Jupyter for Data Science Teams Training Course

This course explores practical methodologies for Data Science and AI using Python, equipping professionals with the expertise to analyze data, develop machine learning models, and deploy AI-powered applications within business environments. It covers the CRISP-DM framework, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is ideal for beginners looking to obtain a Python data science certification and receive career-focused analytics training.

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level participants who aim to automate and manage machine learning workflows, encompassing model training, validation, and deployment via Apache Airflow.

Upon completing this training, participants will be capable of:

• Configuring Apache Airflow to orchestrate machine learning workflows.
• Automating tasks related to data preprocessing, model training, and validation.
• Integrating Airflow with various machine learning frameworks and tools.
• Deploying machine learning models through automated pipelines.
• Monitoring and optimizing machine learning workflows in a production environment.

This instructor-led, live training in Italy (online or on-site) is directed at data scientists who intend to use 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.
• 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 Italy (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 Italy (online or on-site) is designed for 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.

Designed specifically for marketing and sales professionals eager to deepen their understanding of data science applications in these fields, this course offers a comprehensive exploration of various data science techniques. These methods are applied to enhance upselling, cross-selling, market segmentation, branding efforts, and Customer Lifetime Value (CLV) analysis.

Understanding the Distinction Between Marketing and Sales - What sets them apart?

Simply put, sales is a process focused on targeting individuals or small groups. In contrast, marketing aims at a broader audience or the general public. Marketing encompasses researching customer needs, developing innovative products, and promoting them through advertising to build awareness. Essentially, marketing is about generating leads or prospects. Once the product reaches the market, the salesperson's role is to persuade these prospects to make a purchase. While sales focuses on converting leads into immediate orders with short-term goals, marketing is oriented toward long-term strategies.

This instructor-led live training in Italy (online or onsite) is designed for data scientists and developers who aim to learn and build their careers in Data Science using Kaggle.

Upon completion of this training, participants will be equipped to:

• Gain a solid understanding of data science and machine learning concepts.
• Conduct exploratory data analysis.
• Understand the functionality and mechanics of the Kaggle platform.

The KNIME Analytics Platform stands as a premier open-source solution for data-driven innovation, empowering users to uncover hidden potential within their data, mine for novel insights, or forecast future trends. Equipped with over 1000 modules, numerous ready-to-run examples, a broad suite of integrated tools, and an extensive selection of advanced algorithms, the KNIME Analytics Platform serves as an indispensable toolkit for data scientists and business analysts alike.

This course on the KNIME Analytics Platform offers an ideal introduction for beginners, advanced users, and KNIME experts. Participants will learn how to utilize KNIME more effectively and how to develop clear, comprehensive reports based on KNIME workflows.

This instructor-led live training, available either online or onsite, is designed for data professionals seeking to leverage KNIME to address complex business requirements.

The program targets individuals without programming experience who intend to employ cutting-edge tools to implement analytical scenarios.

By the conclusion 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 value chain of 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

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

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who want to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

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

• Understand and distinguish between key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data challenges.
• Utilize Python libraries and Jupyter notebooks for practical development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Italy (online or onsite) is designed for beginner-level professionals who want 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.
• Explore various pre-trained model architectures and their specific use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

Python has emerged as a dominant programming language within the financial sector. Widely adopted by leading investment banks and hedge funds, it serves as the foundation for numerous financial applications, spanning from core trading systems to advanced risk management platforms.

Through this instructor-led live training, participants will acquire the skills necessary to leverage Python for developing practical solutions to specific finance-related challenges.

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

• Grasping the fundamental concepts of the Python programming language
• Downloading, installing, and managing optimal development tools for building financial applications in Python
• Selecting and applying appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from diverse sources (such as CSV files, Excel spreadsheets, databases, and web APIs)
• Developing applications that address issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshooting, integrating, deploying, and optimizing Python applications

Audience

• Developers
• Analysts
• Quants

Format of the course

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

Note

• This training is designed to offer solutions for key challenges faced by finance professionals. If there is a specific topic, tool, or technique you would like to include or explore in greater depth, please contact us to arrange accordingly.

This instructor-led, live training in Italy (online or onsite) is designed for data scientists and developers who aim to leverage RAPIDS for building GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms like XGBoost and cuML.

By the conclusion 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.