:

• Gaining a solid understanding of Python programming fundamentals
• Downloading, installing, and managing optimal development tools for building financial applications in Python
• Choosing and applying appropriate Python packages and techniques to organize, visualize, and analyze financial data from various sources (such as CSV, Excel, databases, and web APIs)
• Developing applications that solve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshooting, integrating, deploying, and optimizing Python applications

Audience

• Developers
• Analysts
• Quants

Course Format

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

• This training focuses on providing solutions to key challenges faced by finance professionals. If you have a specific topic, tool, or technique you would like to include or expand upon, please contact us to arrange this.

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.