Introduction to Data Science Training Course

This five-day program serves as a comprehensive introduction to Data Science and Artificial Intelligence (AI).

The course is taught through practical examples and hands-on exercises utilizing Python.

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

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

• Configure Apache Airflow specifically for orchestrating 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 the use of automated pipelines.
• Monitor and optimize machine learning workflows within production environments.

This instructor-led, live training in Bulgaria (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 Bulgaria (online or onsite) is aimed at 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 Bulgaria (online or on-site) is designed for novice data scientists and IT professionals eager to grasp the fundamentals of data science utilizing Google Colab.

Upon completing this training, participants will be equipped to:

• Configure and navigate Google Colab.
• Write and run foundational Python code.
• Import and manage datasets.
• Generate visualizations using Python libraries.

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

Attendees will have the chance to apply this knowledge through hands-on exercises. The class heavily emphasizes group interaction and direct feedback from the instructor.

The course begins with an overview of fundamental Data Science concepts before advancing to the specific tools and methodologies employed in the field.

Audience

• Developers
• Technical analysts
• IT consultants

Format of the Course

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

Note

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

Big data refers to datasets that are so large and complex that traditional data processing applications are insufficient to manage them. Key challenges in big data encompass data capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

Designed specifically for marketing and sales professionals aiming to deepen their understanding of data science applications in these fields, this course offers a comprehensive exploration of various data science techniques. Key areas covered include upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

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

In simple terms, sales is a process focused on targeting individuals or small groups, whereas marketing aims at a broader audience or the general public. Marketing encompasses research to identify customer needs, product development for innovation, and promotion through advertisements to create brand awareness. Essentially, marketing is about generating leads or prospects. Once a product is available in the market, the salesperson's role is to persuade these prospects to make a purchase. While marketing focuses on long-term strategies, sales is concerned with short-term goals, specifically converting leads into orders.

This instructor-led, live training in Bulgaria (online or onsite) introduces the concept of collaborative development in data science and demonstrates how to utilize Jupyter to track and engage 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, multiuser mode, and more 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 such as Apache Spark, all via the Jupyter interface.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at 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 as a premier open-source solution for driving data-driven innovation. It empowers users to uncover hidden potential within their data, extract fresh insights, and predict future trends. Equipped with over 1000 modules, hundreds of ready-to-execute examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms available, KNIME Analytics Platform serves as the ideal toolkit 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 become familiar with the platform, 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 aiming to leverage KNIME to address complex business challenges.

The program targets participants who may not have programming expertise but wish to utilize cutting-edge tools to implement analytics scenarios.

Upon completing 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 lecture and discussion.
• Extensive exercises and practical practice.
• 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 arrange.

In the initial phase of this training, we explore the core concepts of MATLAB, examining its role as both a programming language and a development platform. Key topics include an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

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

In the third section, participants learn how to optimize their workflow by automating data processing and report generation.

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

Assessments will be conducted throughout the course to monitor progress.

Format of the Course

• The course includes theoretical and practical exercises, including case discussions, sample code inspection, and hands-on implementation.

Note

• Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange.

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

Upon completion of 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 Bulgaria (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 Bulgaria (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.