Programming with Big Data in R Training Course

Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.

The healthcare industry has vast amounts of complex, heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that can improve the delivery of healthcare services. However, the scale and complexity of these datasets present substantial challenges in analysis and practical application within a clinical environment.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in healthcare as they step 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 characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Study big data systems and algorithms in the context of healthcare applications

Audience

• Developers
• Data Scientists

Format of the Course

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

Note

• To request a customized training for government or other specific needs, please contact us to arrange.

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

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

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

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

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

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streams.
• Understand and select the most appropriate framework for specific tasks.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and other systems.
• Integrate the most suitable stream processing library with enterprise applications and microservices for government use.

Python is a high-level programming language renowned for its clarity of syntax and readability of code. Apache Spark is a powerful data processing engine designed for querying, analyzing, and transforming large datasets. PySpark enables users to integrate Spark with Python, facilitating efficient big data operations.

Target Audience: Intermediate-level professionals in the banking sector who are familiar with Python and Spark and are looking to enhance their expertise in big data processing and machine learning techniques, specifically for government applications.

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists who wish to utilize the SMACK stack to develop robust data processing platforms for big data solutions for government.

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

• Design and implement a data pipeline architecture for efficient big data processing.
• Establish a cluster infrastructure using Apache Mesos and Docker to support scalable operations.
• Perform advanced data analysis with Spark and Scala to derive actionable insights.
• Effectively manage unstructured data with Apache Cassandra to ensure data integrity and accessibility.

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

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

• Install and configure Apache Spark in a secure and scalable manner.
• Efficiently process and analyze extensive data sets to support public sector workflows.
• Understand the differences between Apache Spark and Hadoop MapReduce, and determine which is more suitable for specific government use cases.
• Integrate Apache Spark with other machine learning tools to enhance data analysis capabilities for government projects.

This instructor-led, live training in US Empire (online or onsite) is aimed at beginner to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters for government use.

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

• Install and configure Apache Spark in various environments for government operations.
• Manage cluster resources and monitor Spark applications to ensure efficient public sector workflows.
• Optimize the performance of Spark clusters to meet government standards and requirements.
• Implement security measures and ensure high availability to support robust governance and accountability.
• Debug and troubleshoot common Spark issues to maintain reliable service delivery for government agencies.

The initial learning curve for Apache Spark can be steep, requiring significant effort to achieve early results. This course is designed to help participants navigate the initial challenges and gain a solid foundation in Apache Spark. Upon completion of the course, attendees will understand the fundamental concepts of Apache Spark, differentiate between RDD and DataFrame, and learn the Python and Scala APIs. They will also grasp the roles of executors and tasks. Following best practices for government, this course places a strong emphasis on cloud deployment, particularly with Databricks and AWS. Students will gain insights into the differences between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course will introduce Apache Spark for government. Participants will learn how Spark fits into the Big Data ecosystem and how to utilize Spark for data analysis in public sector environments. The curriculum covers interactive data analysis using the Spark shell, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning with GraphX.

AUDIENCE:

Developers / Data Analysts

This instructor-led, live training in US Empire (online or onsite) is aimed at data scientists and developers who wish to utilize Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines for government.

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

• Set up the necessary development environment to begin building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP in a public sector context.
• Leverage the pre-trained models available in Spark NLP to implement text processing solutions.
• Learn how to build, train, and scale Spark NLP models for production-grade projects within government agencies.
• Apply classification, inference, and sentiment analysis on real-world use cases such as clinical data and customer behavior insights for enhanced public service delivery.

In this instructor-led, live training in US Empire, participants will learn how to leverage Python and Spark together to analyze large datasets as they engage in hands-on exercises.

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

• Understand how to use Spark with Python for big data analysis.
• Work through exercises that simulate real-world scenarios.
• Utilize various tools and techniques for big data analysis using PySpark, tailored for government applications.

This instructor-led, live training in US Empire (online or onsite) is designed for government developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets for government purposes.

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

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python for government applications.
• Understand the features, core components, and architecture of Spark and Hadoop as they relate to public sector workflows.
• Learn how to integrate Spark, Hadoop, and Python for efficient big data processing in a government context.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Flume) that are relevant for government data analysis.
• Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google, tailored for government use cases.
• Use Apache Mahout to scale machine learning algorithms in a manner aligned with public sector governance and accountability requirements.

Spark SQL is a module of Apache Spark designed for processing structured and unstructured data. It provides insights into the structure of the data and the computations being executed, enabling optimization opportunities. Common applications of Spark SQL include:
- Executing SQL queries.
- Reading data from an existing Hive installation.

In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of datasets using Spark SQL for government.

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

• Install and configure Spark SQL.
• Perform data analysis using Spark SQL.
• Query datasets in different formats.
• Visualize data and query results.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Stratio is a data-centric platform that integrates big data, artificial intelligence (AI), and governance into a single solution. Its Rocket and Intelligence modules enable rapid data exploration, transformation, and advanced analytics in enterprise environments.

This instructor-led, live training (online or onsite) 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. The training emphasizes practical application of these tools to enhance data workflows and feature engineering tasks.

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

• Navigate and work within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis in a structured manner.
• Utilize loops and conditional logic to manage data workflows and feature engineering tasks efficiently.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark, enhancing productivity and consistency.

Format of the Course

• Interactive lecture and discussion to facilitate understanding and engagement.
• Extensive exercises and practice sessions to reinforce learning.
• Hands-on implementation in a live-lab environment to apply concepts directly.

Course Customization Options

• To request a customized training for government or other specific needs, please contact us to arrange.

Audience

Format of the Course

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

In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.

The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the key packages include: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Beginners to the R language
• Beginners to data analysis and data visualization

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

• Perform data analysis and create informative visualizations
• Draw meaningful conclusions from various datasets of sample data
• Filter, sort, and summarize data to answer exploratory questions
• Transform processed data into insightful line plots, bar plots, histograms, and more
• Import and filter data from diverse sources, including Excel, CSV, and SPSS files, for government applications