Data Science and Analytics using R Training by Tonex
This comprehensive course by Tonex provides a deep dive into Data Science and Analytics using the versatile R programming language. Participants will gain hands-on experience with R’s powerful tools and libraries, equipping them with the skills to extract valuable insights from data.
Tonex’s Data Science and Analytics using R Training is an intensive program designed to equip professionals with the essential skills to navigate the evolving landscape of data. Participants delve into R programming, gaining proficiency in statistical analysis, data visualization, and manipulation.
The course extends to exploratory data analysis, machine learning, and big data analytics, offering hands-on experience with R tools. Tailored for data scientists, analysts, and business intelligence professionals, this training provides a comprehensive understanding of R’s applications in solving real-world challenges.
With a focus on practicality and industry relevance, Tonex ensures participants emerge ready to leverage R for insightful decision-making and innovative data-driven solutions.
Learning Objectives:
- Understand the fundamentals of R programming language for data analysis.
- Learn advanced statistical techniques and machine learning algorithms using R.
- Acquire proficiency in data visualization and storytelling with R.
- Master data manipulation and transformation using R packages.
- Develop skills in exploratory data analysis (EDA) and hypothesis testing.
- Gain practical experience in building predictive models and conducting feature engineering.
- Learn to work with big data using R and distributed computing frameworks.
- Explore real-world case studies and apply R to solve complex data science challenges.
Audience: This course is designed for:
- Data Scientists and Analysts
- Statisticians and Researchers
- Business Intelligence Professionals
- IT and Analytics Managers
- Researchers in Data-related fields
- Anyone looking to enhance their data science skills with R
Course Outline:
Introduction to R Programming
- Overview of R and its ecosystem
- Basics of R syntax and data structures
- Introduction to RStudio and R Markdown
Statistical Analysis with R
- Descriptive statistics and inferential statistics
- Hypothesis testing and p-values
- Regression analysis and ANOVA
Data Visualization in R
- Basic plotting functions in R
- Advanced visualization using ggplot2
- Interactive visualizations with Shiny
Data Manipulation with R
- Introduction to dplyr and tidyr
- Reshaping data with pivot_longer and pivot_wider
- Working with dates and times in R
Exploratory Data Analysis (EDA)
- Univariate and bivariate analysis
- Outlier detection and handling missing data
- Correlation analysis and dimensionality reduction
Machine Learning with R
- Introduction to machine learning in R
- Supervised learning algorithms (e.g., regression, classification)
- Unsupervised learning algorithms (e.g., clustering, dimensionality reduction)
Big Data Analytics with R
- Introduction to big data technologies (e.g., Spark)
- Working with distributed computing in R
- Handling large datasets efficiently
Real-World Applications and Case Studies
- Applying R to solve practical data science problems
- Integrating R into business decision-making
- Best practices in deploying R-based solutions