15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of R for statistical Big Data analysis.
- Master distributed computing with R for large datasets.
- Utilize advanced statistical modeling techniques in R.
- Implement data visualization for Big Data analysis in R.
- Design and build efficient R pipelines for Big Data.
- Optimize R code for performance and scalability.
- Troubleshoot and debug R Big Data analysis applications.
- Implement data security and access control in R environments.
- Integrate R with various Big Data platforms.
- Understand how to monitor and maintain R Big Data systems.
- Explore advanced R patterns and techniques for Big Data.
- Apply real world use cases for R in Big Data statistical analysis.
- Leverage R for machine learning in Big Data contexts.
About the Course
This advanced training equips participants with practical skills to use R for statistical analysis of large and complex datasets. The course combines statistical methods, data management, visualization, and scalable analytical techniques to help professionals extract reliable insights from big data.
Participants will learn how to import, clean, transform, explore, visualize, and statistically model large datasets using R. The training covers descriptive and inferential statistics, regression, hypothesis testing, multivariate analysis, predictive modelling, time series, data visualization, and performance optimization. Emphasis is placed on applying R effectively to real-world business, research, financial, economic, and operational datasets.
Curriculum & Topics
15 Topics | 71 Sessions
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Workshop 1.1:
Fundamentals of R for Big Data.
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Workshop 1.2:
Overview of R packages for statistical Big Data analysis.
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Workshop 1.3:
Setting up an R Big Data analysis environment.
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Workshop 1.4:
Introduction to advanced R concepts and techniques.
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Workshop 1.5:
Best practices for R Big Data analysis.
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Workshop 2.1:
Utilizing distributed computing frameworks (SparkR, dplyr with data.table).
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Workshop 2.2:
Implementing parallel processing in R.
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Workshop 2.3:
Designing and building distributed R applications.
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Workshop 2.4:
Optimizing R code for distributed environments.
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Workshop 2.5:
Best practices for distributed computing.
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Workshop 3.1:
Implementing advanced regression models.
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Workshop 3.2:
Utilizing time series analysis and forecasting.
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Workshop 3.3:
Implementing multivariate analysis and clustering.
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Workshop 3.4:
Designing and building statistical models for Big Data.
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Workshop 3.5:
Best practices for statistical modeling.
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Workshop 4.1:
Utilizing advanced data visualization techniques in R.
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Workshop 4.2:
Implementing interactive dashboards and plots.
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Workshop 4.3:
Designing effective data visualizations for Big Data.
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Workshop 4.4:
Optimizing visualizations for large datasets.
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Workshop 4.5:
Best practices for data visualization.
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Workshop 5.1:
Designing efficient R pipelines for Big Data.
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Workshop 5.2:
Utilizing R for data ingestion and transformation.
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Workshop 5.3:
Implementing data quality checks and validation.
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Workshop 5.4:
Automating R data pipelines.
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Workshop 5.5:
Best practices for R pipelines.
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Workshop 6.1:
Optimizing R code for performance.
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Workshop 6.2:
Utilizing profiling and benchmarking tools.
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Workshop 6.3:
Implementing parallel processing and concurrency.
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Workshop 6.4:
Designing scalable R applications.
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Workshop 6.5:
Best practices for performance optimization.
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Workshop 7.1:
Debugging R Big Data analysis applications.
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Workshop 7.2:
Analyzing performance and data issues.
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Workshop 7.3:
Utilizing debugging tools and techniques.
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Workshop 7.4:
Resolving common R Big Data problems.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data security in R environments.
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Workshop 8.2:
Utilizing authentication and authorization.
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Workshop 8.3:
Implementing data encryption and masking.
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Workshop 8.4:
Managing data permissions and privileges.
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Workshop 8.5:
Best practices for data security.
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Workshop 9.1:
Integrating R with various Big Data platforms.
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Workshop 9.2:
Utilizing data connectors and APIs.
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Workshop 9.3:
Implementing data transfer between R and Big Data systems.
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Workshop 9.4:
Best practices for integration.
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Workshop 10.1:
Monitoring R Big Data systems.
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Workshop 10.2:
Implementing alerting and notifications.
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Workshop 10.3:
Utilizing monitoring tools and techniques.
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Workshop 10.4:
Managing R Big Data applications.
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Workshop 10.5:
Best practices for monitoring.
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Workshop 11.1:
Implementing advanced R patterns for Big Data.
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Workshop 11.2:
Utilizing R for building streaming applications.
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Workshop 11.3:
Implementing advanced statistical functions.
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Workshop 11.4:
Advanced techniques for R Big Data development.
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Workshop 11.5:
Best practices for advanced patterns.
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Workshop 12.1:
Implementing R for customer analytics.
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Workshop 12.2:
Utilizing R for financial data analysis.
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Workshop 12.3:
Implementing R for log analysis.
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Workshop 12.4:
Utilizing R for real-time data analysis.
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Workshop 12.5:
Best practices for real world applications.
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Workshop 13.1:
Deploying R data applications on cloud platforms.
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Workshop 13.2:
Utilizing cloud-based R libraries and services.
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Workshop 13.3:
Optimizing cloud resources for R applications.
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Workshop 13.4:
Best practices for cloud deployment.
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Workshop 14.1:
Implementing data governance policies in R environments.
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Workshop 14.2:
Utilizing metadata management for R data.
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Workshop 14.3:
Implementing data lineage and data dictionary.
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Workshop 14.4:
Best practices for data governance.
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Workshop 15.1:
Emerging trends in R for Big Data.
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Workshop 15.2:
Utilizing AI and automation in R data pipelines.
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Workshop 15.3:
Implementing serverless R data applications.
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Workshop 15.4:
Best practices for future R development.
