SMACK Stack for Data Science Training Course

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

The training includes practical examples and exercises conducted in Python.

This instructor-led, live training in Serbia (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 Serbia (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 Serbia (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 Serbia (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 course is an ideal introduction to data science for managers, providing an opportunity to learn about this powerful business instrument.

By 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 exercises. The course heavily incorporates group interaction and direct feedback from the instructor.

The curriculum begins with fundamental Data Science concepts and advances to the specific tools and methods employed in the field.

Audience

• Developers
• Technical analysts
• IT consultants

Course Format

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

Note

• To arrange customized training for this course, please contact us.

Big data consists of datasets that are so large and complex that conventional data processing software becomes insufficient. The challenges associated with big data encompass data capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

This course is designed for marketing and sales professionals seeking to deepen their understanding of how to apply data science 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).

Understanding the Difference Between Marketing and Sales - How do sales and marketing differ?

In simple terms, sales focuses on individuals or small groups, whereas marketing targets larger audiences or the general public. Marketing involves research (identifying customer needs), product development (creating innovative solutions), and promotion (advertising to build awareness). Essentially, marketing generates leads or prospects. Once a product is in the market, the salesperson's role is to persuade these prospects to make a purchase. While sales aims to convert leads into orders, marketing focuses on long-term goals, whereas sales is often oriented toward shorter-term objectives.

This instructor-led, live training Serbia (available online or onsite) targets data scientists and developers who aspire 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.

This instructor-led, live training in Serbia (online or onsite) is aimed at 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 course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This instructor-led live training in Serbia (online or onsite) targets data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms such as XGBoost and cuML.

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

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

In this instructor-led, live training in Serbia, participants will learn how to combine Python and Spark to analyze big data while engaging in hands-on exercises.

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

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

Stratio is a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics, making it ideal for enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals eager to master the Rocket and Intelligence modules within the Stratio ecosystem using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and complex data logic.

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

• Efficiently navigate and operate within the Stratio platform, utilizing both Rocket and Intelligence modules.
• Apply PySpark effectively for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to orchestrate data workflows and execute feature engineering.
• Develop and manage user-defined functions (UDFs) to create reusable data operations in PySpark.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training tailored to your specific needs, please reach out to us to make arrangements.