R For Statistical Big Data Analysis Training Course: Big Data R Analytics

Introduction

Unlock the power of statistical analysis with our R for Statistical Big Data Analysis Training Course. This program is designed to equip you with the essential skills to use R for statistical analysis of Big Data, enabling you to extract meaningful insights from large datasets. In today's data-driven world, mastering R for Big Data is crucial for performing complex statistical modeling and analysis. Our R training course provides hands-on experience and expert guidance, empowering you to build robust and scalable data analysis solutions.

This Big Data R statistical analysis training delves into the core concepts of advanced R programming, covering topics such as distributed computing with R, advanced statistical modeling, and data visualization. You'll gain expertise in using industry-standard R packages and techniques to perform statistical analysis of Big Data, meeting the demands of modern data environments. Whether you're a data scientist, statistician, or data analyst, this R course will empower you to become a proficient data analyst.

Target Audience:

• Data Scientists
• Statisticians
• Data Analysts
• Researchers
• Data Engineers
• Business Analysts
• Anyone needing R for Big Data statistical analysis skills

Course 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.

Duration

10 Days

Course content

Module 1: Introduction to R for Statistical Big Data Analysis

• Fundamentals of R for Big Data.
• Overview of R packages for statistical Big Data analysis.
• Setting up an R Big Data analysis environment.
• Introduction to advanced R concepts and techniques.
• Best practices for R Big Data analysis.

Module 2: Distributed Computing with R

• Utilizing distributed computing frameworks (SparkR, dplyr with data.table).
• Implementing parallel processing in R.
• Designing and building distributed R applications.
• Optimizing R code for distributed environments.
• Best practices for distributed computing.

Module 3: Advanced Statistical Modeling in R

• Implementing advanced regression models.
• Utilizing time series analysis and forecasting.
• Implementing multivariate analysis and clustering.
• Designing and building statistical models for Big Data.
• Best practices for statistical modeling.

Module 4: Data Visualization for Big Data Analysis

• Utilizing advanced data visualization techniques in R.
• Implementing interactive dashboards and plots.
• Designing effective data visualizations for Big Data.
• Optimizing visualizations for large datasets.
• Best practices for data visualization.

Module 5: R Pipelines for Big Data

• Designing efficient R pipelines for Big Data.
• Utilizing R for data ingestion and transformation.
• Implementing data quality checks and validation.
• Automating R data pipelines.
• Best practices for R pipelines.

Module 6: Performance Optimization and Scalability

• Optimizing R code for performance.
• Utilizing profiling and benchmarking tools.
• Implementing parallel processing and concurrency.
• Designing scalable R applications.
• Best practices for performance optimization.

Module 7: Troubleshooting and Debugging

• Debugging R Big Data analysis applications.
• Analyzing performance and data issues.
• Utilizing debugging tools and techniques.
• Resolving common R Big Data problems.
• Best practices for troubleshooting.

Module 8: Data Security and Access Control

• Implementing data security in R environments.
• Utilizing authentication and authorization.
• Implementing data encryption and masking.
• Managing data permissions and privileges.
• Best practices for data security.

Module 9: Integration with Big Data Platforms

• Integrating R with various Big Data platforms.
• Utilizing data connectors and APIs.
• Implementing data transfer between R and Big Data systems.
• Best practices for integration.

Module 10: Monitoring and Maintenance

• Monitoring R Big Data systems.
• Implementing alerting and notifications.
• Utilizing monitoring tools and techniques.
• Managing R Big Data applications.
• Best practices for monitoring.

Module 11: Advanced R Patterns and Techniques

• Implementing advanced R patterns for Big Data.
• Utilizing R for building streaming applications.
• Implementing advanced statistical functions.
• Advanced techniques for R Big Data development.
• Best practices for advanced patterns.

Module 12: Real-World Use Cases

• Implementing R for customer analytics.
• Utilizing R for financial data analysis.
• Implementing R for log analysis.
• Utilizing R for real-time data analysis.
• Best practices for real world applications.

Module 13: R and Cloud Environments

• Deploying R data applications on cloud platforms.
• Utilizing cloud-based R libraries and services.
• Optimizing cloud resources for R applications.
• Best practices for cloud deployment.

Module 14: R and Data Governance

• Implementing data governance policies in R environments.
• Utilizing metadata management for R data.
• Implementing data lineage and data dictionary.
• Best practices for data governance.

Module 15: Future Trends in R for Big Data Analysis

• Emerging trends in R for Big Data.
• Utilizing AI and automation in R data pipelines.
• Implementing serverless R data applications.
• Best practices for future R development.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.