Data Science Training in Ankara

This instructor-led, live training session in Ankara (online or on-site) is tailored for beginner-level professionals who want 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:

• Grasp 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 Ankara (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 Ankara (online or onsite) targets 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 Ankara (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 increasingly popular within the financial sector, embraced by leading investment banks and hedge funds to develop a diverse array of applications, from core trading platforms to risk management systems.

Through this instructor-led live training, participants will acquire the skills necessary to leverage Python for creating practical solutions to specific financial challenges.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and manage the optimal development tools for building financial applications in Python
• Choose and apply the most appropriate Python libraries and programming techniques to organize, visualize, and analyze financial data from various sources (such as CSV, Excel, databases, web APIs, etc.)
• Develop applications that address issues such as asset allocation, risk analysis, and investment performance
• Troubleshoot, integrate, deploy, and optimize Python-based applications

Audience

• Developers
• Analysts
• Quants

Course Format

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

Note

• While this training addresses key challenges faced by finance professionals, participants with specific topics, tools, or techniques they wish to explore further should contact us to arrange customized content.

This course offers a hands-on exploration of Data Science and AI through Python, empowering professionals with the expertise to analyze data, construct machine learning models, and implement AI-powered applications within business environments. The curriculum spans 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 ideally suited for beginners pursuing a Python data science certification and seeking career-focused analytics training.

KNIME Analytics Platform stands as a premier open-source solution for driving data-centric innovation. It empowers users to uncover hidden potential within their data, extract fresh insights, or forecast future trends. Featuring over 1000 modules, hundreds of ready-to-run examples, a robust suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course on KNIME Analytics Platform offers an excellent introduction for beginners, advanced users, and KNIME experts alike. Participants will learn to utilize the platform more effectively and master the creation of clear, comprehensive reports based on KNIME workflows.

Delivered as an instructor-led live training (available online or onsite), this program is designed for data professionals seeking to leverage KNIME to address complex business challenges.

The course is particularly targeted at individuals without programming experience who wish to utilize cutting-edge tools to implement analytics scenarios.

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

• Install and configure KNIME.
• Develop Data Science scenarios.
• Train, test, and validate models.
• Implement the end-to-end value chain for data science models.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Ankara (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.