MBA(A) 113 Introduction to Analytics and R Notes from Unnati Educations cover all 4 units of the GGSIPU MBA (Analytics) Semester 1 paper in a 59-page book, with 73 worked R code examples, 24 solved short answers and 29 practice long questions. They follow the MBA (Analytics) Scheme and Syllabus 2022-24 onwards.
Written by Prateek Talwar, Founder, Unnati Educations · Reviewed by Ritik Rawat, B.Tech, Senior Full Stack Developer · Checked against the MBA (Analytics) Scheme and Syllabus 2022-24 onwards (ipu.ac.in) · Updated 22 October 2026 · Editorial policy
Scheme: MBA (Analytics) Scheme and Syllabus 2022-24 onwards | Applies to: 2022-24 onwards (as printed on the PDF cover) | Verified against: syllMBAAnalytics180225.pdf, Semesters 1-2 approved by the Academic Council on 22.03.2023
MBA(A) 113 Introduction to Analytics and R at a glance: GGSIPU MBA (Analytics) Sem 1 credits, units and exam pattern
MBA(A) 113 is the first MBA (Analytics) paper where you write R code yourself.
| Code and title | MBA(A) 113 Introduction to Analytics and R |
|---|---|
| Course and semester | MBA (Analytics), First Semester |
| Category and credits | Skill Enhancement · L 2 · 2 credits |
| Teaching hours | 28 (Units I to IV: 8, 6, 7, 7) |
| Internal assessment | 25: Class Test-I 15 + presentation, viva-voce or group discussion 10 |
| End-term marks, total and pattern | Not printed in the official PDF |
| Applies to | Scheme 2022-24 onwards; Semesters 1-2 approved 22.03.2023 |
| Paired lab | MBA(A) 153 Introduction to Analytics and R Lab · P 2 · 1 credit |
The scheme says it follows Ordinance 11, so end-term rules for MBA(A) 113 come from Ordinance 11 (examinations and evaluation, ipu.ac.in).
MBA(A) 113 sits with ten other first-semester codes on the GGSIPU MBA (Analytics) Semester 1 papers page.
MBA(A) 113 Introduction to Analytics and R Notes unit-wise: 73 R code examples and 24 solved short answers across 4 units
These GGSIPU MBA (Analytics) Sem 1 Introduction to Analytics and R notes run to 59 pages; every R code box shows its output.
Other papers: GGSIPU MBA (Analytics) notes.
MBA(A) 113 Unit 1: Introducing to R
Also covered as "Introducing R" (pages 4 to 21). After the four types of analytics and the six data structures, most pages go to vectors: recycling, NA vs NULL, filtering, ifelse(). Data frames, factors, control statements, recursion and set.seed() simulation follow. 28 R code examples.
Quick look: grading with nested ifelse() (from page 10)
marks <- c(45, 78, 62, 30, 90)
ifelse(marks >= 75, "A", ifelse(marks >= 50, "B", "C"))Output: "C" "A" "B" "C" "A", with no loop.
MBA(A) 113 Unit 2: Matrices, Arrays and Lists
Pages 22 to 33. A region-by-product sales matrix runs through t(), det(), solve() and apply(), then 3-D arrays of region, product and year. Lists close the unit: [ ] vs [[ ]], lapply() and recursive lists. 16 R code examples.
Quick look: keeping a row as a matrix (from page 27)
m <- matrix(1:6, nrow = 2)
dim(m[1, ]) # NULL: dimension dropped
dim(m[1, , drop = FALSE]) # 1 3: still a matrixMBA(A) 113 Unit 3: Statistics
Also covered as "Statistics using R" (pages 34 to 47). Summary measures lead into three distributions, t-tests, chi-square and ANOVA, then correlation, lm() and logistic glm(), time series with HoltWinters(), factor analysis and clustering. 18 R code examples.
| Business question | Technique | R function |
|---|---|---|
| Is gender related to brand choice? | Chi-square test | chisq.test() |
| Do 3 ad strategies differ in sales? | One-way ANOVA | aov() |
| Will a customer buy or not? | Logistic regression | glm() |
| How can customers be segmented? | Cluster analysis | kmeans() |
Quick look: which technique for which business question (from page 46)
- Business question
- Is gender related to brand choice?
- Technique
- Chi-square test
- R function
chisq.test()
- Business question
- Do 3 ad strategies differ in sales?
- Technique
- One-way ANOVA
- R function
aov()
- Business question
- Will a customer buy or not?
- Technique
- Logistic regression
- R function
glm()
- Business question
- How can customers be segmented?
- Technique
- Cluster analysis
- R function
kmeans()
MBA(A) 113 Unit 4: Advanced R Programming
Pages 48 to 58. R talks to C++ (Rcpp), Python (reticulate) and SQL (DBI); text mining scores sentiment; a neuron's sigmoid output is worked by hand. Monte Carlo risk, a decision tree on iris and market basket analysis finish it. 11 R code examples.
Quick look: estimating π by Monte Carlo (from page 53)
set.seed(42)
x <- runif(100000); y <- runif(100000)
4 * mean(x^2 + y^2 <= 1) # about 3.14Market basket analysis in business analytics: excerpt from page 56 of the MBA(A) 113 notes
Excerpt from page 56
Meaning
Market Basket Analysis (MBA in the notes, not to be confused with the degree) is a data-mining technique that finds which products are often bought together by studying customer transactions. Its output is a set of association rules such as {Bread} → {Butter}, read as "customers who buy bread also tend to buy butter". The method is based on the Apriori algorithm of Agrawal and Srikant (1994).
Where businesses use it
The notes list store layout and shelf placement, cross-selling and "frequently bought together" suggestions on e-commerce sites, combo offers and bundle pricing, catalogue design and targeted promotions.
The three measures of a rule A → B
| Measure | Formula | What it tells you |
|---|---|---|
| Support | P(A and B) | How often the items appear together |
| Confidence | Support(A and B) ÷ Support(A) | How often B is bought when A is bought |
| Lift | Confidence ÷ Support(B) | Above 1 positive, 1 independent, below 1 negative |
Support, confidence and lift for a rule A → B (excerpt, page 56)
- Measure
- Support
- Formula
- P(A and B)
- What it tells you
- How often the items appear together
- Measure
- Confidence
- Formula
- Support(A and B) ÷ Support(A)
- What it tells you
- How often B is bought when A is bought
- Measure
- Lift
- Formula
- Confidence ÷ Support(B)
- What it tells you
- Above 1 positive, 1 independent, below 1 negative
Worked illustration: five grocery transactions
T1: Bread, Milk · T2: Bread, Butter, Milk · T3: Bread, Butter · T4: Milk, Eggs · T5: Bread, Butter, Milk, Eggs.
Rule {Bread} → {Butter}: bread and butter appear together in 3 of 5 baskets, so support = 3/5 = 0.60. Bread appears in 4 baskets, so confidence = 3/4 = 0.75. Butter alone has support 3/5, so lift = 0.75 ÷ 0.60 = 1.25: a positive, strong rule. In store terms, butter belongs on the shelf next to bread.
Rule {Butter} → {Milk}: the pair appears in 2 baskets, so support = 2/5 = 0.40. Butter appears in 3 baskets, so confidence = 2/3 = 0.67. Milk has support 4/5, so lift = 0.67 ÷ 0.80 = 0.83. Lift below 1 shows a weak, negative association, so a butter-and-milk combo offer is not backed by this data.
On the next page, the notes run the same five baskets through apriori() with minimum support 0.4 and confidence 0.7, then sort the rules by lift.
MBA(A) 113 exam corner: 24 solved short answers and 29 ten-to-fifteen-mark practice questions by unit
Each unit ends with six solved short questions and long practice questions marked "10–15 Marks" (no written answers for these).
| Unit | Solved short answers | Practice (10–15 Marks) | R code examples |
|---|---|---|---|
| Unit 1 | 6 | 8 | 28 |
| Unit 2 | 6 | 7 | 16 |
| Unit 3 | 6 | 8 | 18 |
| Unit 4 | 6 | 6 | 11 |
MBA(A) 113 exam questions and R code examples per unit
- Unit
- Unit 1
- Solved short answers
- 6
- Practice (10–15 Marks)
- 8
- R code examples
- 28
- Unit
- Unit 2
- Solved short answers
- 6
- Practice (10–15 Marks)
- 7
- R code examples
- 16
- Unit
- Unit 3
- Solved short answers
- 6
- Practice (10–15 Marks)
- 8
- R code examples
- 18
- Unit
- Unit 4
- Solved short answers
- 6
- Practice (10–15 Marks)
- 6
- R code examples
- 11
The MBA(A) 113 notes solve 24 short-answer questions across 4 units, list 29 practice questions marked 10–15 Marks, and carry 73 R code examples with output.
- Unit 1: NA vs NULL in R? NA is a missing value inside an object (length 1); NULL means no object (length 0).
- Unit 2: * vs %*%?
*multiplies matching elements;%*%is matrix multiplication and needs columns of A equal to rows of B. - Unit 3: One-way vs two-way ANOVA? One factor vs two factors; with replication, two-way also tests interaction.
- Unit 4: Classification vs clustering? Supervised with predefined classes vs unsupervised groups found in unlabelled data.
More practice sits in the MBA(A) 113 important questions.
Who writes and checks the MBA(A) 113 Analytics and R notes?
- Prateek Talwar, author. Founder, Unnati Educations. Associate Company Secretary (ACS), ICSI, membership no. 58704, since 15 May 2019. B.Com, University of Delhi (School of Open Learning), 2012. Author bio
- Ritik Rawat, subject reviewer for the R code and statistics. B.Tech. Senior Full Stack Developer, Unnati Educations. Reviewer bio
The 2026-27 edition of these MBA(A) 113 Introduction to Analytics and R IPU notes maps every section to the 2022-24 onwards scheme and includes a "Which Technique When?" chart in Unit 3. Last updated 22 October 2026, under our editorial policy.
The same team prepares GGSIPU important questions for every course.
Do the MBA(A) 113 notes cover the full GGSIPU syllabus?
Yes. Every topic in the four official units of MBA(A) 113 has a matching section in the notes; our unit check found no gap.
| Official unit | Official topics | In the notes | Status |
|---|---|---|---|
| I: Introducing to R | R data structures, help functions, vectors, NA and NULL, filtering, vectorised if-then-else, data frames, factors and tables, control statements, operators, recursion, replacement functions, math and simulations | Sections 1-10, pages 4-21 | Covered |
| II: Matrices, Arrays and Lists | Matrix operations, applying functions to rows and columns, vector/matrix distinction, avoiding dimension reduction, higher dimensional arrays, lists, recursive lists | Sections 1-8, pages 22-32 | Covered |
| III: Statistics | Descriptive statistics, graphs, binomial, Poisson and normal distributions, hypothesis testing, ANOVA, correlation, linear and logistic regression, time series, factor and cluster analysis | Sections 1-10, pages 34-46 | Covered |
| IV: Advanced R Programming | Interfacing R to other languages, text mining, neural networks, Monte Carlo methods, classification, market basket analysis | Sections 1-6, pages 48-57 | Covered |
MBA(A) 113 official syllabus mapped to the notes
- Official unit
- I: Introducing to R
- Official topics
- R data structures, help functions, vectors, NA and NULL, filtering, vectorised if-then-else, data frames, factors and tables, control statements, operators, recursion, replacement functions, math and simulations
- In the notes
- Sections 1-10, pages 4-21
- Status
- Covered
- Official unit
- II: Matrices, Arrays and Lists
- Official topics
- Matrix operations, applying functions to rows and columns, vector/matrix distinction, avoiding dimension reduction, higher dimensional arrays, lists, recursive lists
- In the notes
- Sections 1-8, pages 22-32
- Status
- Covered
- Official unit
- III: Statistics
- Official topics
- Descriptive statistics, graphs, binomial, Poisson and normal distributions, hypothesis testing, ANOVA, correlation, linear and logistic regression, time series, factor and cluster analysis
- In the notes
- Sections 1-10, pages 34-46
- Status
- Covered
- Official unit
- IV: Advanced R Programming
- Official topics
- Interfacing R to other languages, text mining, neural networks, Monte Carlo methods, classification, market basket analysis
- In the notes
- Sections 1-6, pages 48-57
- Status
- Covered
Verify it in the official MBA (Analytics) Scheme and Syllabus 2022-24 onwards (PDF, ipu.ac.in); MBA(A) 113 is on pages 21 and 22.
Every course in GGSIPU notes is mapped to its official PDF the same way.
How to write MBA(A) 113 answers: R code with output, one-line interpretation and ten-to-fifteen-mark theory answers
A strong MBA(A) 113 answer has three layers: a definition, a few lines of R code with output, and a business reading of that output. Examiners in an analytics paper want to see that you can read the result, not only type the command. The exam tips printed in the notes give these patterns:
- Data structures: draw the homogeneous vs heterogeneous chart (Figure 1.2), add the comparison table and one line of code per structure.
- Loops: after
for,whileandrepeat, add that loops are slow on big data and R users prefer vectorised functions and the apply family. - Matrices: state that
*is element-wise and%*%is matrix multiplication;A * Bfor a matrix product is a mistake the notes flag. - Lists: five headings: creation, accessing (
[ ],[[ ]],$), general operations, applying functions, recursive lists, each backed by 2 to 3 lines of code. - Market basket numericals: write the three formulas first, show each step, then interpret lift.
- Clustering and simulation: mention
scale()before clustering when variables use different units, andset.seed()so simulated results can be reproduced. - Small slips the notes warn about: R is case-sensitive, indexing starts at 1, and
0.1 + 0.2 == 0.3is FALSE, so compare decimals withall.equal().
Worked example: regression with an interpretation line (page 42)
adv <- c(10, 12, 15, 18, 20, 25) # ad spend, Rs thousand
sales <- c(40, 45, 52, 58, 63, 75) # sales, Rs lakh
model <- lm(sales ~ adv)
coef(model)Output: intercept 17.063877, slope 2.306167; summary(model) reports R-squared 0.9992 and a p-value of 2.424e-07.
Write under it: "Sales = 17.06 + 2.31 × Adv. Each extra Rs 1,000 of advertising raises sales by about Rs 2.31 lakh, and advertising explains 99.9% of the variation in sales." Add the p-value line too: below 0.05, so advertising is a significant predictor. That interpretation turns code into an analytics answer.
Practise this format on GGSIPU MBA (Analytics) important questions.
MBA(A) 113 and the MBA(A) 153 Analytics and R lab, plus other GGSIPU MBA (Analytics) Sem 1 papers
MBA(A) 153 Analytics and R lab notes: P 2, 1 credit. The scheme bases the lab on MBA(A) 113 and asks for a lab file signed by the faculty.
- R data structures → Units 1 and 2 (pages 4-32).
- Correlation and regression → Unit 3, sections 6-7 (pages 41-44).
- Neural networks → Unit 4, section 3 (pages 51-52).
- Market basket analysis → Unit 4, section 6 (pages 56-57).
Other Semester 1 papers: MBA(A) 101 Management Process and Organisational Behaviour, 103 Accounting for Management, 105 Economics and Quantitative Analysis, 107 Information Technology for Management, 109 Communication in Organizations, 111 Marketing Management, 115 Data Preparation and Exploration, and labs 151 and 155. MBA(A) 115 Unit IV needs the hypothesis testing and clustering taught here in Unit 3.
MBA(A) 113 Analytics and R notes FAQs
How do I get the full MBA(A) 113 notes?
Message 9899436384 on WhatsApp using the "Get Full Notes" button. The full MBA(A) 113 notes come as a printed book only and are not available as a PDF. Delivery takes a maximum of 5 to 7 days, and the exact time depends on your location.
What is the passing mark for MBA(A) 113?
The official MBA (Analytics) scheme prints only the internal assessment for MBA(A) 113: 25 marks, made of Class Test-I (15) and a presentation, viva-voce or group discussion (10). It does not print the end-term marks, the total or a pass percentage. Its rules follow Ordinance 11, so check end-term minimums and re-appear rules in Ordinance 11 itself.
Do the MBA(A) 113 notes match the 2022-24 onwards scheme?
Yes. The MBA(A) 113 notes were checked unit by unit against the MBA (Analytics) Scheme and Syllabus 2022-24 onwards, whose first two semesters were approved on 22.03.2023. Every listed topic, from vector equality in Unit I to market basket analysis in Unit IV, has a section. The syllabus map above gives the page range for each unit.
Which unit of MBA(A) 113 has the most R code?
Unit 1, Introducing to R. It runs from page 4 to page 21 and carries 28 of the 73 R code examples in the MBA(A) 113 notes, mostly on vectors, data frames, factors and functions. Unit 3 has 18 examples, Unit 2 has 16 and Unit 4 has 11, and each one is printed with its output.
Do I need coding experience before MBA(A) 113?
The notes do not assume any. The MBA(A) 113 notes start with R as a calculator, the <- assignment arrow and installing a package, then add one data structure at a time. Code boxes are short and show output, so you can follow the logic before typing it in RStudio. Unit 4 is heavier but reuses Units 1 to 3.
How is MBA(A) 113 different from the MBA(A) 153 lab?
MBA(A) 113 is the theory paper: 2 lecture hours a week, 2 credits, with a class test and presentation in its internal assessment. MBA(A) 153 is its lab: 2 practical hours, 1 credit, where you run data structures, regression, neural networks and market basket analysis yourself and keep a lab file signed by your faculty.
Get the full MBA(A) 113 Introduction to Analytics and R Notes on WhatsApp
| Unit | Pages | R code examples | Solved short answers |
|---|---|---|---|
| Unit 1 | 4-21 (18) | 28 | 6 |
| Unit 2 | 22-33 (12) | 16 | 6 |
| Unit 3 | 34-47 (14) | 18 | 6 |
| Unit 4 | 48-58 (11) | 11 | 6 |
Pages and solved content per unit in the MBA(A) 113 notes book
- Unit
- Unit 1
- Pages
- 4-21 (18)
- R code examples
- 28
- Solved short answers
- 6
- Unit
- Unit 2
- Pages
- 22-33 (12)
- R code examples
- 16
- Solved short answers
- 6
- Unit
- Unit 3
- Pages
- 34-47 (14)
- R code examples
- 18
- Solved short answers
- 6
- Unit
- Unit 4
- Pages
- 48-58 (11)
- R code examples
- 11
- Solved short answers
- 6
The book is a set of Introduction to Analytics and R notes in English: 59 pages, printed book only (not a PDF). Delivery takes a maximum of 5 to 7 days, depending on your location.
Order the MBA(A) 113 notes book on WhatsApp (4 units, 59 pages)
Unnati Educations is an independent education provider based in Vikaspuri, Delhi. We are not affiliated with, authorised by, or endorsed by Guru Gobind Singh Indraprastha University (GGSIPU). 'GGSIPU' and 'IPU' are used only to describe the syllabus our notes follow. For official information, visit ipu.ac.in.
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