This course is ideal for:
Cesar’s course is rated: Excellent
4.6/5
4.8 out of 5 based on over 3.8k+ reviews
Master Data Analysis
with R Comprehensive Training
Take your data analysis skills to the next level with our R Software for Data Analysis course.
Whether you're a researcher, data analyst, or postgraduate student, this course will equip you with skills to harness
the power of R, the leading language for data science.
3.8k+
Why Learn R?
R provides an extensive range of tools for data analysis, including linear and nonlinear modeling, time-series forecasting, clustering, and more. With Base R and an expansive library of user-contributed packages, R covers everything from data wrangling to sophisticated statistical analysis, making it a comprehensive solution for data professionals.
One of R’s standout features is its ability to generate publication-quality visualizations, complete with mathematical symbols, annotations, and formulas, allowing users to present their data with precision and clarity. Its flexibility, coupled with a vibrant community that continuously develops new packages and tools, ensures that R remains at the forefront of data science innovation.
R is a versatile and powerful open-source programming language specifically designed for statistical computing and data visualization.
Master Data
Analysis with R -
Comprehensive Training
Take your data analysis skills to the next level with our R Software for Data Analysis course.
Whether you're a researcher, data analyst, or postgraduate student, this course will equip you with skills to harness the power of R, the leading language for data science.
Why Learn R?
R is a versatile and powerful open-source programming language specifically designed for statistical computing and data visualization. It offers a comprehensive range of tools for data analysis, including linear and nonlinear modeling, time-series forecasting, clustering, and machine learning. With Base R and an extensive ecosystem of user-contributed packages, R enables users to handle everything from data manipulation and transformation to cutting-edge statistical analysis.
One of R’s standout features is its ability to generate publication-quality visualizations, complete with mathematical symbols, annotations, and formulas, allowing users to present their data with precision and clarity.
Its flexibility and power have made R the preferred choice for data scientists, statisticians, and researchers across industries like finance, healthcare, academia, and technology.
This course is ideal for anyone looking to build a strong foundation in R programming and data analysis, whether you are a beginner or have some prior experience.
By the end of the program, you’ll be able to handle real-world datasets, perform statistical analyses, and contribute effectively to research and data-driven projects.
- Dates
- Status
- Training Format
- Online Training
- In-Person Training
- Learning Track
- Oct 6 - 11, 2024
- Accepting Applications
- Online & In-Person
- R9,900.00 Excl. VAT
- R13,000.00 Excl. VAT
- Certificate of Completion
The 5 days program fee for 'In-person Training' covers tuition and program materials (course lecture notes and training manual). It includes a welcome tea, full refreshments (lunch), two breaks for tea with pastries, complimentary parking, and a certificate of attendance.
The 4 weeks programme for online training provides for:
- Pre-course evaluation
- Course notes
- Course brochure
- Installation of software
- Post-course evaluation
Cesar’s course is rated: Excellent
4.6/5
4.8 out of 5 based on over 3.8k+ reviews
Dates
Nov 18 – 22, 2024
Status
Accepting Applications
Training Format
In-Person & Online
5 Days In-Person Training
R13,000.00 Excl. VAT
3 Weeks Online Training
R9,900.00 Excl. VAT
Learning Track
Certificate of Completion
Course Overview
This course is designed to equip participants with a comprehensive understanding of R programming for data analysis, covering both fundamental and intermediate concepts. Throughout the course, participants will engage in hands-on projects that delve into data cleaning, visualization, statistical modeling, and advanced analysis techniques.
By the end, they will have developed a solid foundation in statistical computing and practical skills that can be applied to real-world research and analytical scenarios, empowering them to effectively tackle diverse data challenges across various domains.
Teaching Methods
The course uses a fine blend of interactive discussions, group exercises, self-paced learning, and practical project reports to simulate real-world applications
Interactive Discussions
Engage with instructors and peers to discuss core concepts and clear any doubts.
Group Exercises
Collaborate in group activities that reinforce understanding and foster teamwork.
Self-Paced Learning
Access learning materials and recorded sessions to learn at your own pace, accommodating different learning styles.
Project Reports
Work on two comprehensive project reports that simulate real-world data analysis scenarios, allowing you to apply what you’ve learned in a practical setting.
Learning Outcomes
By the end of the course, participants will be able to:
Confidently Use R for Data Analysis
Clean, organize, and analyze raw datasets, transforming them into actionable insights.
Critically Analyze Research Reports
Develop the ability to appraise and evaluate research publications, understanding the statistical methodologies and conclusions presented.
Perform Real-World Data Analysis Projects
Tackle project-based assignments that simulate real-world scenarios, enhancing their problem-solving and analytical thinking skills.
Interpret and Communicate Statistical Results
Understand key statistical concepts such as descriptive statistics, bi-variate analysis, and hypothesis testing, enabling them to interpret outputs and draw accurate conclusions.
Training Formats
Online Training
A 4-week flexible learning experience that you can manage at your own pace.
Traditional Training
5 days of immersive, face-to-face learning.
Exclusive Benefits
As part of this course, each participant receives a FREE 21-day teaching license that provides full access to Stata’s extensive suite of tools to:
Perform hands-on data analysis with no restrictions. Experiment with various statistical models and visualization techniques, and lastly apply what you learn immediately through practical assignments.
The program fee for ‘In-person Training’ covers tuition, and program materials (course lecture notes and training manual). A welcome tea, Full refreshments (lunch), Two breaks for tea including pastries Complimentary parking, and a Certificate of attendance.
The program fee for ‘In-person Training’ covers tuition and program materials (course lecture notes and training manual). It includes a welcome tea, full refreshments (lunch), two breaks for tea with pastries, complimentary parking, and a certificate of attendance.
The programme for online training provides for:
– Pre-course evaluation
– Course notes
– Course brochure
– Installation of software
– Post-course evaluation
Course Overview:
This course is designed to equip participants with a comprehensive understanding of R programming for data analysis, covering both fundamental and intermediate concepts. Throughout the course, participants will engage in hands-on projects that delve into data cleaning, visualization, statistical modeling, and advanced analysis techniques.
By the end, they will have developed a solid foundation in statistical computing and practical skills that can be applied to real-world research and analytical scenarios, empowering them to effectively tackle diverse data challenges across various domains.
Teaching Methods:
The course uses a fine blend of interactive discussions, group exercises, self-paced learning, and practical project reports to simulate real-world applications:
- Interactive Discussions: Engage with instructors and peers to discuss core concepts and clear any doubts.
- Group Exercises: Collaborate in group activities that reinforce understanding and foster teamwork.
- Self-Paced Learning: Access learning materials and recorded sessions to learn at your own pace, accommodating different learning styles.
- Project Reports: Work on two comprehensive project reports that simulate real-world data analysis scenarios, allowing you to apply what you’ve learned in a practical setting.
Learning Outcomes:
By the end of the course, participants will be able to:
- Confidently Use R for Data Analysis: Clean, organize, and analyze raw datasets, transforming them into actionable insights.
- Interpret and Communicate Statistical Results: Understand key statistical concepts such as descriptive statistics, bi-variate analysis, and hypothesis testing, enabling them to interpret outputs and draw accurate conclusions.
- Perform Real-World Data Analysis Projects: Tackle project-based assignments that simulate real-world scenarios, enhancing their problem-solving and analytical thinking skills.
- Critically Analyze Research Reports: Develop the ability to appraise and evaluate research publications, understanding the statistical methodologies and conclusions presented.
With these skills, participants will be able to handle data efficiently, make informed decisions, and contribute effectively to their respective fields.
Exclusive Benefits:
- Traditional Training: 5 days of immersive, face-to-face learning.
- Online Training: A 4-week flexible learning experience that you can manage at your own pace.
In-Person Training Schedule
Day 1
Introduction to Data Management and Analysis with R
- Research questions
- From variables to datasets
- Explore the capabilities of R
- Learn to use R: windows, commands, do-files, log file
- Import data in different formats into R
- Export data in R into different formats
- Appending and merging data from different sources
Day 2
Advanced Data Management and Preparation for Analysis
- Applications of data combination
- Data management procedures
- Working with do-files
- Data cleaning before and during analysis
- Introduction to descriptive statistics
Day 3
Data Visualization and Subgroup Analysis in R
- Introduction to R graphs
- Subgroup analysis
- First project report
Day 4
Introduction to Inferential Statistics and Hypothesis Testing
- Inferential statistics and meaning of differences
- Hypothesis testing, p-values and confidence intervals
- Chi-squared and Fisher’s exact tests for categorical data
- Parametric Tests: T-test, ANOVA, correlation
Day 5
Non-Parametric Tests and Introduction to Multiple Regression
- Non-parametric tests: ranksum test, Kruskal Wallis, correlation
- Why multiple regression?
- Second project report
Online Course Schedule
Week 1
Introduction to R: Data Structures, Functions, and Programming
- Research questions and data structure
- Introduction to R, explore the capabilities of R, comparison with Stata and Python
- Downloading R and R Studio
- Working in R – data structures and functions
- Accessing R packages, writing R functions
- Introduction to R programming
Week 2
Data Management and Organization in R
- R script files – organizing and running R codes
- Reading and importing data into R
- Basic data management
Week 3
Descriptive Statistics and Subgroup Analysis in R
- Making sense of data (concepts in descriptive statistics)
- Using R for descriptive analysis
- Using R for subgroup analysis
Week 4
Statistical Tests and Confidence Intervals
- Calculating confidence intervals
- Different types of statistical tests
- Chi-square test
- Parametric and non-parametric tests
In-Person Training Schedule
Day 1
Introduction to Data Management and Analysis with R
We’d Explore the capabilities of R by learning to handle research questions, manage variables and datasets, navigate R’s interface and commands, work with do-files and log files, import and export data in various formats, and efficiently append and merge data from different sources.
Day 2
Advanced Data Management and Preparation for Analysis
We’d Master data management procedures by combining datasets, working with do-files, and performing data cleaning both before and during analysis to ensure accurate and reliable results.
Day 3
Data Visualization and Subgroup Analysis in R
- Introduction to R graphs
- Subgroup analysis
- First project report
We’d begin with an introduction to R graphs, conduct subgroup analysis, and compile findings in your first project report.
Day 4
Introduction to Inferential Statistics and Hypothesis Testing
- Inferential statistics and meaning of differences
- Hypothesis testing, p-values and confidence intervals
- Chi-squared and Fisher’s exact tests for categorical data
- Parametric Tests: T-test, ANOVA, correlation
We’d understand inferential statistics and the meaning of differences through hypothesis testing, p-values, and confidence intervals, while exploring statistical methods such as Chi-squared and Fisher’s exact tests for categorical data, as well as parametric tests like T-tests, ANOVA, and correlation.
Day 5
Non-Parametric Tests and Introduction to Multiple Regression
- Non-parametric tests: ranksum test, Kruskal Wallis, correlation
- Data collection
- Second project report
Wed explore non-parametric tests such as the ranksum test, Kruskal-Wallis, and correlation, followed by data collection and the preparation of your second project report.
Online Training Schedule
Week 1
Introduction to R: Data Structures, Functions, and Programming
- Introduction to R programming
- Research questions and data structure
- Introduction to R, explore the capabilities of R, comparison with Stata and Python
- Downloading R and R Studio
- Working in R – data structures and functions
- Accessing R packages, writing R functions
Week 2
Data Management and Organization in R
- R script files – Organizing and running R codes
- Reading and importing data into R
- Basic data management
Week 3
Descriptive Statistics and Subgroup Analysis in R
- Making sense of data (concepts in descriptive statistics)
- Using R for descriptive analysis
- Using R for subgroup analysis
Week 4
Statistical Tests and Confidence Intervals
- Calculating confidence intervals
- Different types of statistical tests
- Chi-square Test
- Parametric and Non-parametric tests
WHY LEARN FROM US
CESAR is a world-class training and consultancy organization based in Johannesburg, South Africa. We have provided top-notch training and consultancy services in applied research, monitoring and evaluation, statistical analysis, and evidence-based practice to organizations across 29 countries and four continents.
Expert Instructors:
Our instructors are seasoned professionals with years of experience using Stata in real-world research and business environments.
Hands-on Learning:
We focus on practical, actionable knowledge through group exercises and projects that simulate real-life data challenges.
Comprehensive Support:
We provide ongoing assistance during the course, from learning R basics to tackling complex data analysis challenges.
WHY LEARN FROM US
CESAR is a world-class training and consultancy organization based in Johannesburg, South Africa. We have provided top-notch training and consultancy services in applied research, monitoring and evaluation, statistical analysis, and evidence-based practice to organizations across 29 countries and four continents.
Expert Instructors
Our instructors are seasoned professionals with years of experience using Stata in real-world research and business environments.
Hands-on Learning
We focus on practical, actionable knowledge through group exercises and projects that simulate real-life data challenges.
Comprehensive Support
We provide ongoing assistance during the course, from learning R basics to tackling complex data analysis challenges.
Who Should Participage?
Researchers
Scientists
Data Analysts
Epidemiologists
Clinicians
Economists
Biostatisticians
Research analysts
Programme Managers
Postgraduate Students
Market Researchers
Government Practitioners
Clinical and medical researchers
Who Should Participage?
This course is ideal for:
Researchers
Scientists
Epidemiologists
Data Analysts
Market Researchers
Programme Managers
Clinicians
Economists
Research analysts
Biostatisticians
Government Practitioners
Clinical and medical researchers
Course Reviews
Participant Stories
☆☆☆☆☆
The course was a game-changer for me! The lessons were detailed yet easy to follow, making it simple to learn R from scratch. I can now perform data cleaning, analysis, and visualization tasks with ease. Perfect for anyone looking to advance their data skills!
☆☆☆☆☆
The 'Master Data Analysis with Stata' course exceeded my expectations! The step-by-step approach made complex statistical concepts easy to understand and apply. I now feel confident in using Stata for my research projects. Highly recommended for anyone looking to master data analysis!
☆☆☆☆☆
This training provided a deep dive into Stata and data analysis. The course structure was easy to follow, and the instructor’s knowledge was impressive. I now understand how to conduct statistical analysis and interpret results confidently. It’s the best investment I’ve made in my career!
☆☆☆☆☆
As someone new to Stata, I was worried that I might not keep up, but this course proved to be exactly what I needed. The explanations were clear, and the practical examples helped me get hands-on experience. I can now analyze datasets effectively and produce professional reports!
☆☆☆☆☆
The course was a game-changer for me! The lessons were detailed yet easy to follow, making it simple to learn R from scratch. I can now perform data cleaning, analysis, and visualization tasks with ease. Perfect for anyone looking to advance their data skills!
☆☆☆☆☆
Having tried other courses in the past, I can say that this is by far the most comprehensive and well-structured R course I’ve taken. I can now perform complex data manipulations and build insightful visualizations with R. Definitely worth every penny!
☆☆☆☆☆
This training helped me move from being a beginner to confidently using R for my research work. The instructor provided excellent explanations and practical examples that made the concepts stick. I highly recommend this course to anyone serious about mastering data analysis with R.
☆☆☆☆☆
The course completely course transformed the way I approach data analysis. The blend of theory and hands-on projects was exceptional. I now feel equipped to tackle any data challenge using R and can produce professional-grade reports with confidence!
Frequently Asked Questions
Find answers to common questions about the comprehensive Data Analyst course with R
This course provides a complete overview of data analysis using R, covering data cleaning, visualization, statistical modeling, and advanced analytics. It’s designed to help learners become proficient in using R for real-world data analysis.
The course covers importing and cleaning data, exploratory data analysis, data visualization using ggplot2, basic and advanced statistical models, machine learning algorithms, and report generation using RMarkdown.
Yes, the course is designed for both beginners and those with some experience. It starts with the basics of R programming and data analysis, then progresses to more advanced topics.
Traditional: 18 – 22 Nov.
No prior knowledge of R is required, but a basic understanding of statistics will be helpful.
The course is divided into multiple modules, each focusing on a specific area of data analysis. Each module includes video lectures, hands-on coding exercises, quizzes, and a final project.
You will need R and RStudio installed on your computer. R is the programming language, while RStudio is a user-friendly development environment for R.
Yes, we provide several data sets for hands-on practice, which are relevant to the topics covered in each module.
Yes, you will have lifetime access to all the course materials, including videos, code scripts, and data sets.
Yes, upon successful completion of the course and all assessments, you will receive a certificate of completion.
Absolutely! The certificate can be shared on your resume and LinkedIn profile to showcase your expertise in R and data analysis.
While the course doesn’t include direct career support, we provide guidance on how to build a portfolio using your course projects, which can be used to demonstrate your skills to potential employers.
If you encounter any technical issues, you can reach out to our support team via email. Most issues are resolved within 24 hours.
FREQUENTLY ASKED QUESTIONS
This course provides a complete overview of data analysis using R, covering data cleaning, visualization, statistical modeling, and advanced analytics. It’s designed to help learners become proficient in using R for real-world data analysis.
The course covers importing and cleaning data, exploratory data analysis, data visualization using ggplot2, basic and advanced statistical models, machine learning algorithms, and report generation using R Markdown.
Yes, the course is designed for both beginners and those with some experience. It starts with the basics of R programming and data analysis, then progresses to more advanced topics.
The Traditional Training will take place on the: 18 – 22 Nov
The Online Training will take place on the: 11 Nov. – 06 Dec.
No prior knowledge of R is required, but a basic understanding of statistics will be helpful.
The course is divided into multiple modules, each focusing on a specific area of data analysis. Each module includes video lectures, hands-on coding exercises, quizzes, and a final project.
You will need R and RStudio installed on your computer. R is the programming language, while RStudio is a user-friendly development environment for R.
Yes, we provide several data sets for hands-on practice, which are relevant to the topics covered in each module.
Yes, you will have lifetime access to all the course materials, including videos, code scripts, and data sets.
Yes, upon successful completion of the course and all assessments, you will receive a certificate of completion.
Absolutely! The certificate can be shared on your resume and LinkedIn profile to showcase your expertise in R and data analysis.
While the course doesn’t include direct career support, we provide guidance on how to build a portfolio using your course projects, which can be used to demonstrate your skills to potential employers.
If you encounter any technical issues, you can reach out to our support team via email or through the course platform’s chat function. Most issues are resolved within 24 hours.