Purpose
A course on quantitative data management and analysis in R is a course designed to teach participant how to use the R programming language to manage and analyze quantitative data. The course will cover the basics of R programming, including data types, control structures, and functions. Participant will also learn how to use popular R libraries such as dplyr, tidyr, and ggplot2 to perform data management and visualization tasks. The course will cover topics such as data cleaning, data manipulation, statistical analysis, and data visualization. Participant will learn how to import and export data, create summary statistics, create plots and charts, and perform statistical tests. The course will also cover advanced topics such as machine learning, big data, and text mining.
The course will provide a hands-on approach, with participant working on real-world examples and datasets and using R to run the analysis and create visualizations. The course is intended for participant and professionals who have a basic understanding of statistics and programming, and who are interested in learning how to manage and analyze quantitative data using R.
Course Objectives
- Understand the basics of R programming, including data types, control structures, and functions.
- Learn how to use popular R libraries such as dplyr, tidyr, and ggplot2 for data management and visualization.
- Gain experience in data cleaning, data manipulation, and statistical analysis using R.
- Learn how to create plots and charts using ggplot2 and other visualization libraries in R.
- Understand how to perform statistical tests, such as t-tests, chi-squared tests, and ANOVA, using R.
- Learn how to import and export data from various sources, such as CSV, Excel, and SQL databases.
- Gain hands-on experience working with real-world examples and datasets using R to perform quantitative data management and analysis.
- Understand how to apply the concepts learned in the course to various domains such as finance, economics, and social sciences.
- Understand the role of quantitative data management and analysis in decision-making and problem-solving.
- Learn to understand and work with big data and machine learning techniques in R.
- Understand the text mining techniques in R to extract information from text data.
Target Audience
- Graduate participant in statistics, computer science, finance, economics, and social sciences who are interested in learning how to perform quantitative data management and analysis using R.
- Researchers and academics who want to use R to perform data management and analysis in their field of study.
- Data scientists and analysts who want to learn how to use R for quantitative data management and analysis tasks.
- Professionals in the field of finance, economics, and social sciences who want to improve their skills in quantitative data management and analysis.
- Business analysts and consultants who want to learn how to use R for quantitative data management and analysis to understand the behavior of firms and markets.
- IT professionals and programmers who want to learn how to use R for quantitative data management and analysis tasks.
- Anyone who is interested in understanding the basics of quantitative data management and analysis and how it can be applied to real-world problems using R.
Program Outline
Topic 1. Introduction to R Programming
Overview of the R programming language, including data types, control structures, and functions.
Topic 2. Data Manipulation with dplyr and tidyr
Overview of how to use dplyr and tidyr for data manipulation tasks such as cleaning, reshaping, and merging data.
Topic 3. Data Visualization with ggplot2
Overview of how to use ggplot2 and other visualization libraries in R to create plots and charts.
Topic 4. Statistical Analysis with R
Overview of how to perform statistical analysis using R, including summary statistics, t-tests, chi-squared tests, and ANOVA.
Topic 5. Data Import and Export
Overview of how to import and export data from various sources, such as CSV, Excel, and SQL databases.
Topic 6. Machine Learning and Big Data
Overview of how to use R for machine learning and big data tasks, including supervised and unsupervised learning, and working with large datasets.
Topic 7.Text Mining
Overview of how to use R to extract information from text data using text mining techniques.
Topic 8. Applications in different fields
Overview of how the concepts learned in the course can be applied in various domains such as finance, economics, and social sciences.
Topic 9. Quantitative data management and analysis in decision-making and problem-solving
Overview of how quantitative data management and analysis can be used to support decision-making and problem-solving.
Throughout the course, participant will be using R to manage and analyze data, perform the hypothesis testing and interpret the results. The course will also cover the best practices, tips, and tricks to make the most out of R for quantitative data management and analysis.