Programming with Big Data in R Training Course

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

Upon completing this training, participants will be able to:

• Establish a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data within a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in Botswana (online or onsite) is aimed at beginner-level data analysts who wish to use 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 plays an essential role in modern business. This course concentrates on building practical, hands-on skills for data analysis. Our objective is to empower participants 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 Botswana (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 acclaimed, open-source environment designed for statistical computing, data analytics, and graphics. This course introduces learners to the R programming language, covering foundational language elements, essential libraries, and advanced concepts. Participants will engage in advanced data analytics and graphical representation using real-world datasets.

Target Audience

Software Developers and Data Analytics Professionals

Duration

3 days

Delivery Format

Lecture-based instruction combined with practical, hands-on exercises

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

This course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets effectively by applying distributed computing principles.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) is aimed at beginner to intermediate system administrators who wish to deploy, maintain, and optimise 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 initial learning curve for Apache Spark can be steep, requiring considerable effort before seeing tangible results. This course is designed to help you surmount that early hurdle. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, gain proficiency in both Python and Scala APIs, and comprehend the roles of executors and tasks. Furthermore, adhering to industry best practices, the course places strong emphasis on cloud deployment, with specific focus on Databricks and AWS. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark-related services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Botswana, participants will learn how to use Python and Spark together to analyze big data while working 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.

This instructor-led, live training in Botswana (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 is a unified platform that brings together big data, artificial intelligence, and governance. Through its Rocket and Intelligence modules, it facilitates swift data exploration, transformation, and advanced analytics for enterprise settings.

This instructor-led live training, available online or onsite, is designed for intermediate data professionals keen on leveraging the Rocket and Intelligence modules within Stratio using PySpark. The focus is on mastering looping structures, user-defined functions, and sophisticated data logic.

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

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

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Customisation Options

• To arrange a tailored training session for this course, please contact us.