Introduction to Data Science Training Course

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

Learning is reinforced through practical examples and exercises conducted using Python.

This instructor-led, live training in Brazil (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 Brazil (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.

This instructor-led, live training in Brazil (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 Brazil (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.

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

Attendees 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 an overview of fundamental Data Science concepts, then advances to the specific tools and methodologies utilized in the field.

Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Note

• To request customized training for this course, please contact us to arrange.

Big data refers to datasets so massive and intricate that conventional software tools fall short in handling them effectively. Challenges associated with big data encompass data capture, storage, analysis, search capabilities, sharing mechanisms, transfer processes, visualization techniques, querying methods, update procedures, 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 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.

This instructor-led, live training in Brazil (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 Brazil (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.

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.

In the initial segment of this training, we explore the core fundamentals of MATLAB, highlighting its role as both a programming language and a comprehensive platform. This section covers an introduction to MATLAB syntax, arrays and matrices, data visualization techniques, script development, and object-oriented principles.

The second segment demonstrates how to leverage MATLAB for data mining, machine learning, and predictive analytics. To give participants a clear and practical understanding of MATLAB's approach and capabilities, we compare its usage with other common tools such as spreadsheets, C, C++, and Visual Basic.

During the third segment, participants will learn how to streamline their workflows by automating data processing and report generation tasks.

Throughout the course, participants will apply the concepts learned through hands-on exercises within a lab environment. By the conclusion of the training, participants will possess a thorough understanding of MATLAB's capabilities and will be able to utilize it to solve real-world data science problems and automate their work processes.

Assessments will be conducted throughout the course to monitor progress.

Course Format

• The course comprises both theoretical lessons and practical exercises, including case discussions, sample code reviews, and hands-on implementation.

Note

• Practice sessions will utilize pre-arranged sample data report templates. If you have specific requirements, please contact us to make arrangements.

This instructor-led, live training in Brazil (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 Brazil (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 Brazil (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.