Statistical Data Analysis with R Training Course
Have you ever wondered how top-performing companies harness the power of data to make smarter, faster decisions? In an age where data is king, the ability to analyze and interpret this data effectively is critical. But how do you transform raw data into actionable insights? Enter the world of R—a powerful, versatile tool that’s revolutionizing statistical data analysis across industries. Welcome to Trainingcred's Statistical Data Analysis with R course, where we empower you to make data-driven decisions that propel your organization forward. Ready to turn data into your competitive advantage?
In-Person: Classroom Sessions
| Venue Location | Duration | Language | |
|---|---|---|---|
| Nairobi, Kenya | 10 Days | English | Dates & Prices |
| Kigali, Rwanda | 10 Days | English | Dates & Prices |
| Kampala, Uganda | 10 Days | English | Dates & Prices |
| Dubai, United Arab Emirates (UAE) | 10 Days | English | Dates & Prices |
| Mombasa, Kenya | 10 Days | English | Dates & Prices |
| Naivasha, Kenya | 10 Days | English | Dates & Prices |
| Nakuru, Kenya | 10 Days | English | Dates & Prices |
| Kisumu, Kenya | 10 Days | English | Dates & Prices |
Virtual (Zoom) Instructor-Led
| Code | Start Date | End Date | Fee | |
|---|---|---|---|---|
| DSR-01 | Oct 28, 2024 | Nov 08, 2024 | USD. 1500 | Register Register Group |
| DSR-01 | Nov 18, 2024 | Nov 29, 2024 | USD. 1500 | Register Register Group |
| DSR-01 | Dec 23, 2024 | Jan 03, 2025 | USD. 1500 | Register Register Group |
| DSR-01 | Jan 06, 2025 | Jan 17, 2025 | USD. 1500 | Register Register Group |
| DSR-01 | Feb 03, 2025 | Feb 14, 2025 | USD. 1500 | Register Register Group |
In-House Training
Transform Your Workforce
Learn emerging skills quickly with custom curriculum designed as per your needs.
Why top organizations prefer Trainingcred
- High engagement and outcome-centric learning
- Customized curriculum built with industry leaders, for industry leaders
- Hands-on exercises and industry use cases
- Strong reporting to track learning and calculate training ROI for managers
- Day 1 production ready on the completion of the training
Programs delivered as per your training needs
On Premises
Virtual Instructor-Led
Self-Paced
Blended
Modules Covered, Designed by Experts
Module 1: Introduction to R and Data Analysis
- Why R? Overview of R’s Capabilities and Uses
- Setting Up R and RStudio: A Step-by-Step Guide
- Basics of R Programming: Syntax, Variables, and Functions
- Understanding Data Structures in R: Vectors, Matrices, Data Frames, and Lists
Module 2: Data Manipulation with R
- Importing and Exporting Data: From CSV to Databases
- Data Cleaning Techniques: Handling Missing Values and Outliers
- Data Transformation with dplyr: Select, Filter, Mutate, and Arrange
- Merging and Joining Data Frames: Reshaping Data for Analysis
Module 3: Descriptive Statistics and Exploratory Data Analysis (EDA)
- Calculating Central Tendency: Mean, Median, and Mode
- Measuring Variability: Standard Deviation, Variance, and Range
- Visualizing Data Distributions: Histograms, Boxplots, and Density Plots
- Identifying Patterns and Relationships: Correlation and Scatterplots
Module 4: Inferential Statistics with R
- Understanding Sampling and Probability Distributions
- Hypothesis Testing: t-tests, Chi-Square Tests, and ANOVA
- Confidence Intervals and p-Values: Making Informed Decisions
- Non-Parametric Tests: When and How to Use Them
Module 5: Data Visualization with R
- Introduction to ggplot2: The Grammar of Graphics
- Creating Bar Charts, Line Graphs, and Pie Charts
- Advanced Visualization: Heatmaps, Facets, and Interactive Plots
- Customizing Plots: Themes, Labels, and Annotations
Module 6: Regression Analysis and Predictive Modeling
- Simple Linear Regression: Modeling Relationships
- Multiple Regression: Handling Multiple Predictors
- Logistic Regression: Predicting Categorical Outcomes
- Evaluating Model Performance: R-Squared, Residuals, and More
Module 7: Time Series Analysis in R
- Introduction to Time Series Data and Applications
- Decomposing Time Series: Trend, Seasonality, and Noise
- Forecasting with ARIMA and Exponential Smoothing
- Visualizing Time Series Data: Line Graphs and Seasonal Plots
Module 8: Advanced Statistical Techniques
- Cluster Analysis: Grouping Similar Data Points
- Principal Component Analysis (PCA): Reducing Dimensionality
- Survival Analysis: Understanding Duration Data
- Bootstrapping and Resampling Methods: Estimating Accuracy
Module 9: Automation and Integration with R
- Automating Reports with RMarkdown
- Scheduling and Automating R Scripts
- Integrating R with Excel, SQL, and Other Tools
- Using R in Shiny for Interactive Data Applications
About the Course
This course is your gateway to mastering statistical data analysis using R, the go-to language for data professionals worldwide. We’ll guide you through the essentials, from understanding data structures in R to performing complex statistical tests and creating stunning visualizations. Whether you're new to R or looking to deepen your expertise, this course offers the tools, techniques, and best practices you need to analyze data with confidence. Through a mix of theory and hands-on practice, you'll learn how to manipulate data, run sophisticated analyses, and present your findings in a way that drives impactful decisions.
Target Audience
This course is designed for professionals who want to leverage data analysis in their roles, including:
- Data Analysts and Scientists looking to expand their toolset with R.
- Business Analysts who need to interpret data for strategic decisions.
- Statisticians and Researchers aiming to streamline their data analysis process.
- IT Professionals interested in integrating R into data-driven projects.
- Managers and Team Leaders who want to understand and guide data analysis in their teams.
- Academics and Educators looking to incorporate R into their curriculum or research.
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamentals of R programming and its applications in data analysis.
- Manipulate and clean data using R’s powerful packages and functions.
- Perform descriptive and inferential statistical analyses.
- Create and interpret complex data visualizations to communicate insights effectively.
- Apply regression models and advanced statistical techniques.
- Use R for hypothesis testing and data modeling.
- Automate data analysis tasks to improve efficiency.
- Integrate R with other data tools and platforms.
Organizational and Professional Benefits
Enhance your professional capabilities and unlock new opportunities by mastering statistical data analysis with R:
- Elevate your data analysis skills with a deep understanding of R.
- Improve decision-making by basing your strategies on solid data insights.
- Expand your career opportunities in data science, business analysis, and beyond.
- Boost productivity through the automation of complex data tasks.
- Gain recognition as a data-driven professional who adds value to any project.
Implementing this course within your organization can lead to significant advantages:
- Data-driven decision-making becomes the norm, leading to better outcomes.
- Enhanced analytical capabilities across teams, fostering innovation and efficiency.
- Increased accuracy and reliability in data interpretations and reports.
- Streamlined processes through automation, saving time and resources.
- Improved collaboration between technical and non-technical teams, with a shared understanding of data insights.
Training Methodology
Our training approach combines theory with practical, real-world application to ensure you gain the skills you need:
- Expert-led lectures that explain complex concepts in an engaging and understandable way.
- Hands-on exercises using RStudio and real datasets, allowing you to practice what you learn.
- Interactive discussions and group work to deepen understanding and encourage knowledge sharing.
- Case studies that demonstrate how R is used in different industries and scenarios.
- Continuous feedback and support to help you master each concept before moving on to the next.
Upcoming Sessions in International Locations
Certification: Your Badge of Honor!
Upon successful completion of our Statistical Data Analysis with R Training Course, you won't just walk away with newfound knowledge – you'll also snag a Trainingcred Certificate! This is your golden ticket, showcasing your expertise and dedication in Big Data Analytics, Data Science and Data Engineering.
Tailor-Made Course: Like a Suit, But for Your Brain!
Imagine Statistical Data Analysis with R Training Course that fits your team's needs as perfectly as a tailor-made suit! That's what we offer with our bespoke training solution. We don't believe in one-size-fits-all; instead, we're all about crafting a learning experience that's as unique as your organization.
How do we do it? By diving deep with a Training Needs Assessment, we uncover the hidden gems – the skills your team already rocks at, the knowledge gaps we need to bridge, and the ambitions soaring in their minds. It's not just training; it's a transformation journey, meticulously designed just for you and your team. Let's make learning personal.
Accommodation and Airport Pickup
We’re here to make your experience seamless! If you need accommodation or airport pickup, just let us know. To arrange your reservations, please reach out to our Training Officer:
- Email: [email protected]
- Call/WhatsApp: +254759509615
We’re happy to assist!
Frequently Asked Questions
No worries at all. We all find ourselves with questions now and then.
What is R?
R is a software environment and programming language used for data analysis and statistical computing. Because of its robust data manipulation capabilities and large package ecosystem, statisticians and scientists use it extensively.
How I import data into R?
To import data, utilize functions such as `read.table ()} for tab-delimited files and `read.csv ()} for CSV files. Utilize programs like `readex1} or `openx1sx} for Excel files. Make sure your code accurately specifies the file path and format. Accurate analysis depends on properly imported data.
How do I perform a t-test in R?
Conduct a t-test using the `t.test ()` function to compare means between two groups. For example, `t.test (x, y)` compares the values in vectors `x` and `y`. The function provides p-values and confidence intervals to assess statistical significance. Use t-tests to evaluate hypotheses about group differences.
How do I create a basic plot in R?
Create basic plots using the `plot ()' function, such as `plot (x, y)`for scatter plots. Customize plots by adjusting parameters within the `plot()` function. For more advanced visualizations, use the `ggp1ot2` package, which offers extensive customization options. Effective plotting helps in visualizing and interpreting data trends.
How do I resolve package conflicts or errrors in R?
Resolve package conflicts by specifying the package name with `package: : function()`. For errors, review the error messages and consult documentation or online forums for solutions. Check for compatibility issues between packages and R versions. Troubleshooting helps ensure smooth operation and accurate results.
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