Data Science Training in Rio de Janeiro

This instructor-led, live training in Rio de Janeiro (online or onsite) is aimed at 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.

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

• Comprehend the concept and advantages of pre-trained models.
• Investigate various pre-trained model architectures and their respective use cases.
• Fine-tune a pre-trained model for specific tasks.
• Integrate pre-trained models into straightforward machine learning projects.

This instructor-led, live training in Rio de Janeiro (online or onsite) is designed for 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 Rio de Janeiro (online or onsite) is tailored for intermediate-level professionals seeking to automate and manage machine learning workflows, encompassing model training, validation, and deployment via Apache Airflow.

Upon completing this training, participants will be equipped to:

• Configure Apache Airflow to orchestrate machine learning workflows.
• Automate essential tasks such as data preprocessing, model training, and validation.
• Seamlessly integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows within production environments.

This instructor-led live training in Rio de Janeiro (online or onsite) is aimed at 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 Rio de Janeiro (online or onsite) 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". It guides participants through the creation of a sample data science project built upon the Jupyter ecosystem.

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

• Install and configure Jupyter, including creating and integrating a team repository on Git.
• Leverage Jupyter features such as extensions, interactive widgets, and multiuser mode to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from languages such as 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 Rio de Janeiro (online or on-site) 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.

Python has become a highly favored programming language within the financial sector. Utilized by major investment banks and hedge funds, it powers a diverse array of financial applications, from core trading systems to risk management solutions.

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

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of the Python programming language
• Download, install, and maintain the optimal development tools for building financial applications in Python
• Choose and employ the most effective Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (such as CSV, Excel, databases, and web APIs)
• Develop applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Course Format

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

Note

• This training focuses on providing solutions for key challenges faced by finance professionals. If there is a specific topic, tool, or technique you wish to include or explore in greater depth, please contact us to arrange it.

This course delves into practical methodologies for Data Science and AI utilizing Python, empowering professionals with the expertise to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. It encompasses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is an ideal choice for beginners aiming for a Python data science certification and seeking career-ready analytics training.

This instructor-led, live training in Rio de Janeiro (online or onsite) targets data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in 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.

Designed specifically for marketing and sales professionals eager to deepen their understanding of data science applications in these fields, this course offers comprehensive coverage of various techniques used for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

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

In straightforward terms, sales is a process focused on targeting individuals or small groups. Marketing, conversely, aims at a broader audience or the general public. Marketing involves identifying customer needs through research, developing innovative products, and promoting them to create awareness. Essentially, marketing generates leads or prospects. Once a product reaches the market, the salesperson's role is to persuade these prospects to make a purchase. Sales focuses on converting leads into orders and purchases, while marketing is oriented toward longer-term goals compared to the short-term objectives of sales.

KNIME Analytics Platform stands out as a premier open-source solution for driving data-led innovation. It empowers you to uncover hidden value within your data, extract new insights, or forecast future trends. Equipped with over 1,000 modules, numerous pre-configured examples, a broad suite of integrated tools, and the most extensive selection of advanced algorithms, KNIME Analytics Platform serves as an essential toolkit for both data scientists and business analysts.

This course on KNIME Analytics Platform offers an excellent entry point for beginners, as well as opportunities for advanced users and experts to deepen their understanding. Participants will learn to utilize KNIME more efficiently and master the creation of clear, thorough reports built on KNIME workflows.

This instructor-led live training, available both online and onsite, is designed for data professionals looking to leverage KNIME to address complex business challenges.

The program is particularly suited for individuals without programming backgrounds who wish to utilize state-of-the-art tools to implement analytical scenarios.

Upon completing this training, participants will be able to:

• Install and set up KNIME.
• Develop Data Science scenarios.
• Train, test, and validate models.
• Implement the complete end-to-end data science model lifecycle.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within 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 Rio de Janeiro (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who aim to utilize machine learning techniques in Python to extract insights, generate predictions, and automate data-driven decisions.

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

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

This instructor-led live training in Rio de Janeiro (online or onsite) is designed for data scientists and developers who wish 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.