Data Science Training in Tashkent

This instructor-led, live training in Tashkent (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Tashkent (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 Tashkent (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 Tashkent (online or on-site) is designed to help participants prepare for the Microsoft Certified: Data Scientist Associate certification exam (DP-100).

The course covers the fundamentals of data science and machine learning, along with their implementation using Microsoft Azure Machine Learning. Participants will learn how to prepare data, build and train machine learning models, and deploy these models to the cloud. Additionally, the course addresses how to evaluate model performance and perform feature engineering.

This instructor-led, live training in Tashkent (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.

Python has become a highly popular programming language within the financial sector. It is adopted by major investment banks and hedge funds to develop a diverse array of financial applications, from core trading systems to risk management tools.

In this instructor-led live training, participants will learn to leverage Python for creating practical applications that address specific finance-related challenges.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and maintain the most effective development tools for building financial applications in Python
• Select and apply appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (such as CSV, Excel, databases, and web APIs)
• Develop applications that address issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Format of the course

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

Note

• This training aims to provide solutions for key problems faced by finance professionals. If you have specific topics, tools, or techniques you wish to explore further, please contact us to arrange custom content.

This course offers a hands-on exploration of Data Science and AI applications via Python. It empowers professionals with the essential skills to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. Topics include CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with Tensorflow, natural language processing, big data processing with Spark, and data-driven storytelling. It is ideal for beginners looking to obtain a Python data science certification and receive career-focused analytics training.

This is an ideal introduction to data science for managers, giving you the chance to learn about this powerful business tool.

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.

This instructor-led, live training in Tashkent (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.