MFA-106 Business Analytics Notes from Unnati Educations cover all 4 units of the GGSIPU MBA (FA) Semester 2 paper in 72 pages, with 38 diagrams, 13 worked illustrations and 95 unit-wise exam questions. They follow the MBA (FA) scheme for batches from the 2021-22 session onwards and are sold as a printed book.
Written by Prateek Talwar, Founder, Unnati Educations · Reviewed by Ritik Rawat, B.Tech, Senior Full Stack Developer · Checked against MBA (Financial Analysis) Scheme and Syllabus, 2021-22 onwards · Updated 28 October 2026 · Editorial policy
Scheme: MBA (FA) 2021-22 onwards | Applies to: 2021-22 batch onwards | Verified against: official PDF, 28 October 2026
MFA-106 Business Analytics at a glance: GGSIPU MBA (FA) Sem 2 credits, units and exam pattern
| Code and title | MFA-106 Business Analytics |
|---|---|
| Course and semester | MBA (Financial Analysis), Semester 2 |
| Category and credits | Skill Enhancement · L-2, no T/P · 2 credits |
| Marks | 100: 50 internal (Class Test I 15 + Class Test II 15 + assignments, viva and participation 20) + 50 end-term |
| End-term pattern | Written exam of 50 marks; question pattern not printed in the official PDF |
| Units and hours | 4 units: 4 + 7 + 7 + 10 = 28 hours |
| Applies to | Batches from the 2021-22 academic session onwards |
| Paired lab | MFA-120 Business Analytics - Lab · P-2 · 1 credit · 40 internal + 60 external |
| Pass rules | No pass percentage in the MBA (FA) PDF; see GGSIPU Ordinance 11 |
Unit 4 alone takes 10 of the 28 hours. MFA-106 sits with ten other GGSIPU MBA (FA) Semester 2 papers.
MFA-106 Business Analytics Notes unit-wise: 13 worked illustrations and 95 exam questions across 4 units
These MFA-106 Business Analytics IPU notes follow the four official units in order, each closing with a Quick Revision table and a question list. Other papers are in GGSIPU MBA (FA) notes.
MFA-106 Unit 1: Introduction to Business Analytics
Pages 4-18. Four definitions of business analytics, its evolution from Taylor to AI, and BI vs BA. The DIKW hierarchy, data scales, the 5 Vs of Big Data, decision models by form, purpose and certainty, and the analyst's role in the BA process and CRISP-DM. 10 figures, 2 illustrations, 14 short and 8 long questions.
Quick look: two funds compared by EMV (from page 14)
| Fund | Bull (0.5) | Normal (0.3) | Bear (0.2) | EMV |
|---|---|---|---|---|
| Equity | ₹25,000 | ₹10,000 | -₹15,000 | ₹12,500 |
| Debt | ₹8,000 | ₹7,500 | ₹7,000 | ₹7,650 |
Quick look: payoffs on ₹1 lakh for one year (from page 14)
- Fund
- Equity
- Bull (0.5)
- ₹25,000
- Normal (0.3)
- ₹10,000
- Bear (0.2)
- -₹15,000
- EMV
- ₹12,500
- Fund
- Debt
- Bull (0.5)
- ₹8,000
- Normal (0.3)
- ₹7,500
- Bear (0.2)
- ₹7,000
- EMV
- ₹7,650
EMV picks equity; Maximin picks debt.
MFA-106 Unit 2: Exploring Data and Data Visualization
Pages 19-36, the spreadsheet unit. Pivot tables, pivot charts and chart choice, the lookups VLOOKUP, HLOOKUP, XLOOKUP and INDEX-MATCH, data validation, the What-if tools, Looker Studio and Power BI. 10 figures, 5 Excel step boxes, 2 illustrations, 15 short and 8 long questions.
Quick look: Scenario Manager on a profit model, fixed cost ₹4,00,000 (from page 28)
| Scenario | Price (₹) | Variable cost (₹) | Units | Profit (₹) |
|---|---|---|---|---|
| Best | 520 | 290 | 3,500 | 4,05,000 |
| Base | 500 | 300 | 3,000 | 2,00,000 |
| Worst | 480 | 320 | 2,500 | 0 |
Quick look: Profit = (Price - Variable cost) × Units - Fixed cost (from page 28)
- Scenario
- Best
- Price (₹)
- 520
- Variable cost (₹)
- 290
- Units
- 3,500
- Profit (₹)
- 4,05,000
- Scenario
- Base
- Price (₹)
- 500
- Variable cost (₹)
- 300
- Units
- 3,000
- Profit (₹)
- 2,00,000
- Scenario
- Worst
- Price (₹)
- 480
- Variable cost (₹)
- 320
- Units
- 2,500
- Profit (₹)
- 0
MFA-106 Unit 3: Data Preparation and Data Warehousing
Pages 37-54. Data preparation steps, MCAR, MAR and MNAR with deletion and imputation, outliers and erroneous data, then the warehouse: Inmon's characteristics, OLTP vs OLAP, architecture, data marts, ETL, data quality and the 1-10-100 rule, storage options up to lakehouses, and master data management. 9 figures, 2 illustrations, 16 short and 9 long questions.
Quick look: an outlier among 12 branches by the IQR method (from page 41)
- Sales (₹ lakh): 22, 25, 27, 28, 30, 31, 32, 33, 35, 36, 38, 95.
QUARTILE.INC: Q1 = 27.75, Q3 = 35.25, IQR = 7.5.- Fences: 16.5 and 46.5. 95 is above 46.5, so it is an outlier (z = 3.08 agrees).
- Without it the mean falls from 36.0 to 30.6; the median, 31.5, barely moves.
MFA-106 Unit 4: Predictive Modelling and Analysis, Data Reduction Techniques
Pages 55-71, also covered as "Predictive Modelling, Analysis and Data Reduction". Logic-driven vs data-driven modelling, model-building strategies, supervised learning, simple, multiple and logistic regression, then PCA, k-means and k-NN. 9 figures, 7 illustrations, 16 short and 9 long questions.
Quick look: a bank's logistic model for loan default (from page 63)
| Borrower | DTI | Late payment | z | Default probability |
|---|---|---|---|---|
| P | 50% | Yes | -0.3 | 42.6% |
| Q | 30% | No | -3.3 | 3.6% |
Quick look: z = -6 + 0.09 × DTI + 1.2 × LatePay; p = 1 ÷ (1 + e-z) (from page 63)
- Borrower
- P
- DTI
- 50%
- Late payment
- Yes
- z
- -0.3
- Default probability
- 42.6%
- Borrower
- Q
- DTI
- 30%
- Late payment
- No
- z
- -3.3
- Default probability
- 3.6%
ETL and the star schema in data warehousing: excerpt from page 46 of the MFA-106 notes
Excerpt from page 46
Star, snowflake and galaxy schemas
A data warehouse is usually organised around a central fact table that holds the numbers (sales amount, units, revenue) and keys that point to dimension tables such as date, product, customer and branch. How those dimension tables are arranged gives three common schemas.
| Schema | Structure | Feature |
|---|---|---|
| Star schema | One central fact table linked directly to de-normalised dimension tables | Simple, fast queries; most common |
| Snowflake schema | Dimension tables normalised into sub-dimensions (Product → Category) | Less redundancy, more joins, slower |
| Galaxy (fact constellation) | Several fact tables share common dimension tables | Used in large enterprise warehouses |
Warehouse schemas compared (excerpt, page 46)
- Schema
- Star schema
- Structure
- One central fact table linked directly to de-normalised dimension tables
- Feature
- Simple, fast queries; most common
- Schema
- Snowflake schema
- Structure
- Dimension tables normalised into sub-dimensions (Product → Category)
- Feature
- Less redundancy, more joins, slower
- Schema
- Galaxy (fact constellation)
- Structure
- Several fact tables share common dimension tables
- Feature
- Used in large enterprise warehouses
Do you know? Cloud data warehouses
Warehouses are increasingly built in the cloud: Snowflake, Google BigQuery, Amazon Redshift and Azure Synapse or Microsoft Fabric. These platforms keep storage and computing separate, scale on demand and charge on a pay-as-you-use basis, so even a mid-sized firm can now afford a data warehouse.
ETL processes
ETL stands for Extract, Transform, Load. It is the process of taking data from source systems, converting it into a clean and consistent format, and loading it into the data warehouse or another target. Think of ETL as the plumbing of the warehouse: in most warehouse projects it takes the largest share of the effort.
- Extract: read data from the sources, such as databases, ERP systems, files, APIs and the web.
- Transform: clean, standardise, integrate, aggregate and derive new fields in a staging area.
- Load: write the prepared data into warehouse tables, then refresh indexes and summaries.
Extract
Extraction reads data from homogeneous or heterogeneous sources, for example Oracle, SQL Server, SAP, Excel, CSV files and web APIs. The data is first copied into a staging area, so the live source systems are not disturbed while the warehouse is being fed. The pages after this one cover full versus incremental extraction, the transformation types with banking examples, load types and a full ETL vs ELT comparison.
Do the MFA-106 notes cover the full GGSIPU syllabus?
Yes. Every topic in the four official MFA-106 units has its own section in the notes; the unit check found no gap.
| Official unit | Official topics | Where in the notes | Status |
|---|---|---|---|
| Unit I | Concept and Scope, Importance of data in Business, Decision Models: Concept and Types, Analyst's role in the Business Analytics Model | Sections 1-5, pages 4-16 | Covered |
| Unit II | Pivot Tables, Pivot Charts, Look-up functions, Data Validation, what-if analysis; Google Dashboards and Power BI | Sections 1-9, pages 19-34 | Covered |
| Unit III | Missing Values, Outliers and erroneous data; Need for a data warehouse, ETL, Data Quality, Alternative Ways of Storing Data, Master data management | Sections 1-9, pages 37-52 | Covered |
| Unit IV | Logic driven modelling, strategies for predictive models, data-driven modelling, Supervised learning, simple, multiple and logistic regression; Principal Component Analysis, Clustering, k-Nearest Neighbors | Sections 1-10, pages 55-70 | Covered |
MFA-106 official syllabus topics mapped to the notes
- Official unit
- Unit I
- Official topics
- Concept and Scope, Importance of data in Business, Decision Models: Concept and Types, Analyst's role in the Business Analytics Model
- Where in the notes
- Sections 1-5, pages 4-16
- Status
- Covered
- Official unit
- Unit II
- Official topics
- Pivot Tables, Pivot Charts, Look-up functions, Data Validation, what-if analysis; Google Dashboards and Power BI
- Where in the notes
- Sections 1-9, pages 19-34
- Status
- Covered
- Official unit
- Unit III
- Official topics
- Missing Values, Outliers and erroneous data; Need for a data warehouse, ETL, Data Quality, Alternative Ways of Storing Data, Master data management
- Where in the notes
- Sections 1-9, pages 37-52
- Status
- Covered
- Official unit
- Unit IV
- Official topics
- Logic driven modelling, strategies for predictive models, data-driven modelling, Supervised learning, simple, multiple and logistic regression; Principal Component Analysis, Clustering, k-Nearest Neighbors
- Where in the notes
- Sections 1-10, pages 55-70
- Status
- Covered
Verify it yourself: MBA (Financial Analysis) scheme and syllabus, 2021-22 onwards (PDF), page 26.
The syllabus says Google Dashboards; the notes use its current name, Looker Studio. Every course in GGSIPU notes is mapped to its official PDF the same way.
How to write MFA-106 answers: Excel syntax, EMV and regression workings, and twelve-and-a-half-mark theory answers
MFA-106 questions (2.5 Marks and 12.5 Marks in the notes) come in three shapes.
Distinctions (BI vs BA, OLTP vs OLAP, ETL vs ELT, k-means vs k-NN): define each side in one line, then a four to six row table on fixed bases such as focus, data, users and tools.
Spreadsheet questions: syntax first, such as =VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]), one line per argument, then one call with its result. For What-if, give each tool one small numerical and add Solver.
Long theory answers: draw, then explain. The notes' tips ask for the four pivot-table areas, the BA process figure and the MDM hub. On outliers, never write "always delete them": they are investigated first, since they may be errors or genuine signals such as fraud.
Worked example: fit a regression line (Unit 4, long question 4)
Advertising X and sales Y (₹ lakh), six months: ΣX = 27, ΣY = 238, ΣXY = 1,175, ΣX² = 139. b = (6 × 1,175 - 27 × 238) ÷ (6 × 139 - 27²) = 624 ÷ 105 = 5.943; a = 39.667 - 5.943 × 4.5 = 12.924. So Ŷ = 12.92 + 5.94X, and ₹8 lakh of advertising predicts ₹60.47 lakh of sales. Check it with =SLOPE in Excel.
These are Business Analytics notes in English with Indian finance examples, so simple language is fine; structure carries the marks. Practise these formats on GGSIPU MBA (FA) important questions.
MFA-106 exam corner: 61 two-and-a-half-mark and 34 twelve-and-a-half-mark questions by unit
| Unit | Short (2.5 Marks) | Long (12.5 Marks) | Illustrations |
|---|---|---|---|
| Unit 1 | 14 | 8 | 2 |
| Unit 2 | 15 | 8 | 2 |
| Unit 3 | 16 | 9 | 2 |
| Unit 4 | 16 | 9 | 7 |
MFA-106 exam questions in the notes, by unit (2.5 Marks and 12.5 Marks, as printed)
- Unit
- Unit 1
- Short (2.5 Marks)
- 14
- Long (12.5 Marks)
- 8
- Illustrations
- 2
- Unit
- Unit 2
- Short (2.5 Marks)
- 15
- Long (12.5 Marks)
- 8
- Illustrations
- 2
- Unit
- Unit 3
- Short (2.5 Marks)
- 16
- Long (12.5 Marks)
- 9
- Illustrations
- 2
- Unit
- Unit 4
- Short (2.5 Marks)
- 16
- Long (12.5 Marks)
- 9
- Illustrations
- 7
The MFA-106 notes list 61 two-and-a-half-mark and 34 twelve-and-a-half-mark exam-style questions across 4 units, plus 13 worked illustrations. Each maps to a section of its unit. One per unit:
- Unit 1: What is a decision model? Name its components. Links decisions, uncontrollable inputs and outcomes; five components (page 11).
- Unit 2: Write the syntax of VLOOKUP and explain its arguments. Four arguments; FALSE means exact match, TRUE needs the first column sorted ascending (page 24).
- Unit 3: Distinguish between OLTP and OLAP. OLTP runs daily transactions on current data; OLAP analyses historical data (pages 44-45).
- Unit 4: R² vs adjusted R². R² is the share of variation in Y explained; adjusted R² corrects for the number of predictors and falls when a useless one is added (page 62).
More practice sits in the MFA-106 important questions.
Who writes and checks the MFA-106 Business Analytics notes?
- Prateek Talwar, author. Founder, Unnati Educations; Associate Company Secretary (ACS), ICSI, membership no. 58704, since 15 May 2019; B.Com, University of Delhi (SOL), 2012. Author profile
- Ritik Rawat, subject reviewer. B.Tech, Senior Full Stack Developer, Unnati Educations; reviews the spreadsheet, Power BI, warehousing and modelling content. Reviewer profile
These GGSIPU MBA (FA) Sem 2 Business Analytics notes (2026-27 edition, 72 pages) use finance examples: fund EMV, EMIs with PMT, default scoring. Updated 28 October 2026 under our editorial policy.
The same team prepares GGSIPU important questions for every course.
MFA-106 and the MFA-120 Business Analytics lab, plus other GGSIPU MBA (FA) Sem 2 papers
Paired lab: MFA-120 Business Analytics lab notes. P-2, 1 credit, 40 internal + 60 external.
The four official MFA-120 units follow the MFA-106 units one to one:
- Unit 1 → lab Unit I: data scales and decision-model exercises.
- Unit 2 → lab Unit II: lookups, validation, What-if, Google and Power BI dashboards.
- Unit 3 → lab Unit III: outliers by box plots, Mahalanobis' distance and Cook's D (lab only), plus four or more spreadsheet models.
- Unit 4 → lab Unit IV: testing regression models, PCA and cluster analysis.
Other Semester 2 papers: MFA-102 Financial Management, MFA-104 Investment Analysis and Portfolio Management, MFA-108 Macro and Mathematical Economics, MFA-110 Business Research, MFA-112 Marketing Management, MFA-114 Human Resource Management, MFA-116 Minor Project, MFA-118 Professional Skill Development (NUES) and MFA-122 MOOC/Open Elective.
MFA-106 Business Analytics notes FAQs
How do I get the full MFA-106 notes?
Message us on WhatsApp at 9899436384 with the code MFA-106. The full notes come only as a printed book, not as a PDF. Delivery takes a maximum of 5-7 days, depending on your location.
Is MFA-106 a theory paper or a lab?
MFA-106 is a theory paper. The MBA (FA) scheme lists it as Skill Enhancement with L-2 and 2 credits, with no tutorial or practical hours. The practical side is a separate paper, MFA-120 Business Analytics - Lab, with P-2 and 1 credit, which repeats the four MFA-106 units as hands-on exercises in spreadsheets, dashboards, regression, PCA and clustering.
What is the passing mark for MFA-106?
MFA-106 is marked out of 100: 50 internal (two class tests of 15 each plus 20 for assignments and participation) and 50 for the end-term written exam. The MBA (FA) scheme PDF does not print a pass percentage, so we do not quote one. For minimum marks, re-appear and result rules, read GGSIPU Ordinance 11 on ipu.ac.in.
Which MFA-106 unit needs the most preparation?
Unit 4. The syllabus gives it 10 of 28 teaching hours, and in the notes it holds 7 of the 13 worked illustrations: simple and multiple regression, a loan-default logistic model, PCA, k-means and k-NN. Its 16 short and 9 long questions tie with Unit 3 for the most. Work the regression arithmetic by hand at least once before the exam.
Do the MFA-106 notes match the current MBA (FA) syllabus?
Yes. We compared all four MFA-106 units with the MBA (Financial Analysis) Scheme and Syllabus, 2021-22 onwards, on ipu.ac.in, and every listed topic has a section in the notes. The only naming difference is Google Dashboards, taught under its current name, Looker Studio. If GGSIPU revises the syllabus, we update the notes and this page's date.
Are the MFA-106 exam questions answered in the notes?
Each unit ends with short (2.5 Marks) and long (12.5 Marks) questions, 95 in all, without separate model answers. Every question maps to a section of the same unit, and the Quick Revision table before each list gives a one-line summary of every concept. Use the sections as answer material and the exam tips inside each unit for structure.
Get the full MFA-106 Business Analytics Notes on WhatsApp
| Unit | Pages |
|---|---|
| Unit 1: Introduction to Business Analytics | 4-18 (15) |
| Unit 2: Exploring Data and Data Visualization | 19-36 (18) |
| Unit 3: Data Preparation and Data Warehousing | 37-54 (18) |
| Unit 4: Predictive Modelling and Data Reduction | 55-71 (17) |
Pages per unit in the full MFA-106 notes book
- Unit
- Unit 1: Introduction to Business Analytics
- Pages
- 4-18 (15)
- Unit
- Unit 2: Exploring Data and Data Visualization
- Pages
- 19-36 (18)
- Unit
- Unit 3: Data Preparation and Data Warehousing
- Pages
- 37-54 (18)
- Unit
- Unit 4: Predictive Modelling and Data Reduction
- Pages
- 55-71 (17)
A 72-page printed book, not a PDF. Order on WhatsApp; delivery takes a maximum of 5-7 days, and the rest depends on your location.
Order the full MFA-106 notes book on WhatsApp (4 units, 72 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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