Python in 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.

This instructor-led, live training in Italy (online or on-site) targets data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

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

• Configure the environment to begin building big data processing workflows with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (NumPy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level Python developers and data analysts looking to enhance their data analysis and manipulation skills using Pandas and NumPy.

Upon completing this training, participants will be able to:

• Set up a development environment comprising Python, Pandas, and NumPy.
• Develop a data analysis application utilizing Pandas and NumPy.
• Execute advanced data wrangling, sorting, and filtering operations.
• Perform aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

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.

This instructor-led live training, offered online or onsite, is tailored for developers aiming to utilize the FARM (FastAPI, React, and MongoDB) stack to construct dynamic, high-performance, and scalable web applications.

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

• Setting up the required development environment that integrates FastAPI, React, and MongoDB.
• Understanding the key concepts, features, and benefits of the FARM stack.
• Learning how to build REST APIs with FastAPI.
• Learning how to design interactive applications with React.
• Developing, testing, and deploying applications (front end and back end) using the FARM stack.

This instructor-led live training, conducted Italy (online or onsite), is designed for developers aiming to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater speed and ease.

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

• Set up the necessary development environment to create APIs using Python and FastAPI.
• Develop APIs more quickly and simply using the FastAPI library.
• Understand how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication features in APIs using FastAPI tools.
• Create container images and deploy web APIs to cloud servers.

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.

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.