Memra

academic · beginner · ≈ 13 h · University of Waterloo

AFM 112 — Analytic Methods for Business 1

The data-analytics process from Sheets to Excel to R, on one company’s data — 90 lessons

The whole of AFM 112 as its open-book quizzes and final test it: how the course is assessed, the CRISP-DM process and how it serves the SAF problem-solving process, data and variable types, spreadsheet formula mechanics, descriptive statistics, data preparation with logical and lookup formulas, pivot tables, charts and visualisation, then the R half — the on-ramp with tiny vectors, the same twelve-row orders data frame the learner already knows from Sheets, data understanding and preparation in base R, simple models and how to read summary(lm()), and dashboards, deployment and the crew project. One company, Maple & Birch Office Supply, runs through all fourteen modules so the same numbers recur in both tools; every scenario question is multiple choice with the adjacent wrong tool as the distractor; every line of R output came from a real run.

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Open-book, online, and the final is the shock

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How This Course Is Examined, and the CRISP-DM Process

0 / 8 lessons
  1. Open-book, online, and the final is the shock 7 m
  2. What business analytics is, and who it serves 7 m
  3. Six phases, arrows both ways, an outer loop 8 m
  4. Business understanding: objectives, situation, data goal, plan 9 m
  5. The three phases where the tools live 9 m
  6. Business fit vs technical fit, and what "deploy" means here 8 m
  7. Client-facing stages, data-facing phases 8 m
  8. The primary MCQ skill, drilled 9 m

Data: Records, Variables, and Types

0 / 6 lessons
  1. Rectangular data, sources, and the tidy layout 8 m
  2. The first split, and the numeric-looking trap 8 m
  3. Nominal, ordinal, interval, ratio — as permitted operations 9 m
  4. The describe-data deliverable 8 m
  5. Three types that misbehave 10 m
  6. Blank, NA, −999, and why they must be declared 8 m

The Spreadsheet Grid and Formula Mechanics

0 / 4 lessons
  1. Addresses, ranges, and the fill handle 7 m
  2. `=`, operators, parentheses — and the same rule in R 8 m
  3. `$` and what the fill handle does to each 10 m
  4. A stable source, and checking your own formulas 8 m

Describing Data in a Spreadsheet

0 / 7 lessons
  1. Counting a categorical variable, three ways 9 m
  2. The centre, in both tools 9 m
  3. Which centre to report for money-like data 8 m
  4. The middle half and the 1.5·IQR rule 10 m
  5. Spread, sample vs population, and comparing across scales 10 m
  6. From a numeric column to a distribution 10 m
  7. Which cell answers which question 9 m

Data Preparation I: Derived Variables, Text, Dates, and IF

0 / 6 lessons
  1. One formula per row in Sheets, one line in R 9 m
  2. TRIM, UPPER, LEFT, & — and their R twins 9 m
  3. Days between, month buckets, and the epoch trap 8 m
  4. IF and ifelse, and the case-sensitivity conflict 9 m
  5. Compound conditions: functions in Sheets, operators in R 8 m
  6. Three-plus bands: IFS, nested IF, and cut() 10 m

Data Preparation II: Conditional Aggregates, Lookups, Sort and Filter

0 / 8 lessons
  1. Criteria are text, wildcards work, cells need & 9 m
  2. The sum range moves first in the plural form 10 m
  3. Lookup value, table, column index, match type 10 m
  4. FALSE for IDs, TRUE only for a sorted band table 9 m
  5. The modern lookups, and what each error means 9 m
  6. Keys, inner vs left, unmatched rows 9 m
  7. Records stay intact; filters hide, they do not delete 9 m
  8. Every M5–M6 operation, tagged 9 m

Pivot Tables: Aggregating and Comparing Groups

0 / 7 lessons
  1. The four drop zones 8 m
  2. One category → Rows; two → Rows × Columns; "for only…" → Filter 9 m
  3. Why is my total 12? 8 m
  4. "Of Ontario's sales" vs "of online sales" 10 m
  5. Months from dates, and the two ways to filter 8 m
  6. A cell is the summary of the rows matching both labels 9 m
  7. Pivot ⇔ SUMIFS ⇔ aggregate / tapply 10 m

Charts and Visualisation Principles

0 / 6 lessons
  1. A chart with no message is a table in disguise 7 m
  2. Bar for categories, histogram for a numeric, line for time, scatter for two numerics 9 m
  3. The time axis is categorical; stacked hides everything but the bottom 9 m
  4. The "which display is inappropriate" item 7 m
  5. Zero-based bars, honest time axes, index to 100 9 m
  6. Comparison, distribution, trend, relationship, composition 9 m

R and RStudio I: The Console, Vectors, and Types

0 / 6 lessons
  1. Reproducible, re-runnable, auditable 8 m
  2. [1] is an index, not a value 8 m
  3. A vector is a column 9 m
  4. One text entry turns the whole column chr 8 m
  5. Position, range, drop, condition — and R is 1-based 9 m
  6. ?mean, defaults, and the first R gotcha 10 m

R and RStudio II: Factors, Data Frames, and Reading Data

0 / 6 lessons
  1. Levels, and why their order matters 8 m
  2. Equal-length columns, $, nrow, dim, names 9 m
  3. "cannot open file" means the wrong folder 9 m
  4. str() is the data dictionary check 9 m
  5. Four messages, four causes 8 m
  6. Index prefixes, row names, truncated frames 8 m

Data Understanding in R

0 / 6 lessons
  1. Min · 1st Qu. · Median · Mean · 3rd Qu. · Max · NA's 9 m
  2. Counts, shares, margins 9 m
  3. df[rows, cols], subset(), and the trailing comma 10 m
  4. &, |, !, %in%, and the proportion idiom 9 m
  5. order() keeps records together; which.max is a position 8 m
  6. Look, then check 9 m

Data Preparation in R

0 / 7 lessons
  1. df$new <- …, names(), <- NULL 8 m
  2. Two-way flags and bands, vectorised 9 m
  3. as.numeric, as.Date(format =), gsub 9 m
  4. na.omit, imputation, duplicated 9 m
  5. The pivot, as a formula 10 m
  6. Join, stack, and the named-vector lookup 9 m
  7. Load → inspect → clean → derive → aggregate → write 10 m

Simple Models: Scatter, Correlation, the Line, and Reading lm()

0 / 7 lessons
  1. Response on the vertical axis, chosen from the decision 8 m
  2. Sign, strength, linear only, and never causation 9 m
  3. ŷ = b₀ + b₁x, residuals, least squares 10 m
  4. RSQ, FORECAST.LINEAR, the trendline, the ToolPak block 9 m
  5. Estimate, Multiple R-squared, Residual standard error 11 m
  6. Plug in, but only inside the x-range 9 m
  7. Sign, size, range, decision — and a pivot is a model too 9 m

Communicating Results: Dashboards, Deployment, and the End-to-End Walk

0 / 6 lessons
  1. One message per panel; six panels at most 8 m
  2. Data sheet → pivots → pivot charts → slicer; R feeds the data sheet 9 m
  3. Same colour per measure, zero-based bars, per-unit not totals 8 m
  4. Question stated? Right chart? Right aggregation? Fair comparison? Filters obvious? Units and dates? 9 m
  5. Who refreshes it, did it meet the criteria, how a crew ships 9 m
  6. The final's signature item 11 m
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