Jupyter for Data Science Teams Training Course

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

The course includes practical examples and exercises using Python.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 is an ideal introduction to data science for managers, giving you the chance to learn about this powerful business tool.

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

The participants will have the opportunity to apply this knowledge through hands-on exercises. Group interaction and instructor feedback are integral components of the course.

The course begins with an introduction to fundamental concepts in Data Science, then delves into the tools and methodologies used in the field.

Audience

• Developers
• Technical Analysts
• IT Consultants

Format of the Course

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

Note

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

Big data refers to extremely large and intricate datasets that exceed the capabilities of conventional data processing applications. The challenges associated with big data encompass various aspects such as data capture, storage, analysis, searching, sharing, transferring, visualizing, querying, updating, and ensuring information privacy.

This course is designed for Marketing and Sales Professionals who are looking to delve deeper into the application of data science in their respective fields. The course offers comprehensive coverage of various data science techniques used for upselling, cross-selling, market segmentation, branding, and customer lifetime value (CLV).

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

In simple terms, sales can be described as a process that focuses on individuals or small groups. On the other hand, marketing targets a broader audience or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative products), and promotion (through advertising) to raise awareness about the product among consumers. Essentially, marketing involves generating leads or prospects. Once the product is available in the market, it is the salesperson's role to persuade customers to make a purchase. While marketing aims for long-term goals, sales focus on achieving short-term objectives.

This instructor-led, live training (online or onsite) is designed for professionals who wish to embark on a career in Data Science.

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

• Install and configure Python and MySQL.
• Understand what Data Science entails and how it can bring value to virtually any business.
• Learn the basics of coding in Python.
• Explore supervised and unsupervised Machine Learning techniques, and understand how to implement them and interpret their results.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Uzbekistan (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 is a top-tier open-source solution for data-driven innovation, enabling you to uncover the hidden potential in your data, derive fresh insights, or forecast new possibilities. With over 1,000 modules, numerous ready-to-run examples, a broad array of integrated tools, and an extensive selection of advanced algorithms, KNIME Analytics Platform is the ideal toolkit for any data scientist and business analyst.

This course on KNIME Analytics Platform offers a valuable opportunity for beginners, intermediate users, and KNIME experts to familiarize themselves with KNIME, learn how to use it more effectively, and create clear, comprehensive reports based on KNIME workflows.

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

It is particularly suited for those with no programming background but who wish to use cutting-edge tools to implement advanced analytics scenarios.

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

• Install and configure KNIME.
• Develop data science scenarios.
• Train, test, and validate models.
• Implement the entire value chain of data science models from start to finish.

Format of the Course

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

Course Customization Options

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

In the initial segment of this training, we delve into the foundational aspects of MATLAB, exploring its role as both a programming language and an integrated platform. This section covers an introduction to MATLAB's syntax, arrays and matrices, data visualization techniques, script creation, and object-oriented programming principles.

During the second part, we showcase how MATLAB can be utilized for data mining, machine learning, and predictive analytics. To provide participants with a clear and practical understanding of MATLAB's capabilities, we compare its use to other tools such as spreadsheets, C, C++, and Visual Basic.

In the third segment, participants will learn techniques to streamline their work by automating data processing and report generation tasks.

Throughout the training, participants will apply the concepts learned through hands-on exercises in a laboratory setting. By the end of the course, participants will have a comprehensive understanding of MATLAB's features and will be able to use it effectively for solving real-world data science problems as well as automating their workflows.

Assessments will be conducted throughout the training to monitor progress.

Format of the Course

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

Note

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

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