| Week | Start Date | Topic |
|---|---|---|
| 01 | January 17/21 | Introduction to Data |
| 02 | January 27/28 | Getting started with R |
| 03 | February 03/04 | Exploratory Data Analysis |
| 04 | February 10/11 | Summarizing Data |
| 05 | February 17/18 | Sampling |
| 06 | February 24/25 | Hypothesis Testing |
| 07 | March 03/04 | Introduction to Linear Regression |
| 08 | March 17 | Linear Regression |
| 09 | March 24 | Multivariate Regression |
| 10 | March 31 | Probability |
| 11 | April 07 | Probability |
| 12 | April 14 | Principles of Data Visualization |
| 13 | April 21 | Communicating Results |
| 14 | April 28 | Final Project Presentations |
STAT 230 Data Science and Statistics
Course modality: in-person (see next section for information on meeting days, time, and location)
Course Sections
- 01 Monday and Wednesday, 10:00 am - 11:25 am, Center for Comp & Info Science 133
- 04 Tuesday and Thursday, 12:00 pm - 1:25 pm, Richardson Hall 104
- 06 Tuesday and Thursday, 3:50 pm - 5:15 pm, Richardson Hall 256
Instructor
- Adriana Picoral, PhD
- Office: Schmitt 374
- email: picorala@montclair.edu
- Office Hours (open door/drop in):
- Tuesdays and Thursdays, 1:45pm to 3:30pm
Course Text
Course Description
Introduction to the general methodology of statistical data science in the context of the purpose, planning, analysis, and conclusions associated with a variety of field-specific research studies. Topics include Exploratory Data Analysis (EDA), summary measures, the normal distribution, linear regression, correlation, sampling distributions, statistical estimation and inference including the t-test and Chi-square test. Understanding data is the primary focus of this course with emphasis on statistical techniques for gathering, analyzing, summarizing and communicating the results of these analyses. Statistical software is used.
Prerequisites
MATH 111 with a grade of C- or higher or placement through the Montclair State University Placement Test (MSUPT) or MATH 122 with a grade of C- or higher or AMAT 120 with a grade of C- or higher
Course Goal
This course focuses on building a foundational understanding of statistical methods and their practical applications.
Learning Outcomes
By the end of this course, students will be able to:
Describe the use of statistics in data science, including its role in research study design, data collection, analysis, and conclusion drawing.
Calculate and interpret measures of central tendency (mean, median, mode) and variability (e.g., range, standard deviation).
Conduct Exploratory Data Analysis (EDA): summarize data, identify patterns, trends, and outliers.
Explain different sampling distributions and their role in statistical estimation and hypothesis testing.
Perform and interpret correlation, identifying its pitfalls.
Perform statistical estimation and hypothesis tests, including the t-test and Chi-square test, and interpret the results in context.
Perform and interpret Linear Regression: run linear regression models, interpret regression coefficients, and analyze relationships between variables
Use the statistical programming language R to perform data analysis, including data visualization, regression modeling, and hypothesis testing.
Apply statistical techniques to field-Specific problems: demonstrate the ability to plan and conduct statistical analyses tailored to specific fields of study, linking statistical insights to practical applications.
Critique data-based claims and evaluate data-based decisions.
Communicate data analysis results: Effectively summarize and present findings from statistical analyses in a clear and concise manner suitable for various audiences.
Course Schedule
Grading Breakdown
| Assessment Element | Percentage of Final Grade |
|---|---|
| In-class Work | 10% |
| Homework Assignments | 30% |
| Quizzes (5) | 30% |
| Group Project | 30% |
Late Work Policy
Students are expected to complete work on schedule. The late policy for all assignments is as follows:
- 20% points off if submitted within 24 hours after the due date
- 30% off if submitted 24-48 hours after the due date
- No credit if submitted two days or more days after the due date unless prior arrangements are made with the instructor with acceptable reasons.
Note: This is a firm policy and it will be automatically applied
Attendance Policy
Attendance is mandatory. All students must sign an attendance sheet at the beginning of class. Excused absences include illness or a serious personal crisis (a letter from the Dean of students is required). If you are missing class and have a reasonable excuse, contact me before lecture if you want to make up missed assessments.
Grading Scale
| Letter Grade | Grade Percentage |
|---|---|
| A | 94-100% |
| A- | 90-93% |
| B+ | 87-89% |
| B | 84-86% |
| B- | 80-83% |
| C+ | 77-79% |
| C | 74-76% |
| C- | 70-73% |
| D+ (undergraduate only) | 67-69% |
| D (undergraduate only) | 64-66% |
| D- (undergraduate only) | 60-63% |
| F | 0-59% |
Academic Honesty and Integrity
Academic Honesty is a core University value. Take time to understand the University’s policy. Your questions about academic honesty are always welcome.
All work you submit for grading in this course must be your own. Submitting work that includes (minor and/or major) components that are not your own work is considered plagiarism. I recommend that when talking with others about the assignment, do not write anything down.
Keep in mind that all assignments and practice problems provided in this course are meant to help you practice the skills that you will need to do well in all assessments (including on paper quizzes), so it is generally in your best interest to avoid taking shortcuts even on practice problems (which are ungraded).
Sharing your code with others (in addition to copying code from others) is considered a break of the academic integrity code (unauthorized assistance) as well.
Land Acknowledgement
We respectfully acknowledge that Montclair State University occupies land in Lenapehoking, the traditional and expropriated territory of the Lenape. As a state institution, we recognize and support the sovereignty of New Jersey’s three state-recognized tribes: the Ramapough Lenape, Nanticoke Lenni-Lenape, and Powhatan Renape nations.
We recognize the sovereign nations of the Lenape diaspora elsewhere in North America, as well as other Indigenous individuals and communities now residing in New Jersey. By offering this land acknowledgement, we commit to addressing the historical legacies of Indigenous dispossession and dismantling practices of erasure that persist today. We recognize the resilience and persistence of contemporary Indigenous communities and their role in educating all of us about justice, equity, and the stewardship of the land throughout the generations.
Subject to Change
Changes may be made to this syllabus as needed to support student learning. Any updates will be announced in class or through course materials with advance notice.