From Data to Decision with Big Data and Predictive Analytics Training Course

Big data analytics involves the process of examining large and diverse data sets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare industry is inundated with vast amounts of complex and varied medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that can enhance the delivery of healthcare. However, the sheer size and complexity of these datasets present substantial challenges in analysis and practical application within a clinical setting.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in healthcare through a series of hands-on, live-lab exercises.

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

• Install and configure big data analytics tools such as Hadoop MapReduce and Spark
• Understand the unique characteristics of medical data
• Apply big data techniques to manage and analyze medical data effectively
• Examine big data systems and algorithms in the context of healthcare applications

Audience

• Developers
• Data Scientists

Format of the Course

• Part lecture, part discussion, with exercises and extensive hands-on practice.

Note

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

• Grasp the significance of ethics in AI development and deployment.

• Acquire the skills to identify and address ethical risks related to AI.

• Gain a deeper understanding of regulatory and compliance requirements.

• Formulate strategies for promoting an ethical AI culture within your organisation.

Course Format

• Engaging lectures and interactive discussions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab setting.

Course Customisation Options

• For a tailored training experience, please contact us to discuss your specific needs.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level developers who wish to build AI-powered applications using predictive analytics and generative models.

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

• Understand the fundamentals of predictive AI and generative models.
• Utilize AI-powered tools for predictive coding, forecasting, and automation.
• Implement LLMs (Large Language Models) and transformers for text and code generation.
• Apply time-series forecasting and AI-based recommendations.
• Develop and fine-tune AI models for real-world applications.
• Evaluate ethical considerations and best practices in AI deployment.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.

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

• Install and configure Apache Hadoop.
• Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
• Set up HDFS to operate as storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
• Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.

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

• Understand the core concepts of Predictive AI and its applications.
• Collect, clean, and preprocess data for predictive analysis.
• Explore and visualize data to uncover insights.
• Build basic statistical models to make predictions.
• Evaluate the performance of predictive models.
• Apply Predictive AI concepts to real-world scenarios.

In this instructor-led, live training in Uzbekistan (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

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

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process of data continuously, concurrently, and in a record-by-record fashion.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level DevOps professionals who wish to integrate predictive AI into their DevOps practices.

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

• Implement predictive analytics models to forecast and solve challenges in the DevOps pipeline.
• Utilize AI-driven tools for enhanced monitoring and operations.
• Apply machine learning techniques to improve software delivery workflows.
• Design AI strategies for proactive issue resolution and optimization.
• Navigate the ethical considerations of using AI in DevOps.

Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.

Target Audience: Intermediate-level professionals in the banking industry familiar with Python and Spark, seeking to deepen their skills in big data processing and machine learning.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.

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

• Implement a data pipeline architecture for processing big data.
• Develop a cluster infrastructure with Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.

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

• Install and configure Apache Spark.
• Quickly process and analyze very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

The learning curve for Apache Spark starts off gradually but requires significant effort to achieve initial results. This course is designed to help participants navigate through the challenging early stages. By the end of the course, participants will have a solid understanding of the basics of Apache Spark, including the differences between RDD and DataFrame. They will also learn the Python and Scala APIs, gain insight into executors and tasks, and more. Following best practices, the course places a strong emphasis on cloud deployment, particularly using Databricks and AWS. Students will also understand the distinctions between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineer, DevOps, Data Scientist

OBJECTIVE:

This course aims to introduce Apache Spark. Students will gain an understanding of how Spark integrates into the Big Data ecosystem and learn to utilize Spark for data analysis. The curriculum includes interactive data analysis using the Spark shell, an exploration of Spark's internal mechanisms, its APIs, Spark SQL, Spark streaming, and applications in machine learning and graphX.

AUDIENCE :

Developers / Data Analysts

In this instructor-led, live training in Uzbekistan, participants will learn how to use Python and Spark together to analyze big data as they work on 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.

Spark SQL is a module of Apache Spark designed for handling both structured and unstructured data. It provides insights into the structure of the data and the computations being executed, which can be leveraged for optimization. Two primary applications of Spark SQL include:
- Running SQL queries.
- Accessing data from an existing Hive installation.

In this instructor-led, live training (conducted either on-site or remotely), participants will learn how to analyze various types of datasets using Spark SQL.

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

• Install and configure Spark SQL effectively.
• Conduct data analysis with Spark SQL.
• Query datasets in different formats.
• Visualize data and query results for better insights.

Format of the Course

• Interactive lectures and discussions.
• Plenty of exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Stratio is a data-centric platform that integrates big data, AI, and governance into a single solution. The Rocket and Intelligence modules of Stratio facilitate rapid data exploration, transformation, and advanced analytics in enterprise settings.

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level data professionals who wish to effectively utilize the Rocket and Intelligence modules in Stratio with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

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

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Use loops and conditional logic to manage data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

• Interactive lecture and discussion sessions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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