01:960:295 · Statistics · School 01 — New Brunswick School of Arts and Sciences

Data Management And Wrangling With R

Rutgers describes 01:960:295 as a practical introduction to retrieving, tidying, cleaning, and visualizing data for later statistical analysis, using R and including database interaction. The department says students do not need prior R or programming experience, though an introductory computing or statistics prerequisite applies.

3
credits
Typical seasons: Fall · Spring
Guide: Researched guide · 6 sources

Can I take it?

198:142 960:142 960:212

Data 101 · Data 101 · Statistics II

How it’s graded

Weekly main assignments 81% · Weekly small assignments 4% · Final project 15% · Weekly assignments

Spring 2022 archived syllabus; separate 2025 and Fall 2025 student reports (no current syllabus found)

Students rate teaching

4.46 / 5

SIRS · 262 responses · 2022–2025

Spring 2027

2 sections

2 open in snapshot · 2 instructors

When it meets

MTh 12:10 PM

Livingston, Online

Add to schedule Compare in Explorer Watch openings Ask Scarlet Sync

Who’s teaching it — Spring 2027

Checked-in sections · current seat counts are unavailable.

Zhanyun Zhao

MTh 12:10 PM · Livingston · 1 section · 1 open

Course SIRS teaching3.20/5

Steven Buyske

Online · 1 section · 1 open

Course SIRS teaching4.80/5

Course guide

AI-generated overview

Sources (6)

Is this for me?

Best for

Students who want to practice turning raw data into tidy, clean, visualized data that is ready for statistical analysis.
Students interested in working with R and learning how data workflows connect to databases.

Tough if

You need a course focused primarily on statistical modeling; the stated course scope centers on data preparation, visualization, and database interaction.
You may have difficulty keeping up with recurring submissions if deadlines are hard to meet; one student described late penalties as consequential, so check the current syllabus carefully.

What you actually need

The department lists introductory computing/statistics or Level II Statistics as the prerequisite, but says no prior R or programming is needed. The archived syllabus lists specific qualifying courses and likewise says no prior experience is required; confirm the current registration rules and syllabus for the section you choose.

How it’s graded

81% · Weekly main assignments

Spring 2022 archived sample; same point value, with the two lowest scores weighted at half.

4% · Weekly small assignments

Spring 2022 archived sample; small assignments were associated with the remote period.

15% · Final project

Spring 2022 archived sample; do not assume this weighting applies to current sections.

Weekly assignments

A Reddit commenter in a June 2025 thread described an asynchronous Buyske offering with weekly assignments; exact enrollment term and weight were not stated.

Final project

The same 2025 commenter said the project applied course methods to a distinct dataset; exact enrollment term and weight were not stated.

Homework and exams (at least one exam reported)

A May 2026 commenter describing the prior Fall with Zhao mentioned homework and a lengthy first exam; this does not establish the total exam count, shared exams, weights, or current practice.

Evidence term: Spring 2022 archived syllabus; separate 2025 and Fall 2025 student reports (no current syllabus found). Past weights are not current section policy.

What you’ll learn

5 units · topic sequence may vary by instructor

1
Getting started with R and visual output
The archived topic guide begins with R, RStudio Cloud, ggplot, and R Markdown.
R basicsRStudio CloudggplotR Markdown
2
Transforming and joining data
The guide moves through data frames and common transformations, then joins.
data framespipesfilterselectmutategroup_bysummarizejoins
3
Tidying, files, and cleaning
Topics include tidy data, date and string handling, file input/output, missing values, and data-cleaning methods.
tidy datadatesreading and writing filesstrings and regular expressionsmissing valuesrowwise operationsdata-cleaning methodology
4
Reusable and reproducible workflows
The archive includes project management, GitHub, map functions, and writing functions.
project managementGitHubmap functionswriting functions
5
External data and databases
The guide closes with web scraping, APIs, packages, text analysis, and database work through R and SQL, plus an introduction to NoSQL.
web scrapingAPIspackagesggplot themestidytextSQLrelational databasesNoSQL

Students say

Public comments depict hands-on data-cleaning work, with reported formats differing across instructors and terms. A June 2025 commenter described an asynchronous Buyske offering with weekly assignments and a final project; a May 2026 commenter describing the prior Fall with Zhao reported homework and a lengthy first exam. These are individual accounts, not representative estimates or evidence about the current section.

Recurring applied work

Section format may change the experience

Check late rules directly

Do well in it

Common pitfall

Treating the listed prerequisite as proof that you must already know R.

Instead

The department says prior R/programming knowledge is not required; still confirm you meet the formal computing/statistics prerequisite.

Common pitfall

Assuming an older sample syllabus defines the current section's deadlines or exam format.

Instead

Use the current instructor's syllabus; student reports indicate that exam and late-work experiences can differ across offerings.

Common pitfall

Expecting the course to focus on statistical modeling rather than the preparation of data for analysis.

Instead

The official synopsis centers on retrieving, tidying, cleaning, visualizing, and interacting with databases.

1. Refresh the prerequisite-level statistics or introductory computing you used to qualify; R itself is not required beforehand.
2. Preview the core data-frame verbs and joins listed in the archived topic guide so the transformation vocabulary is familiar.
3. The syllabus archive names the free first edition, but R for Data Science 2e is now available free online. Its contents offer a recent path through visualization, transformation, tidy data, import, strings, missing values, joins, databases, and web scraping; confirm whether the instructor assigns or recommends it. Free resource
4. Before the term starts, review the current syllabus for assignment deadlines, late rules, exam format, and required software because archived and student-reported offerings differ.

Sources & coverage

Researched and independently reviewed · 6 sources · 10/12 fields found

Rutgers Statistics course synopsis: 01:960:295 · official · retrieved 2026-10-02
Rutgers Statistics 295 archived sample syllabus, Spring 2022 · official · retrieved 2026-10-02 · 2022-01 · historical
r/rutgers discussion comparing Spring with Buyske and Fall with Zhao · student · retrieved 2026-10-02
r/rutgers student discussion of 01:960:295 workload and final project · student · retrieved 2026-10-02 · 2025-06-17
Rutgers Statistics faculty profile: Zhanyun Zhao · official · retrieved 2026-10-02
R for Data Science, 2nd edition, online table of contents · official · retrieved 2026-10-02 · 2023-07-11

Search coverage

officialSummary: found · Department synopsis describes retrieval, tidying, cleaning, visualization, and database interaction; it states that prior R or programming knowledge is not required.
site:stat.rutgers.edu "Data Management and Wrangling with R" · Rutgers 960:295 Data Wrangling R course synopsis · site:stat.rutgers.edu "Zhanyun Zhao" 295 · site:statistics.rutgers.edu "Zhanyun Zhao" "960:295" · site:sites.math.rutgers.edu "Zhanyun Zhao" 295

atAGlance.weeklyHours: not-found · No reliable weekly out-of-class hour estimate found. The archived syllabus gives weekly assignments but not a time estimate; student reports differ by instructor and term.
site:reddit.com/r/rutgers "960:295" workload hours · site:reddit.com/r/rutgers "Data Wrangling with R" workload · Rutgers 01:960:295 syllabus weekly hours

courseRatings: not-found · Student reports conflict across terms and instructors, and the official syllabus is a stale Spring 2022 archive. No course-wide ratings are sufficiently corroborated.
site:reddit.com/r/rutgers "960:295" "Zhao" · site:reddit.com/r/rutgers "960:295" workload · site:reddit.com/r/rutgers "Data Wrangling with R" "weekly assignments"

assessment: found · No publicly discoverable Zhao syllabus from the last three years was found after targeted Rutgers faculty/course and term-specific PDF searches; the Statistics sample-syllabus page links only to the archived Spring 2022 sample. Dated student accounts give limited structural reports: one post from 2025 describes weekly assignments and a final project for an asynchronous Buyske offering, while a May 2026 post reports homework and at least one long exam in the Fall 2025 Zhao offering. Exact weights and current policies remain unknown.
"01:960:295" "Fall 2024" syllabus · "01:960:295" "Fall 2025" syllabus · "01:960:295" "Spring 2025" syllabus · "01:960:295" "Fall 2023" syllabus · "Zhanyun Zhao" "960:295" filetype:pdf · site:stat.rutgers.edu "295" "Zhaoz" syllabus · Rutgers Statistics sample syllabi 01:960:295:01 · site:reddit.com/r/rutgers "stat 295" · site:reddit.com/r/rutgers "960:295" final exam

fit.bestFor: found · Fit is based on the official practical data-preparation/database scope, plus a student account of applying the semester methods to a dataset in a final project.
site:stat.rutgers.edu "Data Management and Wrangling with R" · site:reddit.com/r/rutgers "960:295" data science · site:reddit.com/r/rutgers "data wrangling rutgers" Zhao

fit.toughIf: found · The contrast with a modeling-focused expectation follows the department synopsis. Deadline concerns are tied to one individual 2026 account and should not be treated as the current policy.
site:stat.rutgers.edu "Data Management and Wrangling with R" · site:reddit.com/r/rutgers "960:295" late penalties · site:reddit.com/r/rutgers "stat 295"

prerequisitesInPractice: found · The formal prerequisite is prior introductory computing/statistics or Level II Statistics; the department and archived syllabus say no prior R/programming experience is required.
Rutgers 01:960:295 course synopsis prerequisites · Rutgers 01:960:295 Spring 2022 syllabus prerequisite

instructorVariation: found · Rutgers faculty profile confirms Zhao is Statistics teaching faculty, but no personal course page or recent public 295 syllabus was found. The 2026 Reddit report identifies her by name and describes a Fall 2025 experience; the 2025 report describes a Buyske asynchronous offering without an exact enrollment term.
site:stat.rutgers.edu "Zhanyun Zhao" 295 · site:statistics.rutgers.edu "Zhanyun Zhao" "960:295" · site:sites.math.rutgers.edu "Zhanyun Zhao" 295 · "Zhanyun Zhao" Rutgers "Data Wrangling" syllabus · site:reddit.com/r/rutgers ("stat 295" OR "960:295" OR "Data wrangling") Rutgers Zhao

chapters: found · Course-specific sequence remains sourced to the rough Spring 2022 archive. The free R for Data Science 2e table of contents supplies a current reading map for visualization, workflows, transformation, tidying, import, strings, missing values, joins, databases and web scraping; it is not evidence that this edition is assigned or that the class follows that sequence.
Rutgers 01:960:295 syllabus course topics R data wrangling · Rutgers 01:960:295 sample syllabus archived · R for Data Science 2nd edition contents data transformation tidy data relational data official · site:r4ds.hadley.nz "Table of contents"

commonPitfalls: found · A recent individual student post reports costly late submissions; the official scope also cautions against expecting a statistical-modeling course. Verify current late rules in the term syllabus.
site:reddit.com/r/rutgers "960:295" late penalties · Rutgers 01:960:295 course synopsis topics

prepPlan: found · The archived syllabus points to a free R for Data Science text and identifies core data-frame operations, file work, and SQL as course topics; current materials may differ.
Rutgers 01:960:295 syllabus R for Data Science preparation · R for Data Science free book official

studentPerspective: found · The recent accessible course-specific reports are few: the 2025 discussion describes an asynchronous Buyske offering and the May 2026 discussion reports Fall 2025 under Zhao. Searches for stat 295 and title/code variants otherwise surfaced mostly older, outside-three-year discussions; no identity ambiguity is present.
site:reddit.com/r/rutgers "stat 295" · site:reddit.com/r/rutgers "960:295" · site:reddit.com/r/rutgers "data wrangling rutgers" Zhao · site:reddit.com/r/rutgers "Data Wrangling" "960:295"

Spring 2027 sections

2 open · Livingston, Online

Section	Status	Instructor	Meeting	Campus
0116865	Open	Zhanyun Zhao	Monday 12:10 PM-1:30 PM at TIL 116; Thursday 12:10 PM-1:30 PM at TIL 116TIL 116	Livingston
9016866	Open	Steven Buyske	ONLINE INSTRUCTION(INTERNET)	Online

cachedSource: Checked-in Rutgers Schedule of Classes snapshotsUpdated when term datasets are refreshedMay be stale

SIRS teaching signals

Historical student surveys

Open full SIRS search

Teaching

4.46

Course quality

4.40

Response rate

65.8%

Coverage

5 offerings · 2022–2025

Instructor	Offerings	Teaching	Quality
Steven Buyske	3	4.80	4.77
Steve Buyske	1	4.70	4.70
Zhanyun Zhao	1	3.20	3.00

Course stats

Catalog and planning context

Credits

Current campuses

Livingston, Online

Current availability

2 open of 2

Catalog terms

Fall, Spring

Core codes

None listed

Loaded terms

Prerequisites

(01:198:142) OR (01:960:142) OR (01:960:212) OR (01:960:291) OR (01:960:384) OR (01:960:401)

Who’s teaching it — Spring 2027

Course guide

Is this for me?

Best for

Tough if

What you actually need

How it’s graded

What you’ll learn

Getting started with R and visual output

Transforming and joining data

Tidying, files, and cleaning

Reusable and reproducible workflows

External data and databases

Students say

Recurring applied work

Section format may change the experience

Check late rules directly

Do well in it

Treating the listed prerequisite as proof that you must already know R.

Assuming an older sample syllabus defines the current section's deadlines or exam format.

Expecting the course to focus on statistical modeling rather than the preparation of data for analysis.

Sources & coverage

Spring 2027 sections

SIRS teaching signals

Course stats

Prerequisites

Degree requirement lists

Related STATISTICS courses

Course Wiki

Who’s teaching it — Spring 2027

Course guide

Is this for me?

Best for

Tough if

What you actually need

How it’s graded

What you’ll learn

Getting started with R and visual output

Transforming and joining data

Tidying, files, and cleaning

Reusable and reproducible workflows

External data and databases

Students say

Recurring applied work

Section format may change the experience

Check late rules directly

Do well in it

Treating the listed prerequisite as proof that you must already know R.

Assuming an older sample syllabus defines the current section's deadlines or exam format.

Expecting the course to focus on statistical modeling rather than the preparation of data for analysis.

Sources & coverage

Spring 2027 sections

SIRS teaching signals

Course stats

Prerequisites

Degree requirement lists

Related STATISTICS courses