Programming with Big Data in R Training Course

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimize code, and produce reproducible reports.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

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

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training in Nigeria (online or onsite) targets beginner-level data analysts who wish to utilize R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.

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

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

Data analytics is a vital instrument for modern business operations. This course prioritises the development of practical, hands-on data analysis skills. Our objective is to equip delegates with the ability to provide evidence-based answers to key questions: 

What has happened?

• processing and analyzing data
• producing informative data visualizations

What will happen?

• forecasting future performance
• evaluating forecasts

What should happen?

• turning data into evidence-based business decisions
• optimizing processes

This instructor-led, live training in Nigeria (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.

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

• Understand the basics of Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyze data from multiple sources effectively.

R is a widely used, open-source platform designed for statistical computing, data analytics, and graphics. This course introduces participants to the R programming language, covering its fundamental principles, key libraries, and advanced techniques. Students will engage in advanced data analytics and visualization exercises using real-world datasets.

Target Audience

Developers and data analysts

Duration

3 days

Course Format

Interactive lectures and practical hands-on sessions

Apache NiFi is an open-source, flow-based platform for data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between diverse systems, featuring a web-based user interface and granular control capabilities.

This instructor-led live training, available onsite or remotely, targets intermediate-level administrators and engineers aiming to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completing this training, participants will be equipped to:

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows originating from various sources and destinations.
• Implement logic for flow automation, routing, and transformation.
• Optimize performance, monitor operations, and resolve technical issues.

Course Format

• Interactive lectures accompanied by discussions on real-world architecture.
• Practical labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live lab environment.

Customization Options

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

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight 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 Nigeria (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.

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

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

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

The learning curve for Apache Spark can be steep at the beginning, requiring significant effort before seeing initial results. This course is designed to help you navigate those early challenges. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. You will also gain a solid understanding of executors and tasks. Aligning with best practices, the course places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Students will also learn the key differences between AWS EMR and AWS Glue, one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led live training in Nigeria, participants will learn how to use Python and Spark together to analyse big data while working through hands-on exercises.

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

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

This instructor-led, live training in Nigeria (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

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.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MLlib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Stratio serves as a comprehensive, data-driven platform that unifies big data capabilities, artificial intelligence, and governance into a single cohesive solution. Its Rocket and Intelligence modules empower organizations to conduct rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training session, available both online and onsite, is tailored for intermediate-level data professionals aiming to master the Rocket and Intelligence modules in Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and sophisticated data logic.

Upon completing this training, participants will be equipped to:

• Effectively navigate and utilize the Stratio platform, specifically its Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to manage data workflows and perform feature engineering.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations within PySpark.

Course Delivery Format

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

Options for Course Customization

• For tailored training requirements, please reach out to us to arrange a customized session.