Introduction to Data Science Training Course

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

Instruction is delivered through practical examples and exercises using Python.

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for 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 such as data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in production environments.

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.

This training equips participants with a hands-on, real-world understanding of Data Science, its methodologies, tools, and related technologies.

Through practical exercises, participants will apply their knowledge, supported by group discussions and instructor feedback, which are integral to the learning experience.

The course begins with foundational Data Science concepts and gradually explores the tools and techniques 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

• For customized training options, please contact us to make arrangements.

Big data refers to data sets that are so vast and complex that traditional data processing application software are insufficient to handle them. Key challenges associated with big data include data capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

Designed specifically for Marketing and Sales professionals eager to deepen their understanding of data science applications in these fields, this course offers a comprehensive exploration of techniques utilized for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Understanding the Distinction Between Marketing and Sales - What sets these two disciplines apart?

Simply put, sales is a process focused on targeting individuals or small groups. Marketing, conversely, aims at a broader audience or the general public. The marketing process encompasses research to identify customer needs, product development to create innovative offerings, and promotional activities such as advertising to build brand awareness. Ultimately, marketing generates 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 geared toward immediate conversions of leads into orders.

This instructor-led, live training in Uzbekistan (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 creating 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 setting up and integrating a team repository on Git.
• Utilise Jupyter features such as extensions, interactive widgets, multi-user mode, and more to enable project collaboration.
• Create, share, and organise 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 through the Jupyter interface.

This instructor-led, live training in Uzbekistan (available online or on-site) is tailored for data scientists and developers who aim to learn and advance their careers in Data Science using Kaggle.

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

• Gain a solid understanding of data science and machine learning.
• Explore key concepts in data analytics.
• Learn about Kaggle and how it operates.

The KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation. It empowers users to uncover hidden potential within their data, extract novel insights, or forecast future trends. Featuring over 1000 modules, numerous pre-configured examples, a robust suite of integrated tools, and an extensive array of advanced algorithms, the KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, experienced users, and KNIME specialists to familiarize themselves with the platform, master its effective use, and learn how to generate clear, thorough 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 course is tailored for an audience without programming experience who wish to utilize cutting-edge tools to implement analytics scenarios.

Upon completion of this training, participants will be capable of:

• Installing and configuring KNIME.
• Developing Data Science scenarios
• Training, testing, and validating models
• Implementing the end-to-end value chain of data science models

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Practical implementation in a live laboratory 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. Key topics include an introduction to MATLAB syntax, arrays and matrices, data visualization techniques, script development, and object-oriented programming concepts.

In the second segment, we illustrate how MATLAB can be leveraged for data mining, machine learning, and predictive analytics. To help participants clearly understand MATLAB’s capabilities and advantages, we compare its usage with other tools such as spreadsheets, C, C++, and Visual Basic.

The third segment teaches participants how to optimize their workflows by automating data processing and report generation tasks.

Throughout the course, participants will apply the concepts learned through hands-on exercises in a lab setting. By the conclusion of the training, participants will have a comprehensive understanding of MATLAB’s features and will be equipped to use it for solving real-world data science challenges and streamlining their work via automation.

Assessments will be integrated throughout the course to monitor progress.

Course Format

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

Note

• Practice sessions utilize pre-arranged sample data report templates. If you have specific requirements, please contact us to arrange custom materials.

This instructor-led, live training in Uzbekistan (available online or on-site) is tailored for data scientists and developers who wish to utilize Modin to build and implement parallel computations with Pandas, thereby achieving faster data analysis.

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

• Set up the necessary environment to begin developing scalable Pandas workflows with Modin.
• Understand the features, architecture, and advantages of Modin.
• Distinguish between Modin, Dask, and Ray.
• Execute Pandas operations more rapidly using Modin.
• Implement the full Pandas API and its functions.

This instructor-led, live training in Uzbekistan (available online or on-site) is intended for data scientists and developers who wish to leverage RAPIDS to build GPU-accelerated data pipelines, workflows, and visualisations, applying machine learning algorithms such as XGBoost, cuML, and others.

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

• Set up the required development environment to build data models using NVIDIA RAPIDS.
• Understand the key features, components, and advantages of the RAPIDS framework.
• Harness the power of GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL processes using cuDF and Apache Arrow.
• Learn how to carry out machine learning tasks using XGBoost and cuML algorithms.
• Create data visualisations and perform graph analysis using cuXfilter and cuGraph.