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 Mexico (online or onsite) targets 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:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led, live training in Mexico (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 Mexico (online or onsite) targets intermediate-level data scientists and analysts seeking to utilize AWS Cloud9 for optimized data science workflows.

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

• Establish a data science environment within 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 Mexico (online or onsite) 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.

Participants who complete this training will gain a practical, real-world understanding of Data Science and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Data Science, then progresses into the tools and methodologies used in Data Science.

Audience

• Developers
• Technical analysts
• IT consultants

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

Big data refers to datasets that are so large and complex that traditional data processing software cannot handle them effectively. Challenges associated with big data include data capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

This course is designed for Marketing and Sales professionals looking to deepen their understanding of how to apply data science within these fields. It offers comprehensive coverage of various data science techniques utilized for "upselling," "cross-selling," market segmentation, branding, and Customer Lifetime Value (CLV).

Distinction Between Marketing and Sales - What sets sales and marketing apart?

Simply put, sales focuses on individuals or small groups, whereas marketing targets a broader audience or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative offerings), and promotion (using advertisements to build awareness among consumers). Essentially, marketing aims to generate leads or prospects. Once a product reaches the market, the salesperson's role is to persuade these prospects to make a purchase. While marketing focuses on long-term goals, sales is concerned with converting leads into immediate purchases and orders.

This instructor-led, live training in Mexico (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 on 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.
• 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 Scala, Python, or R to write and execute code against big data systems like Apache Spark, all via the Jupyter interface.

This guided, live training in Mexico (online or on-site) is designed for data scientists and developers aiming to establish or grow their careers in Data Science using Kaggle.

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

• Gain insights into data science and machine learning principles.
• Investigate data analytics techniques.
• Understand Kaggle’s platform and its operational mechanisms.

KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation. It empowers users to uncover the latent potential within their data, extract new insights, and forecast future trends. With over 1,000 modules, hundreds of ready-to-execute examples, a broad array of integrated tools, and the most extensive selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for any data scientist or business analyst.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, experienced users, and KNIME specialists to familiarize themselves with KNIME, learn how to utilize it more efficiently, and develop clear, comprehensive reports based on 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 audiences who may not have programming experience but aim to utilize state-of-the-art tools to implement analytics scenarios.

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

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

Format of the Course

• Interactive lectures and discussions.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

The initial segment of this training focuses on the core principles of MATLAB, highlighting its role as both a programming language and a comprehensive platform. This section covers essential topics such as MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented programming concepts.

In the second segment, we illustrate how to leverage MATLAB for data mining, machine learning, and predictive analytics. To offer participants a clear and practical understanding of MATLAB's capabilities and advantages, we compare its usage with other common tools, including spreadsheets, C, C++, and Visual Basic.

The third segment of the training teaches participants how to streamline their workflows by automating data processing and report generation.

Throughout the course, participants will apply the concepts learned through hands-on exercises in a lab setting. By the end of the training, participants will have a comprehensive understanding of MATLAB's capabilities and will be equipped to use it for solving real-world data science problems and automating their work.

Assessments will be conducted throughout the course to monitor progress.

Course Format

• The course includes theoretical lessons and practical exercises, such as case studies, code inspection, and hands-on implementation.

Note

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

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

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 Mexico (online or on-site) is designed for data scientists and developers who want to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms such as XGBoost, cuML, and others.

Upon completing this training, participants will be able to:

• Configure the required development environment to construct data models using NVIDIA RAPIDS.
• Gain a comprehensive understanding of RAPIDS features, components, and benefits.
• Utilize GPUs to speed up end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL processes using cuDF and Apache Arrow.
• Learn to execute machine learning tasks using XGBoost and cuML algorithms.
• Create data visualizations and perform graph analysis with cuXfilter and cuGraph.