Professional Certificate in Data Science (Marketing)

Trainees who have successfully completed the program are eligible to use the post-nominal title PCiDS, subject to verification and upon issuance of the official certificate. Current and potential employers or educational institutions may contact Data Chord to verify the authenticity of the PCiDS certificate and the Accreditation certificate.

Our respective training centre partner provides the schedule. You may get in touch with them to find out more.

This foundational level training program establishes the core foundation for participants entering the program. It ensures learners from diverse professional backgrounds (marketing managers, analysts, entrepreneurs, or early-career professionals) acquire a shared baseline in data literacy, marketing frameworks, and statistical reasoning. By the end of this level, learners will be able to understand how data science intersects with marketing strategies, collect, clean, and manage different types of customer and market data (CRM, digital campaigns, surveys, social media), apply basic statistical thinking to marketing problems (A/B testing, consumer surveys, hypothesis validation), recognize and respond to ethical, privacy, and regulatory challenges when working with consumer data. This phase balances practical skills (data wrangling, descriptive stats) with strategic understanding (customer journey, compliance), setting the stage for deeper technical modeling in intermediate level. 

This module is about showing how marketing has shifted from relying on gut feelings to using data for smarter decisions. It explains how customer information, ads, and online activity help shape campaigns, and how marketers today must think differently from the past. It introduces the marketing funnel (from awareness to advocacy) and shows how data can track and improve each stage. Learners also discover the types of data used—structured, like purchase history, and unstructured, like social media comments—and why both are valuable. Real examples highlight successes and failures of intuition-driven vs. data-driven campaigns. By the end, participants understand how to connect data with customer journeys, recognize the risks of ignoring analytics, and see how creativity and data can work together for better marketing. 

Topics Covered:

• Role of data science in modern marketing decision-making
  • How marketing decisions have shifted from intuition-based to data-driven.
  • The role of customer data, campaign data, and real-time analytics in shaping strategy.
  • Use cases: campaign optimization, consumer insights, churn prediction, personalization.
  • The evolving role of the “data-driven marketer” vs. traditional marketer.
• Marketing funnel and customer journey models
  • Classical funnel stages: Awareness → Consideration → Conversion → Retention → Advocacy.
  • Modern interpretations: Omnichannel, non-linear journeys, micro-moments.
  • Linking KPIs to each stage (e.g., impressions, CTR, conversion rate, churn).
  • Customer journey mapping using data touchpoints (ads, CRM, social media, website).
• Structured vs. unstructured data in marketing
  • Structured data examples: demographics, purchase history, CRM logs, campaign results.
  • Unstructured data examples: social media posts, reviews, voice/video feedback, chat logs.
• Data-driven vs. intuition-driven campaigns: case comparisons (Self-Directed Learning)
  • Case 1: Traditional intuition-based campaign (creative-first, no data validation).
  • Case 2: Data-driven campaign (A/B tested creatives, targeted audiences).
  • Discussion: strengths and limitations of each approach.
  • Real-world case: Coca-Cola personalization campaign vs. a failed product launch guided only by gut feel.

Learning Outcomes:

• Conceptual Understanding:
  • Explain how data science supports decision-making at each funnel stage (e.g., predictive models for conversion, clustering for retention).
  • Articulate the difference between structured and unstructured marketing data, and why both matter.
• Applied Insight:
  • Recognize opportunities where analytics can replace guesswork in campaigns.
  • Map KPIs to customer journey stages and justify their use.
• Critical Thinking:
  • Evaluate the risks of over-relying on intuition in modern marketing.
  • Reflect on how data-driven strategies can be balanced with creativity.

This module teaches how to get marketing data ready for analysis by working with real-world sources like customer records, sales transactions, surveys, website clicks, and social media posts. It covers common problems such as missing values or duplicate entries and shows how to fix them without skewing insights. Participants also learn how to turn categories like age groups or ad channels into numbers that models can use, and how to clean up messy text data like reviews or hashtags by breaking it into tokens and tagging sentiment. Tools like Excel, Python, and R are introduced with practical demos, and learners practice combining different datasets to uncover customer insights. By the end, they gain the skills to clean, structure, and prepare data so that it can drive reliable marketing decisions. 

Topics Covered:

• Marketing data sources: CRM, POS, survey tools, clickstream, social media APIs
  • CRM data: customer records, lead pipelines, loyalty programs.
  • POS data: sales transactions, SKU-level product purchases.
  • Survey tools: consumer satisfaction surveys, brand perception questionnaires.
  • Clickstream: website navigation, ad impressions, time-on-page, bounce rates.
  • Social media APIs: extracting posts, hashtags, likes, shares, comments for analysis.
  • Case study: combining CRM and social media data to understand purchase intent.
• Handling missing data and duplicates
  • Common causes: incomplete surveys, logging errors, customer profile duplication.
  • Techniques: deletion, mean/median imputation, forward/backward fill.
  • Business impact: how missing values bias marketing analysis (e.g., under-reporting churn). (SDL)
  • Tools demo: handling NA values in Excel, Python (pandas), and R (tidyverse). (SDL)
• Encoding categorical variables (demographics, regions, channels)
  • Categorical data types in marketing: demographics (gender, age), geography (region, country), channels (online, in-store).
  • One-hot encoding vs. label encoding: when to use each.
  • Avoiding the “dummy variable trap.”
  • Example: converting “region” or “ad channel” into numerical inputs for modeling.
• Preprocessing text data (reviews, hashtags, comments)
  • Unstructured text sources: product reviews, survey open-ends, hashtags, user comments.
  • Steps: tokenization, stop-word removal, stemming/lemmatization.
  • Sentiment tagging as preprocessing (positive, neutral, negative).
  • Case example: preparing restaurant reviews for topic modeling. (Self-Directed Learning)
• Practical workflows in Python/R/Excel
  • Excel: data cleaning functions (TRIM, CLEAN, pivot tables).
  • Python: pandas for wrangling, regex for text preprocessing.
  • R: tidyverse for reshaping, dplyr for filtering/grouping.
  • Workshop: cleaning a mixed dataset (CRM + social comments) using these tools.

Learning Outcomes:

• Technical Skills:
  • Import and prepare marketing datasets from multiple sources.
  • Identify and resolve missing values and duplicates without distorting insights.
  • Encode categorical features into usable formats for statistical and ML models.
• Applied Insight:
  • Justify why different encoding or imputation techniques are appropriate in different scenarios.
  • Convert unstructured text (reviews, hashtags) into structured analyzable variables.
• Practical Competence:
  • Build a reproducible workflow for cleaning and preparing marketing data in Excel, Python, or R. 

This module introduces how marketers can use statistics to evaluate campaigns with more confidence. It covers the basics of tracking and summarizing campaign results, such as impressions, clicks, and conversions, and shows how to compare audience groups using tables, charts, and dashboards. Learners are introduced to confidence intervals and p-values, helping them understand the difference between results that are statistically significant and those that actually matter in practice. They also learn how to set up and interpret hypothesis tests and A/B experiments, including how to size samples and measure the real impact of changes like a new subject line or landing page design. Finally, the module explains why correlation doesn’t always mean causation, stressing the importance of controlled experiments in making reliable claims. By the end, learners gain the ability to summarize campaign performance, run simple statistical tests, and avoid common pitfalls when interpreting marketing data. 

Topics Covered:

• Descriptive statistics for campaign KPIs (frequency, cross-tabs)
  • Core KPIs: impressions, clicks, click-through rate (CTR), conversions, churn.
  • Frequency distributions and percentage breakdowns.
  • Cross-tabulation for comparing audience segments (e.g., region × age group).
  • Visualizing campaign results (tables, charts, dashboards).
  • Case example: comparing campaign performance across three digital channels. (Self-Directed Learning)
• Confidence intervals and p-values
  • Confidence intervals: interpretation and common pitfalls.
  • P-values: statistical vs. practical significance.
  • Example: measuring brand awareness uplift in pre/post surveys.
  • How confidence intervals are reported in marketing research reports.
• Hypothesis testing for campaign evaluation
  • Null vs. alternative hypotheses in marketing contexts.
  • Types of errors: Type I (false positive), Type II (false negative).
  • Example: testing whether a new email subject line improves open rates.
  • Choosing the right test: t-test, chi-square test, ANOVA (basic introduction).
• A/B testing: sample sizing and uplift measurement
  • Randomization and control groups in campaign design.
  • Determining sample size for reliable results.
  • Measuring uplift (percentage difference between variants).
  • Real-world case: optimizing landing page design with A/B testing. (Self-Directed Learning)
• Correlation vs. causation in marketing insights
  • Understanding correlation coefficients.
  • Spurious correlations in marketing data (e.g., ice cream sales vs. ad clicks).
  • Why controlled experiments matter for causal claims.
  • Case example: does increased ad spend cause higher sales, or just correlate?

Learning Outcomes:

• Technical Understanding:
  • Summarize campaign performance using descriptive measures (frequency tables, cross-tabs, charts).
  • Calculate and interpret confidence intervals and p-values in marketing contexts.
• Applied Competence:
  • Formulate and test hypotheses related to marketing campaigns (e.g., open rates, conversion rates).
  • Design and interpret A/B testing experiments with proper sample sizing.
• Critical Thinking:
  • Distinguish between correlation and causation in marketing insights.
  • Identify risks of misinterpretation when reporting campaign results.

This module focuses on how marketing teams can use customer data responsibly while staying compliant with global and local privacy laws. It introduces major regulations like GDPR (Europe), PDPA (Singapore), PDPD (Vietnam), and CCPA (California), explaining what they mean for collecting, storing, and using personal data. Learners explore how to run consent-based marketing campaigns with clear privacy notices, handle consumer rights such as data deletion and portability, and avoid risks of bias or unfair targeting in AI-driven marketing. The module also highlights ethical issues in personalization and chatbot use, showing how to balance effectiveness with customer trust. Real-world cases like Cambridge Analytica and fines against Google, Meta, and Singapore firms are used to illustrate consequences of poor practices. By the end, participants understand the rules, the risks, and how to design marketing strategies that are both effective and ethical. 

Topics Covered:

• Global & local data protection laws: GDPR, PDPA, PDPD, CCPA (SDL)
  • GDPR (EU): consent requirements, lawful basis for processing, data breach notification.
  • PDPA (Singapore): obligations on collection, use, disclosure, and protection of personal data.
  • PDPD (Vietnam): consent framework and cross-border data transfer restrictions.
  • CCPA (California): consumer opt-out rights, data sale disclosures.
  • PDPA (Malaysia): principles of notice and choice, limitations on processing, security safeguards, and restrictions on cross-border transfers unless adequate protection is ensured. 
  • Practical implications for multinational marketing teams.
• Consent-based marketing and transparency practices
  • Mechanisms: opt-in forms, cookie consent banners, preference centers.
  • Transparency requirements: clear privacy notices, purpose limitation.
  • Example: email marketing opt-in vs opt-out compliance in different regions.
• Consumer rights: right to be forgotten, data portability
  • Right to be forgotten: challenges for marketers holding historical campaign data.
  • Data portability: customer control of personal data and interoperability requirements.
  • Case examples of companies fined for mishandling consumer rights requests.
• Bias, fairness & inclusivity in marketing AI
  • Risks of biased targeting (e.g., excluding groups from loan ads).
  • Algorithmic fairness in segmentation and personalization.
  • Ensuring inclusivity in customer engagement (e.g., language, accessibility).
• Responsible personalization & chatbot use
  • Balancing personalization with privacy expectations.
  • Risks of over-targeting or perceived “creepiness.”
  • Chatbot transparency: disclosing AI interactions to customers.
• Case studies: Cambridge Analytica, EU fines, Singapore PDPA enforcement
  • Cambridge Analytica: misuse of Facebook data for political targeting.
  • EU GDPR fines: Google & Meta cases on consent violations.
  • Singapore PDPA enforcement: examples of SMEs penalized for weak data safeguards.
  • Learner analysis of implications for marketers.

Learning Outcomes:

• Regulatory Knowledge:
  • Identify and explain key provisions of GDPR, PDPA, PDPD, and CCPA.
  • Compare regulatory obligations across jurisdictions.
• Practical Application:
  • Implement best practices in consent-based marketing and transparency.
  • Design compliant processes for data collection and usage in campaigns.
• Critical Evaluation:
  • Recognize and mitigate ethical risks in targeting, personalization, and chatbot use.
  • Evaluate real-world data misuse cases and suggest corrective measures.

The Intermediate Level focuses on analytical depth: learners progress from foundational literacy into segmentation, predictive modeling, and recommendation systems. This phase bridges the gap between raw data preparation and advanced AI-driven applications.

This module introduces how businesses can group customers into meaningful segments to improve targeting and personalization. It covers clustering techniques like K-means (useful for larger datasets) and hierarchical clustering (better for smaller, complex patterns), showing how to decide the right number of groups and how to interpret results. Learners practice turning raw clusters into buyer personas by combining demographics (e.g., age, region, income) with behaviors (e.g., frequency of purchases, spending levels). The module also emphasizes visualization tools such as scatterplots, heatmaps, and radar charts to make segmentation insights clear and actionable for marketers and executives. Through hands-on examples in retail and e-commerce, participants learn how to design strategies like product bundling, personalized promotions, and targeted campaigns. By the end, they can apply clustering techniques, build customer personas, and communicate results effectively to support smarter marketing decisions.

Topics Covered:

• Effective Targeting using the Noon Nopi technique
  • Placing products and messages where they are most visible to the target audience.
  • Product placement on shelves, store layout, and in-store promotions to match customer line-of-sight.
  • Ensuring ads, banners, and CTAs are positioned where users are most likely to notice and engage.
  • Powerful for attention capture, but effectiveness can diminish with ad clutter or poor audience alignment.
  • Example: positioning premium products at eye-level shelves in supermarkets or placing promotional CTAs at key scroll depth points on e-commerce sites.
• K-means clustering for customer segmentation
  • Concept of centroid-based clustering.
  • Selecting the number of clusters (elbow method, silhouette score).
  • Strengths & limitations of K-means in marketing contexts.
  • Example: segmenting e-commerce customers by purchase behavior.
• Hierarchical clustering and dendrogram interpretation (Self-Directed Learning)
  • Agglomerative vs. divisive clustering.
  • Reading dendrograms to identify natural clusters.
  • When to use hierarchical over K-means (e.g., smaller datasets with complex patterns).
• Profiling buyer personas using demographics & behaviors
  • Linking clusters to real-world personas (age, region, income, channel preference).
  • Behavioral variables: recency, frequency, monetary (RFM) modeling.
  • Translating technical clusters into marketer-friendly personas.
• Visualizing clusters for decision-making
  • 2D/3D scatterplots, PCA for dimensionality reduction.
  • Cluster heatmaps and radar charts for persona profiling.
  • Communicating cluster insights visually to non-technical stakeholders.
• Case study: customer segmentation for retail/e-commerce
  • Hands-on analysis of a retail dataset.
  • Creating buyer personas from identified clusters.
  • Strategic recommendations: product bundling, targeted campaigns, personalized promotions.

Learning Outcomes:

• Technical Skills
  • Apply K-means and hierarchical clustering to marketing datasets.
  • Select appropriate segmentation techniques based on dataset and business context.
• Applied Competence
  • Translate cluster outputs into meaningful buyer personas (demographics + behaviors).
  • Create compelling cluster visualizations for decision-making.
• Strategic Insight
  • Recommend marketing strategies (targeting, personalization, product design) based on clusters.
  • Communicate segmentation results effectively to both technical and executive audiences.

This module shows how predictive models can help marketers anticipate customer behavior and make smarter decisions. Learners explore logistic regression for predicting whether someone will convert (e.g., buy a product), and decision trees/random forests for understanding and reducing customer churn. They learn how to identify which factors—such as demographics, engagement, or sales channels—most influence customer actions, and how to use feature importance to guide strategy. The module also introduces time-series forecasting, helping teams predict sales and campaign performance by recognizing patterns like seasonality and trends. Through a case study on subscription businesses, participants practice building churn models and turning outputs into strategies such as personalized offers or reactivation campaigns. By the end, learners gain practical experience in building predictive models, interpreting results, and presenting insights in ways that directly support marketing and executive decision-making. 

Topics Covered:

• Logistic regression for conversion prediction
  • Binary classification: conversion (yes/no).
  • Odds ratios and probability interpretation in marketing contexts.
  • Example: predicting whether a website visitor will purchase after viewing a product page.
• Decision trees and random forests for churn modeling
  • Decision tree basics: splitting criteria (Gini, entropy).
  • Overfitting vs pruning.
  • Random forests: ensemble approach for more stable predictions.
  • Example: predicting churn for a subscription streaming service.
• Feature selection and importance in marketing data
  • Identifying key drivers of customer behavior (demographics, channel, engagement).
  • Feature importance in tree-based models.
  • Dimensionality reduction techniques (intro to PCA).
  • Business interpretation: ranking the factors influencing churn.
• Time-series forecasting for campaign/sales data
  • Concepts: trend, seasonality, noise.
  • Simple methods: moving average, exponential smoothing.
  • ARIMA models for campaign and sales prediction.
  • Case example: forecasting sales impact of holiday campaigns.
• Case study: predicting customer churn in subscription businesses (Self-Directed Learning)
  • Dataset: customer subscription data (usage, demographics, engagement).
  • Steps: preprocessing, model selection, evaluation.
  • Strategic recommendations: retention offers, reactivation campaigns.

Learning Outcomes:

• Technical Skills
  • Build and evaluate logistic regression models for conversion prediction.
  • Apply decision trees and random forests to churn analysis.
  • Conduct time-series forecasting for sales and campaign data.
• Applied Competence
  • Interpret feature importance to identify key drivers of churn or conversion.
  • Translate model outputs into actionable marketing insights.
• Strategic Insight
  • Recommend business strategies (e.g., targeted retention campaigns) based on predictive models.
  • Communicate findings in a way that supports executive decision-making.

This module explains how companies like Netflix, Amazon, Shopee, and Lazada use recommendation systems to personalize customer experiences. It introduces collaborative filtering, which suggests items based on user or item similarities (“people who bought this also bought…”), and matrix factorization techniques like SVD, which reveal hidden patterns in customer preferences. Learners also compare content-based methods (using product attributes like genres or keywords) with hybrid systems that combine multiple approaches for better results. The module highlights how to measure effectiveness with metrics such as precision, recall, and MAP, and why balancing accuracy with diversity and novelty matters. Real-world case studies show how big platforms design their recommenders, while exercises allow learners to critique and improve these systems. By the end, participants understand how recommendation engines work, how to evaluate them, and how to apply similar strategies in retail and e-commerce. 

Topics Covered:

• Collaborative filtering: user-based & item-based approaches
  • Principles of “users who bought this also bought…” and “items similar to this.”
  • User-user similarity vs. item-item similarity.
  • Pros & cons: sparsity problems, cold-start issue.
  • Example: recommending movies on Netflix based on viewing history.
• Matrix factorization for personalization
  • Concept of latent factors (user taste dimensions, item attributes).
  • Singular Value Decomposition (SVD) for reducing dimensionality.
  • Example: identifying hidden preferences in e-commerce purchase data.
• Content-based recommendation vs hybrid systems
  • Content-based: recommending based on product attributes (e.g., genre, keywords).
  • Hybrid: combining collaborative + content-based to improve robustness.
  • Real-world usage: Spotify music recommendations, Amazon product bundling.
• Evaluating recommender performance (precision, recall, MAP)
  • Offline evaluation metrics: precision, recall, F1-score, Mean Average Precision (MAP).
  • Online evaluation: A/B testing recommendation systems.
  • Balancing accuracy vs diversity vs novelty.
• Case study: recommendation engines (Amazon, Netflix, Shopee, Lazada) (Self-Directed Learning)
  • Amazon: “frequently bought together” vs “recommended for you.”
  • Netflix: personalized movie ranking.
  • Shopee/Lazada: e-commerce recommendations for cross-selling and up-selling.
  • Learner exercise: analyze one platform’s recommender strategy and critique strengths/weaknesses.

Learning Outcomes:

• Technical Skills
  • Design and implement a basic collaborative filtering recommender (user-based or item-based).
  • Apply matrix factorization to uncover hidden patterns in user-item interactions.
• Applied Competence
  • Compare collaborative, content-based, and hybrid approaches, and justify when each is suitable.
  • Evaluate recommendation effectiveness using standard metrics (precision, recall, MAP).
• Strategic Insight
  • Critically analyze real-world recommendation engines (Amazon, Netflix, Shopee, Lazada).
  • Suggest improvements to a company’s recommendation strategy.

The Advanced Level integrates AI and machine learning applications into marketing practice. Learners will explore NLP for consumer insights, generative AI for marketing content, and responsible AI adoption. By the end, participants will be able to design, evaluate, and critically assess AI-driven marketing strategies. 

This module explores how advanced AI tools are transforming marketing through both creative and predictive applications. Learners see how generative AI (like large language models) can produce ad copy, social posts, visuals, and even customer support scripts, with real-world examples such as Coca-Cola’s “Create Real Magic” campaign. They also practice prompt engineering—designing instructions that guide AI to generate tailored content for different audiences. On the strategic side, the module covers how AI can optimize marketing budgets and channel mix in real time using reinforcement learning. Case studies from global ad agencies and retailers show how AI is being adopted for personalization and efficiency, while raising questions about creativity, ethics, and trust. By the end, participants gain practical skills in using AI for content creation and campaign optimization, while also learning to weigh its benefits, risks, and the balance between human creativity and machine assistance. 

Topics Covered:

• Generative AI in marketing campaigns (LLMs, creative content, copywriting)
  • Role of large language models (LLMs) in campaign ideation.
  • Automating creative content: email subject lines, social media posts, blog intros.
  • AI-generated visuals and video: opportunities and risks.
  • Example: Coca-Cola’s global “Create Real Magic” campaign using generative AI. (Self-Directed Learning)
• Prompt engineering for ad copy, personalization, and customer support
  • Principles of effective prompting: role, context, style, constraints.
  • Personalization prompts for customer segments (e.g., “Write ad copy for Gen Z gamers”).
  • Automating customer support chat flows with AI prompts.
  • Workshop: iterative refinement of prompts for marketing use cases.
• AI-driven optimization of marketing mix & spend allocation
  • AI tools for media planning and spend optimization across channels (search, social, display, email).
  • Reinforcement learning for dynamic budget allocation.
  • Case example: reallocating budget in real-time based on campaign performance.
• Case studies: generative AI use in global ad agencies, retail personalization
  • Global ad agencies: Ogilvy, WPP adoption of generative tools for efficiency.
  • Retail personalization: Amazon & Sephora using AI for customized recommendations and copy.
  • Ethical debate: AI replacing human creativity vs augmenting creative teams.
  • Learner group discussion on “Where to draw the line in AI creativity.”

Learning Outcomes:

• Technical Skills
  • Apply prompt engineering techniques to generate ad copy, personalized content, and customer support scripts.
  • Use AI-based tools for campaign spend allocation and optimization.
• Applied Competence
  • Critically compare human vs AI-generated content for tone, quality, and brand alignment.
  • Evaluate the ROI of AI-driven marketing interventions.
• Strategic Insight
  • Assess when generative AI enhances creativity vs when it risks brand dilution or customer mistrust.
  • Provide recommendations for responsible AI integration in marketing campaigns.

This module shows how natural language processing (NLP) can be used to better understand consumers through their words. Learners explore sentiment analysis, which measures whether reviews or social media posts are positive, negative, or neutral, and topic modeling, which uncovers hidden themes in large volumes of feedback to spot emerging trends. They also learn named entity recognition (NER) to track mentions of brands, products, or competitors, and see how social listening platforms like Brandwatch or Talkwalker help monitor customer opinions in real time. A case study highlights how negative online reactions can force a brand to quickly adjust its campaign, with lessons drawn from real-world examples like Pepsi’s 2017 backlash. By the end, participants can apply NLP techniques to extract insights from text, track brand reputation, and recommend data-driven adjustments that make marketing more responsive to consumer voices. 

Topics Covered:

• Sentiment analysis on customer reviews & social media
  • Lexicon-based vs. machine learning approaches.
  • Preprocessing text: tokenization, stopword removal, stemming/lemmatization.
  • Sentiment scoring (positive, neutral, negative).
  • Example: analyzing Amazon product reviews to identify satisfaction drivers.
• Topic modeling for market research (LDA, NMF)
  • Latent Dirichlet Allocation (LDA): uncovering hidden themes.
  • Non-Negative Matrix Factorization (NMF) for topic extraction.
  • Case example: discovering emerging consumer trends in restaurant reviews.
• Named entity recognition for brand & competitor tracking
  • Identifying brand mentions, product names, locations, competitors in text.
  • Using spaCy/HuggingFace for entity extraction.
  • Example: monitoring competitor mentions in X discussions.
• Social listening platforms and real-time consumer monitoring
  • Tools overview: Brandwatch, Talkwalker, Sprout Social.
  • Setting up alerts and dashboards for brand reputation tracking.
  • Integrating social listening into campaign monitoring workflows.
• Case study: sentiment-driven campaign adjustment (Self-Directed Learning)
  • Scenario: brand launches a new product → initial negative feedback on social media.
  • Task: analyze sentiment & topics, propose adjustments to messaging.
  • Real-world parallel: Pepsi’s 2017 campaign backlash and brand recovery strategies.

Learning Outcomes:

• Technical Skills
  • Conduct sentiment analysis using Python NLP libraries (NLTK, spaCy, HuggingFace).
  • Apply topic modeling methods (LDA, NMF) to uncover themes in consumer feedback.
• Applied Competence
  • Perform brand/competitor tracking with NER.
  • Use social listening tools to monitor real-time customer sentiment.
• Strategic Insight
  • Translate NLP outputs into actionable marketing recommendations.
  • Recommend campaign adjustments based on consumer sentiment analysis.

This module addresses the ethical and governance challenges of using AI in marketing. It explains the risks of “black box” systems, where decisions like targeting or exclusions are not transparent, and introduces tools such as SHAP and LIME that help marketers explain AI outcomes to stakeholders. Learners explore issues of fairness and inclusivity, such as avoiding discrimination in ads or biased personalization, and study how global regulations—including the EU AI Act, US FTC guidelines, and Singapore’s digital trust frameworks—set standards for responsible AI use. The module also guides participants in designing AI governance frameworks, including ethics boards, compliance checks, and internal policies to ensure accountability and fairness. Real-world cases, such as Cambridge Analytica and biased ad targeting, illustrate the consequences of misuse. By the end, participants can identify ethical risks, ensure compliance across regions, and advise organizations on building transparent and responsible AI-driven marketing practices. 

Topics Covered:

• Algorithmic transparency and explainability in marketing
  • What “black box AI” means in marketing systems (e.g., opaque recommendation engines).
  • Explainable AI (XAI) tools for marketers: SHAP, LIME.
  • Communicating algorithmic decisions to non-technical stakeholders.
  • Example: explaining why a customer was excluded from a loyalty campaign. (Self-Directed Learning)
• Fairness and inclusivity in personalization & targeting
  • Risks of discriminatory targeting (e.g., excluding minorities, gender bias in job ads).
  • Ensuring inclusivity in segmentation (representation in training data).
  • Ethical personalization boundaries: balancing relevance vs. over-surveillance.
• Regulatory considerations: AI Act (EU), FTC guidelines (US), MAS/Singapore digital trust frameworks (Self-Directed Learning)
  • AI Act (EU): risk-based approach, restrictions on manipulative AI.
  • FTC Guidelines (US): truth-in-advertising, AI transparency rules.
  • MAS/Singapore Digital Trust Frameworks: governance in AI adoption for financial & consumer applications.
  • Implications for global companies running cross-border campaigns.
• Designing governance frameworks for AI marketing adoption
  • Elements of AI governance: accountability, transparency, fairness, safety.
  • Role of ethics boards, compliance teams, and audit trails.
  • Building an internal “Responsible AI in Marketing” policy.
  • Example governance checklist for a marketing department.
• Case study: regulatory breaches and AI misuse in marketing
  • Case: Facebook/Cambridge Analytica → misuse of psychographic targeting.
  • Case: AI-driven housing ad targeting that excluded groups (Meta fined).
  • Learner discussion: where the line between personalization and manipulation should be drawn.

Learning Outcomes:

• Ethical Awareness
  • Identify risks in AI-driven marketing (bias, manipulation, deepfakes, exclusion).
  • Recognize ethical dilemmas in personalization, targeting, and campaign automation.
• Applied Competence
  • Propose governance frameworks that align with regulatory expectations.
  • Design policies and internal guidelines for responsible AI use.
• Strategic Insight
  • Evaluate compliance requirements across multiple jurisdictions (EU, US, Singapore).
  • Advise leadership on ethical adoption of AI in marketing campaigns.

This is the final assessment for the qualification round of the Professional Certificate in Data Science (Marketing). All final projects are reviewed by an Expert Assessment Panel comprising seasoned professionals with over 15 years of experience in data science.

While there are no strict entry requirements, 

• a background in IT or data science would be beneficial, and
• a display of certain level of English proficiency, and
• at least 21 years old

We welcome learners from all backgrounds to join us in this training program.

Please click on this link to know more about our PCiDS™ Qualifying and Certification Rounds.

At this moment, we are preparing for the next level of training: the Professional Advanced Certificate in Data Science.