Jupyter for Data Science Teams Training Course

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

The training includes practical examples and exercises conducted in Python.

This instructor-led, live training in Serbia (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:

• 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 Serbia (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 Serbia (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 Serbia (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.

This course is an ideal introduction to data science for managers, providing an opportunity to learn about this powerful business instrument.

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

Learners will apply this knowledge through interactive exercises. The course heavily incorporates group interaction and direct feedback from the instructor.

The curriculum begins with fundamental Data Science concepts and advances to the specific tools and methods employed in the field.

Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Note

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

Big data consists of datasets that are so large and complex that conventional data processing software becomes insufficient. The challenges associated with big data encompass data capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

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

Understanding the Difference Between Marketing and Sales - How do sales and marketing differ?

In simple terms, sales focuses on individuals or small groups, whereas marketing targets larger audiences or the general public. Marketing involves research (identifying customer needs), product development (creating innovative solutions), and promotion (advertising to build awareness). Essentially, marketing generates leads or prospects. Once a product is in the market, the salesperson's role is to persuade these prospects to make a purchase. While sales aims to convert leads into orders, marketing focuses on long-term goals, whereas sales is often oriented toward shorter-term objectives.

This instructor-led live training, available either online or onsite, is designed for professionals looking to launch a career in Data Science.

Upon completing this training, participants will be capable of:

• Installing and setting up Python alongside MySQL.
• Gaining a clear understanding of Data Science and its potential to create value for virtually any business.
• Mastering the core principles of Python coding.
• Understanding and applying supervised and unsupervised Machine Learning techniques, including how to implement and interpret their outcomes.

Training Format

• Engaging lectures and interactive discussions.
• Abundant exercises and practical practice sessions.
• Real-world implementation within a live laboratory environment.

Customization Options

• For organizations seeking tailored training for this course, please reach out to us to discuss arrangements.

This instructor-led, live training Serbia (available online or onsite) targets data scientists and developers who aspire 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 data-driven innovation. It empowers users to uncover the hidden potential within their data, extract fresh insights, and predict future trends. Boasting over 1,000 modules, numerous ready-to-run examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolbox for any data scientist or business analyst.

This course offers beginners, advanced users, and KNIME experts alike an excellent opportunity to become familiar with KNIME, enhance their proficiency in using it, and learn how to generate clear, comprehensive reports based on KNIME workflows.

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

The program is specifically tailored for individuals who may not have a programming background but wish to utilize state-of-the-art tools to implement analytics scenarios.

Upon completion of this training, participants will be equipped 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 practical application.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• For customized training requests or further information about this program, please contact us to arrange.

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

In the second section, we demonstrate 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 power, we draw comparisons between using MATLAB and other tools such as spreadsheets, C, C++, and Visual Basic.

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

Throughout the course, participants will apply learned concepts through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

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

Note

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

This instructor-led, live training in Serbia (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 Serbia (online or onsite) targets data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, while 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.