Introduction to Data Science Training Course

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

Instruction is delivered through practical examples and hands-on exercises using Python.

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.

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

Learners will have the chance to apply this knowledge through practical exercises. The class places significant emphasis on group interaction and feedback from the instructor.

The course begins with foundational concepts of Data Science before advancing to the specific tools and methods utilized in the field.

Target Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Note

• For inquiries regarding customized training options for this course, please contact us to make arrangements.

Big data refers to datasets of such immense volume and complexity that conventional data processing software is insufficient to handle them. The challenges associated with big data encompass capturing, storing, analyzing, searching, sharing, transferring, visualizing, querying, updating, and ensuring information privacy.

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 on-site) introduces the concept of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea". The course guides participants through the creation of a sample data science project built upon the Jupyter ecosystem.

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

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from Scala, Python, or R to write and execute code against big data systems like Apache Spark, all via the Jupyter interface.

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.

In the initial phase of this training, we explore the core principles of MATLAB, highlighting its dual role as both a programming language and a comprehensive platform. The curriculum introduces MATLAB syntax, arrays and matrices, data visualization techniques, script development, and object-oriented concepts.

The second segment demonstrates how to leverage MATLAB for data mining, machine learning, and predictive analytics. To clearly illustrate MATLAB’s capabilities and efficiency, we compare its workflow with other common tools such as spreadsheets, C, C++, and Visual Basic.

In the final segment, participants will learn to streamline their workflows by automating data processing and report generation tasks.

Throughout the course, participants will apply their learning through hands-on exercises in a lab setting. By the conclusion of the training, participants will possess a deep understanding of MATLAB’s features, enabling them to address real-world data science challenges and automate their work processes effectively.

Progress will be assessed continuously throughout the course.

Course Format

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

Note

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

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 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 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.