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A working model is where analytics marks begin, not where they end. The grade sits in whether your cleaning is logged, your metrics match the cost of each error, and your recommendation reaches a decision.
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The analytics major runs on the ANL series: Python, data mining, machine learning, forecasting and text mining, feeding into an applied project. Several codes were renumbered and the old pages are still live.
| Older code | Current equivalent | What changed |
|---|---|---|
| ANL251 Python Programming | ANL252 Python for Data Analytics | Same ground, reframed around analytics rather than programming |
| ANL303 Fundamentals of Data Mining | ANL304 Foundations of Data Analytics | The published topic lists are word for word identical |
| ANL307 Predictive Modelling | ANL308 Machine Learning Applications | Adds bagging, boosting, random forests and deep neural networks |
| ANL305 Association and Clustering | Folded into ANL304 | Clustering and association rule mining now sit inside the foundations course |
Plus the business courses alongside it: accounting, financial management, marketing management and business skills. The applied project is the capstone, defended before a supervisor and an examiner.
The published exam timetable is the only reliable guide, because course pages do not state assessment splits. In one recent semester ANL304, ANL308, ANL310 and ANL317 had two-hour online exams, while ANL252, ANL312, ANL321, ANL355 and the applied project did not appear.
Modes change by semester, so read your own timetable. Either way, you must pass the continuous assessment and the examinable component separately.
Different ones, and the published topic lists say so. Python with pandas and scikit-learn for the foundations courses, R or a modeller for text mining, SAS for forecasting, no new tool for deployment.
| Module | Tool | What the topics actually cover |
|---|---|---|
| ANL252 | Python, plus SQL from inside Python | numpy, pandas, matplotlib, scikit-learn trees and clustering, and evaluating AI-generated code |
| ANL304 | Python in a cloud notebook | Association rule mining, cluster analysis, predictive modelling, and a documented process framework |
| ANL308 | Python | Logistic regression, decision trees, bagging and boosting, random forests, support vector machines, neural networks, parameter tuning |
| ANL310 | No new tool | Deployment, association versus causality, conditions for causality, alternative explanations, model latency |
| ANL312 | R, or a statistical modeller as the alternative | Text preprocessing, document classification and clustering, topic modelling, sentiment analysis, large language models |
| ANL317 | SAS | Moving averages, exponential smoothing, stationarity, ARMA, ARIMA and seasonal ARIMA |
| ANL321 | Statistical software | Ordinary least squares, regression assumptions, omitted variable bias, multicollinearity, maximum likelihood |
The deployment module asks whether your model's association is a cause. That is where a technically correct report most often fails.
Analytics is taught in four tiers: what happened, why it happened, what might happen next, and what should be done. Most reports do the first three and then write recommendations with nothing prescriptive in them.
Summarising what the data shows about the past. The easiest marks, and the tier students over-invest in.
Explaining the pattern rather than restating it: association versus causality, the conditions for claiming cause, and the explanations you have not ruled out.
A model is built, evaluated and reported, and the paper ends. Recommendations then restate the findings in the imperative mood.
Choosing between actions under constraints, with the trade-off stated: which customers to contact on a fixed retention budget, which route to run with the fleet you have. Operations research teaches this.
A coefficient is reported correctly only when the sentence carries the number, the unit of the predictor and the unit of the outcome. Published guidance defines it as the predicted change in the outcome per one-unit increase, holding the other variables constant.
Pasted
Price: B = -0.42, SE = 0.11, t = -3.82, p = .000
Reported
Each additional dollar on unit price is associated with 0.42 fewer units sold per store-week, holding promotion and store size constant.
Pasted
R² = 0.63, Adjusted R² = 0.61
Reported
The model accounts for roughly three fifths of the variation in weekly sales, so the recommendation cannot rest on it alone.
Pasted
Promotion importance: 0.41 (highest)
Reported
Promotion ranks highest in this fitted model's importance scores, measured on held-out data. That makes it important to the model, not a driver of sales.
One SUSS analytics course lists regression assumptions, omitted variable bias and multicollinearity as named topics. If a variable is missing, or two predictors move together, your coefficient is not holding anything constant.
Because on a dataset where one in ten customers churns, a model that predicts nobody churns is ninety per cent accurate and finds nothing. Report precision and recall with the counts behind them, and tie your metric to what each error costs.
A churn model on 4,000 customers, 412 of whom actually churned
51
Caught
The only cell where a retention offer reaches someone who was leaving.
361
Missed
Customers lost with no intervention. Each one costs a full customer lifetime.
113
False alarm
A retention voucher for someone who was never leaving. Cheap, but it scales.
3,475
Correctly left alone
The cell that makes the accuracy figure look good and tells you nothing.
Accuracy 88%. Almost all of it comes from the bottom-right cell, which required no skill.
Recall 0.12. Of the 412 who left, the model found 51. That is what the business cares about.
Precision 0.31. Of 164 flagged, 51 churned. That decides how much of the budget is wasted.
The sentence to write is not that accuracy was eighty-eight per cent, but that the model found fifty-one of four hundred and twelve churners. Balanced accuracy exists because conventional accuracy flatters an imbalanced set.
In three parts. State how many clusters and why, using a silhouette score or a business constraint such as the campaigns marketing can run. Name each cluster and the variables that separate it. Then say what you would do differently to each.
The coefficient compares a point's closeness to its own cluster against the nearest other. One is best and minus one worst, near zero means overlap, and negative values mean a point is in the wrong cluster.
A cluster you cannot name in five words is a cluster you have not understood. One SUSS text-mining course makes naming topics an explicit requirement.
The importance scores off a fitted tree are described in the scikit-learn documentation as strongly biased: they favour high-cardinality features over binary or small categorical variables.
Permutation importance avoids that, because it can be computed on unseen data. Features important on the training set but not the held-out set may be causing the overfitting.
Testing a model on the data it learned from. The documentation calls this a methodological mistake: the model repeats the labels it has just seen and predicts nothing useful.
By logging each decision instead of summarising the lot. One SUSS end-of-course paper puts thirty of its hundred marks on preparing the data, written up in three hundred words. A sentence saying the data was cleaned earns none of them.
“The dataset was cleaned by removing missing values, correcting data types and handling outliers before modelling.”
“Dropped 214 rows, 3.1 per cent of the sample, with missing tenure. Median imputation was considered and rejected because the missingness concentrated in accounts opened in the final month, so imputing would have manufactured tenure for genuinely new customers.”
One row per decision: what you found, how many rows or cells it touched, what you did, and the option you rejected. Tidy data means each variable a column, each observation a row, each type of unit its own table.
Several SUSS modules name a standard process framework: business understanding, data understanding, preparation, modelling, evaluation and deployment. Its guidance is that the sequence is not rigid, and the log shows that movement.
One SUSS paper allocates thirty marks to three figures with four hundred and fifty words across them, roughly a hundred and fifty words per chart. Those words must state the comparison the chart makes and what it means for the decision.
Visualisation guidance is explicit that bars on a linear scale should always start at zero, because the shaded area has to be proportional to the value. On a log scale they start at one, representing ratios.
People judge distance more accurately than area, which is why a bar chart beats a pie chart or treemap.
Decide the sentence before you build the chart. If the sentence is that two segments behave differently after a price change, the chart shows those two series on a common axis.
SUSS briefs say so directly: less relevant detail belongs in an appendix, word limits exclude code and references, and one group brief puts ten marks on report-writing standards alone. The body carries decision, method, finding, limitation and recommendation.
If a sentence names a library or a hyperparameter and contains no business noun, it is appendix material. A package name is not a finding, and the grid you searched is not a result.
A named business decision. One text-mining paper puts twelve marks on the project background, objectives and the role analytics plays in meeting them.
Prior literature. The same paper puts twenty of a hundred marks on analysing two research articles.
The alternative you did not use. Another paper gives ten marks for discussing alternative analytics methods that would strengthen the insight.
Defensibility out loud. The applied project's outcomes include debating effectiveness and defending results to supervisors.
Analytics work stalls in four places: what business question the brief is setting, getting the code to run, writing up output so it says something, and compressing it into a word limit that excludes code.
We work out the business decision behind the brief, which tier it is asking for, and what each marked section needs.
The analysis and the report, with the cleaning logged, the model justified, the metrics tied to cost, and the code in the appendix.
Python, R or SAS depending on your module. Getting the notebook to run, the splits done properly, the output usable.
Read as a marker would: pasted output, accuracy on an imbalanced set, importance from a training set, charts without claims.
The person who takes your analytics brief works in this field already. See all experts.
BSc in Business Analytics, PSB Academy
4.8/5
Specialist in: Dashboards, data reports, SPSS
Experience: 4 years analytics, 3 years writing
BSc in Finance, Singapore Institute of Management
4.8/5
Specialist in: Financial modelling, cost accounting
Experience: 4 years finance, 5 years writing
BEng in Info Systems Engineering, SIT
4.7/5
Specialist in: Security analysis, database design
Experience: 4 years IT, 3 years academic
BSc in IT & Data Systems, SMU
4.7/5
Specialist in: ERD, SQL, system analysis
Experience: 5 years backend, 3 years assignment work
MSc in Marketing Analytics, NTU
4.8/5
Specialist in: Consumer behaviour, digital strategy
Experience: 7 years in marketing, 5 years academic
MSc in Educational Psychology, NUS
5.0/5
Specialist in: Learning theory, developmental needs
Experience: 8 years in education, 5 years writing
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You pay for the part you hand over. Scoping costs less than running the analysis, and an interpretation check less than both.
| Per 250 words | 10+ days | 3–5 days | 24 hours |
|---|---|---|---|
| Scope and analysis plan | S$9 | S$12 | S$18 |
| Written from scratchmost students start here | S$26 | S$35 | S$50 |
| Analysis and debugging | S$12 | S$16 | S$22 |
| Review and interpretation check | S$8 | S$11 | S$15 |
Indicative rates in Singapore dollars, to show how the work is priced. Your own brief, length and deadline decide the quote, and you see it in writing first. Nothing is charged until you confirm the scope, and revisions and refunds follow our terms of work.
Send the brief and your module code. We reply with the business decision behind it, which tier it is asking for, and a fixed quote.
Straight answers on modules and tools, reporting models, metrics, data preparation, privacy and price. More in our full FAQ.
The ANL series carries the major: Python for data analytics, foundations of data analytics, machine learning applications, business analytics applications and issues, text mining, business forecasting, statistical methods and applied operations research, feeding into the applied project.
Effectively yes. The published topic lists for ANL303 and ANL304 are word for word identical, ANL251 became ANL252, ANL307 became ANL308 with bagging, boosting, random forests and deep neural networks added, and ANL305's clustering and association content now sits inside ANL304.
The exam timetable is the only reliable source, because SUSS course pages do not publish assessment splits. In one recent semester ANL304, ANL308, ANL310 and ANL317 had two-hour online exams, while ANL252, ANL312, ANL321, ANL355 and the applied project did not appear.
The topic lists differ. Python with numpy, pandas, matplotlib and scikit-learn for ANL252 and ANL304, plus SQL from inside Python. R or a statistical modeller for text mining, SAS for forecasting, and no new tool for ANL310, which covers deployment and causality.
With three things in the sentence: the number, the unit of the predictor and the unit of the outcome. A coefficient is the predicted change in the outcome per one-unit increase in the predictor, holding the other variables constant.
Because on an imbalanced dataset it can come almost entirely from the majority class. If one customer in ten churns, predicting that nobody churns scores ninety per cent and finds nothing. Report precision and recall with the counts behind them.
Precision is not labelling a negative case as positive: of the customers you flagged, how many actually churned. Recall is finding all the positive cases: of those who churned, how many you flagged. Which matters depends on the cost of each error.
By evidence and by constraint. Report a silhouette score across a range of cluster counts, where one is the best value, near zero means the clusters overlap and negative values mean a point is in the wrong cluster. Then add the business constraint.
Not without a caveat. The scikit-learn documentation describes impurity-based importance for trees as strongly biased, favouring high-cardinality features over binary or small categorical ones, and warns it can rate features highly that are not predictive on unseen data. Permutation importance on held-out data avoids that.
Because the documentation calls it a methodological mistake: a model that repeats the labels it has just seen scores perfectly and predicts nothing useful. Tuning on the test set repeats the problem one level up, which is why a validation split or cross-validation exists.
As a log, not a summary. One SUSS paper puts thirty of a hundred marks on preparing the data in three hundred words. One row per decision: what you found, how many rows it affected, what you did, and the alternative you rejected.
Descriptive asks what happened, diagnostic why it happened, predictive what might happen next, and prescriptive what should be done. It maps onto the SUSS sequence, from data exploration to causality, then modelling and forecasting, then operations research. Most reports stop at predictive.
The appendix. SUSS briefs state that less relevant detail belongs in an appendix and that word limits exclude code and references, and one group brief puts ten marks on report-writing standards alone.
Less than you think. One paper allocates thirty marks to three figures across four hundred and fifty words, which is roughly a hundred and fifty words each. Those words have to state the comparison the chart makes and what it means for the decision.
It depends on the module, the deliverables, whether analysis or debugging is involved and how much already exists. You get a fixed quote in writing before you pay anything, with no extras added later.
Last reviewed 5 October 2026 by the SUSS Assignment Help analytics team.
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