Jupyter for Data Science Teams Training Course

This five-day program provides an introductory overview of Data Science and Artificial Intelligence (AI).

The course is delivered through practical examples and exercises using Python.

This instructor-led, live training in Turkey (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, held in Turkey (online or onsite), is designed for data scientists who aim to utilize the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a consolidated 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 Turkey (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 Turkey (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.

Upon completing this training, participants will develop a practical, real-world grasp of Data Science, including its associated technologies, methodologies, and tools.

Learners will apply this knowledge through interactive, hands-on exercises. The course places significant emphasis on group collaboration and feedback from the instructor.

The curriculum begins by introducing fundamental Data Science concepts, then advances to explore the specific tools and methodologies employed in the field.

Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Note

• For customized training options, please contact us to arrange your schedule.

Big data refers to data sets so vast and complex that conventional data processing software falls short. Key challenges in big data encompass data capture, storage, analysis, searching, sharing, transfer, visualization, querying, updating, and ensuring information privacy.

This course is designed for Marketing and Sales professionals eager to deepen their understanding of data science applications within these fields. It offers comprehensive coverage of various data science techniques applied to upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).\n

The Distinction Between Marketing and Sales - How do these two disciplines differ?

Simply put, sales focuses on targeting individuals or small groups, while marketing aims at larger audiences or the general public. Marketing involves research (identifying customer needs), product development (creating innovative solutions), and promotion (through advertising to build consumer awareness). Essentially, marketing generates leads and prospects. Once a product is launched, the sales team's role is to persuade customers to make a purchase. Sales converts leads into orders, whereas marketing focuses on long-term goals compared to the shorter-term objectives of sales.

This guided, live training session (available online or at your location) is designed for professionals looking to launch a career in Data Science.

Upon completion, participants will be able to:

• Install and set up Python and MySql.
• Grasp the concept of Data Science and its potential to add value to virtually any industry.
• Master the basics of Python programming.
• Explore fundamental supervised and unsupervised Machine Learning techniques, learning how to implement them and interpret the outcomes.

Course Format

• Engaging lectures and interactive discussions.
• Extensive exercises and practice sessions.
• Practical application in a live-lab setting.

Customization Options

• For tailored training on this topic, please reach out to us to arrange your specific requirements.

This instructor-led live training in Turkey (online or on-site) is designed for data scientists and developers who wish to launch or grow 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 principles.
• Perform data analytics exploration.
• Understand the functionalities and operations of Kaggle.

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.

The initial segment of this training explores the core principles of MATLAB, highlighting its role as both a programming language and a comprehensive platform. This section introduces essential topics including MATLAB syntax, arrays and matrices, data visualization techniques, script creation, and fundamental object-oriented concepts.

In the second segment, the course illustrates how MATLAB can be utilized for data mining, machine learning, and predictive analytics. To offer participants a clear and practical understanding of MATLAB's capabilities and advantages, we compare its usage with other tools such as spreadsheets, C, C++, and Visual Basic.

During the final segment, participants will learn how to enhance their efficiency by automating data processing workflows and report generation processes.

Throughout the course, participants will apply their knowledge through practical exercises in a laboratory setting. By the conclusion of the training, participants will possess a deep understanding of MATLAB's features, enabling them to tackle real-world data science challenges and automate tasks to improve workflow efficiency.

Progress assessments will be integrated throughout the course to evaluate participant development.

Course Format

• The course combines theoretical instruction with practical exercises, featuring case study discussions, code analysis, and hands-on implementation.

Important Note

• Practice sessions will utilize pre-arranged sample data report templates. If you have specific customization needs, please contact us to make arrangements.

This instructor-led, live training in Turkey (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 Turkey (online or onsite) is designed for 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 Turkey (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 such as XGBoost, cuML, and others.

Upon completing this training, participants will be able to:

• Configure the required development environment to create data models using NVIDIA RAPIDS.
• Comprehend the features, components, and benefits of RAPIDS.
• Utilize GPUs to speed up end-to-end data and analytics pipelines.
• Execute GPU-accelerated data preparation and ETL processes with cuDF and Apache Arrow.
• Perform machine learning tasks using XGBoost and cuML algorithms.
• Create data visualizations and conduct graph analysis with cuXfilter and cuGraph.