Introduction to Data Science Training Course

This is a 5-day introductory course on Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python

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

Upon completion of 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 optimising machine learning workflows in production environments.

This instructor-led, live training in Portugal (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 Portugal (online or onsite) targets intermediate-level data scientists and analysts who wish to use AWS Cloud9 to streamline their data science workflows.

Upon completing this training, participants will be able to:

• Establish a data science environment in AWS Cloud9.
• Conduct data analysis using Python, R, and Jupyter Notebook within Cloud9.
• Integrate AWS Cloud9 with AWS data services such as S3, RDS, and Redshift.
• Utilize AWS Cloud9 for developing and deploying machine learning models.
• Optimize cloud-based workflows for efficient data analysis and processing.

This trainer-led, live training in Portugal (online or in-person) is designed for beginner-level data scientists and IT professionals who wish to learn the fundamentals 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.

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

Attendees will apply their new knowledge through hands-on exercises, with group interaction and instructor feedback forming a key part of the learning experience.

The course begins by introducing fundamental Data Science concepts, then advances to cover the specific tools and methodologies employed in the field.

Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Note

• For information on arranging customized training for this course, please get in touch.

Big data refers to data sets so voluminous and complex that traditional data processing application software is inadequate to handle them. Challenges in big data include capturing, storing, analyzing, searching, sharing, transferring, visualizing, querying, updating data, as well as ensuring information privacy.

This course is designed for marketing and sales professionals looking to deepen their understanding of data science applications within these fields. It offers a comprehensive exploration of various data science techniques utilized for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Differentiating Marketing and Sales - What distinguishes sales from marketing?

In simple terms, sales can be described as a process focused on individuals or small groups. Marketing, conversely, targets broader audiences or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative solutions), and promotion (via advertisements) to raise consumer awareness. Essentially, marketing generates leads or prospects. Once the product reaches the market, the salesperson's role is to persuade customers to make a purchase. Sales involves converting those leads into actual purchases and orders, whereas marketing aims for long-term goals, while sales focuses on shorter-term objectives.

This instructor-led, live training in Portugal (online or onsite) introduces the idea 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 walks participants through the creation of a sample data science project based on top of 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.
• Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboraton.
• Create, share and organize Jupyter Notebooks with team members.
• Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

This instructor-led live training in Portugal (online or onsite) 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.

The KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation. It empowers users to uncover latent potential within their data, extract novel insights, or forecast future trends. Boasting over 1,000 modules, numerous pre-built examples, a robust suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for any data scientist or business analyst.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME specialists alike to become familiar with KNIME, enhance their proficiency, and learn how to generate clear, comprehensive reports using KNIME workflows.

This instructor-led live training (available online or onsite) is designed for data professionals seeking to leverage KNIME to address complex business challenges.

It is specifically targeted at participants with no programming background who wish to utilize cutting-edge tools to implement analytics scenarios.

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

• Install and configure KNIME.
• Develop Data Science scenarios.
• Train, test, and validate models.
• Implement the end-to-end value chain of data science models.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation in a live laboratory environment.

Course Customization Options

• To request customized training for this course or to learn more about this programme, please contact us to arrange it.

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

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

In the final segment, participants learn how to optimize their workflows by automating data processing and report generation.

Throughout the course, participants will apply the concepts learned through practical exercises in a lab setting. By the end of the training, participants will have a comprehensive understanding of MATLAB's capabilities and will be able to use it to solve real-world data science problems and streamline their work through automation.

Progress assessments will be conducted throughout the course.

Course Format

• The course comprises theoretical and practical exercises, including case studies, sample code review, and hands-on implementation.

Note

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

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

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

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Portugal (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 Portugal (online or onsite) is designed for data scientists and developers looking to utilize RAPIDS for creating GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms such as XGBoost and cuML.

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

• Configure the required development environment for building data models with NVIDIA RAPIDS.
• Grasp the features, components, and benefits of RAPIDS.
• Utilize GPUs to accelerate end-to-end data and analytics pipelines.
• Execute GPU-accelerated data preparation and ETL processes using cuDF and Apache Arrow.
• Perform machine learning tasks using XGBoost and cuML algorithms.
• Create data visualizations and conduct graph analysis with cuXfilter and cuGraph.