Data Science Training in Transylvania

This instructor-led, live training in Transylvania (online or on-site) is designed for 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.

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

• Comprehend the concept and advantages of pre-trained models.
• Explore different pre-trained model architectures and their specific use cases.
• Fine-tune a pre-trained model to suit particular tasks.
• Integrate pre-trained models into simple machine learning projects.

This instructor-led, live training in Transylvania (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 Transylvania (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 Transylvania (online or on-site) is designed for 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 instructor-led, live training in Transylvania (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 the creation of 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.
• Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from Scala, Python, or R to write and execute code against big data systems like Apache Spark, all through the Jupyter interface.

This instructor-led, live training in Transylvania (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.

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

In this instructor-led live training, participants will learn how to leverage Python to create practical solutions for a variety of 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 optimal development tools for building financial applications in Python
• Choose and employ the most appropriate Python libraries and programming techniques to organize, visualize, and analyze financial data from multiple sources (CSV, Excel, databases, web, etc.)
• Develop applications that address issues such as asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Format of the course

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

Note

• This training is designed to offer solutions for key challenges faced by finance professionals. If you have a specific topic, tool, or technique you would like to include or expand upon, please contact us to arrange.

Explores practical methodologies for Data Science and AI using Python, empowering professionals with the expertise to analyze data, develop machine learning models, and implement AI-powered applications within business environments. Topics include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. This course is ideal for beginners pursuing a Python data science certification and career-focused analytics training.

This instructor-led live training Transylvania (online or on-site) is targeted at data scientists who wish to utilize the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows within 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 course is designed for marketing and sales professionals aiming to deepen their understanding of data science applications within these fields. It offers a comprehensive exploration of various data science techniques utilized for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Differentiating Marketing and Sales - What sets them apart?

In simple terms, sales focuses on individuals or small groups, whereas marketing targets broader audiences or the general public. Marketing involves researching customer needs, developing innovative products, promoting them through advertising, and building awareness. Essentially, marketing generates leads or prospects. Once the product is launched, the sales team's role is to persuade these prospects to make a purchase. Sales converts leads into orders, focusing on shorter-term goals, while marketing emphasizes long-term strategy and brand building.

KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation, enabling you to uncover hidden potential within your data, extract new insights, or forecast future trends. Boasting over 1000 modules, numerous ready-to-execute examples, a broad spectrum of integrated tools, and an extensive 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 opportunity for beginners, advanced users, and KNIME experts to become familiar with the platform, learn to utilize it more efficiently, and master the creation of clear, comprehensive reports based on KNIME workflows.

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

The training targets individuals with no programming background who wish to utilize state-of-the-art 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 for data science models

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation in a live laboratory environment.

Course Customization Options

• To request customized training for this course or to obtain more information about this program, please contact us to arrange details.

This instructor-led, live training in Transylvania (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.

This instructor-led live training in Transylvania (online or onsite) is designed for data scientists and developers who want to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms like XGBoost and cuML.

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

• Set up the required 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 using XGBoost and cuML algorithms.
• Build data visualizations and execute graph analysis using cuXfilter and cuGraph.