|
|
JOHN CABOT UNIVERSITY
COURSE CODE: "EXP 1029"
COURSE NAME: "Foundations of R Programming"
SEMESTER & YEAR:
Spring 2025
|
SYLLABUS
INSTRUCTOR:
Marco Forti
EMAIL: [email protected]
HOURS:
FRI 2:00PM 6:00PM Course meets on: February 14, March 7, March 21 and April 4
TOTAL NO. OF CONTACT HOURS:
15
CREDITS:
1
PREREQUISITES:
Prerequisites: Recommended: MA 208 or PS 208 or PL 208. Students can take a maximum of three 1 credit courses within the 120 credit graduation requirement.
OFFICE HOURS:
|
|
COURSE DESCRIPTION:
This course will be graded on a PASS/FAIL scale. This course introduces the main foundations and principles of programming in R, a widely-used programming language for statistical analysis in data science. Students will learn the basics of how to generate random variables, manage datasets, and perform various statistical analyses. The course begins with descriptive statistics, covering methods for summarizing and visualizing data using the R programming language, and explores key numerical measures like mean, median, variance, and standard deviation. Sampling methods and distributions, leading to hypothesis testing and statistical inference, will also be discussed. The course includes simple linear regression to model relationships between variables and make predictions. Emphasis is placed on real-world applications, enabling students to analyze data and present findings effectively using R.
|
SUMMARY OF COURSE CONTENT:
This course will be graded on a PASS/FAIL scale. This course introduces the main foundations and principles of programming in R, a widely-used programming language for statistical analysis in data science. Students will learn the basics of how to generate random variables, manage datasets, and perform various statistical analyses. The course begins with descriptive statistics, covering methods for summarizing and visualizing data using the R programming language, and explores key numerical measures like mean, median, variance, and standard deviation. Sampling methods and distributions, leading to hypothesis testing and statistical inference, will also be discussed. The course includes simple linear regression to model relationships between variables and make predictions. Emphasis is placed on real-world applications, enabling students to analyze data and present findings effectively using R.
|
LEARNING OUTCOMES:
This course aims to provide students with a first introduction to the principles and practices of statistical analysis using the R programming language. The primary focus of the course is to provide hands-on experience with statistical analysis using R. Each module combines theoretical instructions with practical exercises, ensuring students can apply statistical techniques to real-world datasets. The course will cover descriptive statistics and the basic of inferential statistical methods. By the end of the course, students will have gained practical skills in data analysis and be prepared for more advanced statistical techniques.
|
TEXTBOOK:
|
REQUIRED RESERVED READING:
RECOMMENDED RESERVED READING:
|
GRADING POLICY
-ASSESSMENT METHODS:
| Assignment | Guidelines | Weight |
| | Upon completion of this course, student will be able to:
- Create and modify random variables and datasets in R.
- Understand and explain key concepts in descriptive statistics, including tabular and graphical methods, and numerical measures such as mean, median, variance, standard deviation and correlation.
- Apply various sampling methods and analyze sampling distributions to understand the principles of statistical inference.
- Construct and interpret confidence intervals and conduct hypothesis tests for population parameters.
- Develop and interpret simple linear regression models to evaluate parameters and to predict outcomes based on statistical data.
- Utilize the R programming language to execute statistical analyses and visualize data effectively.
- Demonstrate the ability to apply theoretical knowledge to real-world datasets, enhancing practical data analysis skills. | |
| Intermediate exam | The instructor reserves the right to ask students for clarification on any exercise on the exam to judge if the work they submitted is actually theirs. | 30/100 |
| Final exam (comprehensive) | The final exam concerns all the topics covered in the course. Details will be given in class as the final exam nears. | 50/100 |
| Attendance and class participation | At the beginning of each class, starting from week two, review questions will be posed, and students will be called upon at random to formulate their answers. In assessing student responses, the level of difficulty of the question posed will be taken into account. | 20/100 |
-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.
-ATTENDANCE REQUIREMENTS:
This course will be graded on a PASS/FAIL scale. This course introduces the main foundations and principles of programming in R, a widely-used programming language for statistical analysis in data science. Students will learn the basics of how to generate random variables, manage datasets, and perform various statistical analyses. The course begins with descriptive statistics, covering methods for summarizing and visualizing data using the R programming language, and explores key numerical measures like mean, median, variance, and standard deviation. Sampling methods and distributions, leading to hypothesis testing and statistical inference, will also be discussed. The course includes simple linear regression to model relationships between variables and make predictions. Emphasis is placed on real-world applications, enabling students to analyze data and present findings effectively using R.
|
|
|
ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic
dishonesty will receive a failing grade on the work in which the dishonesty occurred.
In addition, acts of academic dishonesty, irrespective of the weight of the assignment,
may result in the student receiving a failing grade in the course. Instances of
academic dishonesty will be reported to the Dean of Academic Affairs. A student
who is reported twice for academic dishonesty is subject to summary dismissal from
the University. In such a case, the Academic Council will then make a recommendation
to the President, who will make the final decision.
|
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap.
Students with approved accommodations must inform their professors at the beginning
of the term. Please see the website for the complete policy.
|
|
|
SCHEDULE
|
|
|
Week 1:
|
TOPICS
|
READING MATERIALS
|
|
Introduction to R
Introduction and framework.
Frequency measures and graphical representation of data.
Measures of Central Tendency and Dispersion (sections 3.1.1 - 3.2)
|
Chapters 1, 2, and 3 (3.1.1-3.2).
|
Week 2:
|
TOPICS
|
READING MATERIALS
|
|
Association of two variables (section 4.1 – 4.3.2 – 4.4)
Combinatorics
|
Chapters 4 (4.1, 4.3.2, 4.4) and 5.
|
Week 3:
|
TOPICS
|
READING MATERIALS
|
|
Elements of probability
Random variables
Probability Distributions
|
Chapter 6, 7 (7.1, 7.2, 7.3.4) and 8 (with exeption of 8.1.7 and 8.1.8)
|
Week 4:
|
TOPICS
|
READING MATERIALS
|
|
Inference
Linear and logistic regression
Course review
|
Chapters 9, 10(10.1, 10.2, 10.3) 11 and 12.
|
|
|
|