Introduction to Statistics Using R Training by Tonex
This comprehensive course offers a thorough introduction to statistical concepts using the powerful R programming language. Participants will gain practical skills in data analysis, visualization, and hypothesis testing, equipping them to make informed decisions based on robust statistical insights. Taught by industry experts from Tonex, this training provides a solid foundation for professionals seeking to leverage statistical methods using R.
Tonex’s “Introduction to Statistics using R” training offers a comprehensive exploration of statistical principles using the versatile R programming language. This course caters to professionals and aspiring data scientists aiming to strengthen their analytical prowess. Participants will delve into the foundations of statistical concepts, mastering key techniques such as data exploration, visualization, hypothesis testing, and statistical modeling.
The hands-on training, led by industry experts, ensures a practical understanding of R programming for effective data manipulation and analysis. Through a structured curriculum, attendees will gain proficiency in handling datasets, addressing data quality issues, and employing regression analysis for predictive modeling.
The course culminates in a capstone project, providing an opportunity to apply acquired skills in real-world scenarios. Upon completion, participants receive a certification, validating their expertise in statistical analysis using R and positioning them for success in data-driven decision-making roles.
Learning Objectives:
- Understand fundamental statistical concepts and their application in real-world scenarios.
- Acquire hands-on experience with R programming for data manipulation and analysis.
- Explore data visualization techniques to effectively communicate insights.
- Master hypothesis testing and statistical inference for informed decision-making.
- Develop skills in exploratory data analysis (EDA) for identifying patterns and trends.
- Gain proficiency in statistical modeling and regression analysis using R.
- Learn best practices for handling and cleaning datasets to ensure accuracy.
- Apply statistical concepts to solve practical business problems.
Audience: This course is designed for professionals and aspiring data scientists looking to enhance their statistical analysis skills using R. It is suitable for individuals in roles such as data analysts, business analysts, researchers, and anyone seeking to leverage statistical techniques for data-driven decision-making.
Course Outline:
Introduction to Statistics and R Basics
- Overview of key statistical concepts
- Introduction to the R programming language
- R environment setup and basic operations
- Data types, vectors, and data frames in R
Data Exploration and Visualization
- Exploratory Data Analysis (EDA) techniques
- Creating informative visualizations using ggplot2
- Descriptive statistics for summarizing data
- Handling missing data and outliers in R
Hypothesis Testing and Statistical Inference
- Formulating hypotheses and selecting tests
- Conducting t-tests and chi-square tests in R
- Interpreting p-values and making informed decisions
- Understanding confidence intervals
Statistical Modeling and Regression Analysis
- Linear regression fundamentals
- Multiple regression and model validation
- Logistic regression for binary outcomes
- Practical applications of regression analysis
Data Cleaning and Preprocessing in R
- Identifying and handling data quality issues
- Techniques for data cleaning and transformation
- Normalization and standardization of datasets
- Dealing with categorical variables in R
Practical Applications and Case Studies
- Applying statistical methods to real-world scenarios
- Case studies illustrating the use of R in data analysis
- Addressing challenges and deriving actionable insights
- Group exercises and discussions for practical learning
Advanced Topics in R Programming
- Advanced data manipulation with dplyr
- Time series analysis using R
- Introduction to machine learning with R
- Integration of R with other data science tools
Capstone Project and Certification
- Culminating project applying acquired skills
- Feedback and guidance from instructors
- Certification of completion for successful participants
- Networking opportunities with fellow professionals