Introduction to R for Data Analysis
Program Overview
This 10-week online course provides an intensive introduction to R programming for data analysis. Participants will acquire the technical knowledge and skills to extract valuable insights from large datasets, fostering their careers as data analysts. The course emphasizes the practical application of R in organizational settings, utilizing R techniques to analyze real-world datasets and communicate findings through powerful visualizations. Upon completion, participants will be well-equipped to pursue careers as data analysts in various sectors.
Program Outline
Introduction to R for Data Analysis (Short Course)
Degree Overview:
This 10-week online course offers an intensive introduction to the R programming language and its applications in data analysis. Participants will acquire the technical knowledge and skills to extract valuable insights from large datasets, fostering their careers as data analysts. The course delves into the fundamentals of R, including data structures, manipulation techniques, and statistical functions. Participants will learn to create and customize functions tailored to their specific analytical needs. Advanced topics such as sophisticated graphics, data mining algorithms, and statistical modeling are also covered, providing a comprehensive foundation for data analysis with R. The course emphasizes the practical application of R in organizational settings. Participants will utilize R techniques to analyze real-world datasets and communicate their findings through powerful visualizations. This practical approach empowers participants to leverage data analysis as a strategic tool for organizational planning and decision-making.
Outline:
The course curriculum is structured to provide a progressive learning experience, starting with the basics of R and gradually progressing to more advanced concepts and techniques.
- Data Structures: Participants will explore the fundamental data structures in R, including vectors, factors, matrices, lists, and data frames. They will learn to manipulate these structures effectively using various functions, including aggregative functions, indexing, and the apply() family.
- Graphics: Beyond basic plotting techniques like plot() and hist(), the course delves into advanced visualization tools like ggplot2(), Tableau, and Power BI. Participants will learn to leverage these tools for in-depth analysis and presentation of their findings.
- Statistics: With a focus on practical applications, participants will engage with key statistical concepts like descriptives, distributions, regressions, and correlation analysis. They will also cover more advanced topics like t-tests, ANOVA, and categorical data analysis. The course emphasizes the rigorous application of these techniques to solve real-world organizational problems.
- Programming: Participants will gain a solid understanding of procedural programming principles, including variables, control structures, and writing simple functions. They will then progress to building more sophisticated functions designed for efficient large-dataset manipulation.
- Data Loading, Cleaning, and Transformation: Participants will learn to import data from various sources, including Excel, SQL, XML, and the web. They will master SQL for effective data querying within R and explore techniques for data cleaning, transformation, merging, and sampling.
Assessment:
Continuous assessment is utilized throughout the course to monitor and enhance student learning.
- In-class exercises and assignments: Participants will engage in regular exercises and assignments to test and expand their knowledge of R concepts and techniques.
- Data Analysis Project: A continuous data analysis project serves as the primary assessment tool. Participants will use R techniques to analyze real-world data, deriving insights and communicating their findings through visualizations.
Teaching:
The course employs a blended learning approach, combining interactive lectures with hands-on practical exercises. This approach fosters a dynamic learning environment where participants can actively engage with the material and receive immediate feedback on their progress.
Faculty:
The course is led by experienced data science professionals with a deep understanding of R and its applications in various industries. This ensures that participants receive the most up-to-date and industry-relevant knowledge and skills.
Careers:
Upon completion of the course, participants will be well-equipped to pursue careers as data analysts in various sectors. They will be able to leverage their data analysis skills to extract valuable insights from large datasets and utilize those insights to inform strategic decision-making in their respective organizations.
Other:
The course is designed to be flexible and accessible, allowing participants to learn at their own pace and from the comfort of their own homes. It provides an excellent opportunity for individuals to upskill or refine their existing data analysis skills, enhancing their employability in the data-driven world.
