Introduction to Data Science Training Course

This is a 5 day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python

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

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

• Configure Apache Airflow to orchestrate machine learning workflows.
• Automate tasks related to data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models via automated pipelines.
• Monitor and optimize machine learning workflows in production environments.

This instructor-led, live training in Argentina (online or onsite) is aimed at 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 Argentina (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.
• Conduct data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services such as S3, RDS, and Redshift.
• Use AWS Cloud9 for developing and deploying machine learning models.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led, live training in Argentina (online or onsite) is designed for beginner-level data scientists and IT professionals who want to learn the fundamentals of data science using Google Colab.

Upon completion 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 develop a practical, real-world grasp of Data Science, along with the associated technologies, methodologies, and tools.

Attendees will have the chance to apply what they learn through interactive, hands-on exercises. The course heavily incorporates group collaboration and direct feedback from the instructor.

The curriculum begins by covering foundational Data Science concepts before moving on to the specific tools and methodologies employed in the field.

Target Audience

• Software developers
• Technical analysts
• IT consultants

Course Format

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

Note

• For those interested in tailored training for this course, please reach out to us to make arrangements.

Big data refers to datasets so vast and intricate that conventional data processing software proves insufficient for handling them. Key challenges in big data encompass capturing, storing, analyzing, searching, sharing, transferring, visualizing, querying, updating, and ensuring information privacy.

This course is designed for marketing and sales professionals who wish to deepen their understanding of applying data science within these fields. The curriculum offers a comprehensive overview of various data science techniques applied to upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Distinguishing Marketing from Sales - What differentiates sales from marketing?

Simply put, sales focuses on individuals or small groups, whereas marketing targets larger audiences or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative solutions), and promotion (generating awareness through advertising) to generate leads and prospects. Once products reach the market, the sales team's role is to persuade these prospects to make a purchase. Essentially, sales converts leads into orders and purchases, focusing on short-term goals, while marketing aims for long-term brand building and customer engagement.

This instructor-led, live training in Argentina (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 Argentina (online or onsite) is tailored for data scientists and developers who aim to learn and develop their careers in Data Science using Kaggle.

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

• Gain insights into data science and machine learning concepts.
• Explore the field of data analytics.
• Understand Kaggle’s functionality and how to utilize it effectively.

KNIME Analytics Platform stands out as a premier open-source solution for data-driven innovation, empowering you to uncover hidden potential within your data, extract fresh insights, and predict future trends. Equipped with over 1,000 modules, numerous ready-to-run examples, a broad spectrum of integrated tools, and the most extensive selection of advanced algorithms, KNIME Analytics Platform serves as an ideal toolbox for both data scientists and business analysts.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to familiarize themselves with KNIME, learn how to utilize it more effectively, and develop clear, comprehensive reports based on KNIME workflows.

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

It is targeted at an audience that lacks programming experience but intends to use cutting-edge tools to implement analytics scenarios.

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

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

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This training is divided into three main sections. The first part covers the fundamentals of MATLAB, exploring its role as both a programming language and a computational platform. Key topics include MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

The second part demonstrates how to leverage MATLAB for data mining, machine learning, and predictive analytics. To highlight the advantages and capabilities of MATLAB, we compare its approach and power with other common tools such as spreadsheets, C, C++, and Visual Basic.

In the final part, participants will learn how to streamline their workflow by automating data processing and report generation.

Throughout the course, participants will reinforce their learning through hands-on exercises in a lab environment. By the end of the training, you will have a comprehensive understanding of MATLAB’s capabilities and be equipped to apply them to real-world data science challenges and automate your daily tasks.

Assessments will be conducted throughout the course to monitor your progress.

Course Format

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

Note

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

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level data analysts, developers, or aspiring data scientists who aim to leverage machine learning techniques in Python to extract insights, generate predictions, and automate data-driven decisions.

Upon completing this course, participants will be able to:

• Comprehend and distinguish between key machine learning paradigms.
• Explore 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 Argentina (available online or onsite) is designed for data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

Upon completing this training, participants will be able to:

• Configure the necessary environment to begin developing Pandas workflows at scale using Modin.
• Gain a clear understanding of Modin’s features, architecture, and advantages.
• Identify the key differences between Modin, Dask, and Ray.
• Execute Pandas operations more efficiently with the help of Modin.
• Implement the full range of Pandas API functions.

This instructor-led, live training in Argentina (online or onsite) is aimed at 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, cuML, etc.

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.