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WeAreDevelopers LIVE β€’ Jun 1, 2023

Data Science in Retail

Julian Joseph

How do retail giants turn petabytes of raw data into highly targeted shopping experiences? Discover the machine learning models and MLOps pipelines powering modern e-commerce.

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#1 about 3 min

Introduction to data science applications in the retail sector

An overview of how data science enhances intuition and drives modern e-commerce experiences.

#2 about 3 min

Understanding real-world recommendation systems in common platforms

Evaluating how algorithms parse behavioral data to personalize consumer experiences on major platforms.

#3 about 4 min

Analyzing customer data sets for targeted audience segmentation

Grouping unlabeled tabular data enables businesses to optimize and customize their marketing campaigns.

#4 about 2 min

Performing exploratory data analysis to uncover underlying patterns

Visualizing information using scatter plots reveals narrative trends that govern proactive algorithm selection.

#5 about 4 min

Evaluating alternative clustering algorithms for retail data analysis

Assessing the practical complexity and specific constraints of hierarchical, distribution-based, and density-based clustering.

#6 about 4 min

Implementing K-means clustering for centroid-based data categorization

Initializing centroids and calculating Euclidean distances automates the grouping of disparate data points.

#7 about 2 min

Determining optimal cluster quantities using visualization diagnostic methods

Applying silhouette and elbow plot techniques effectively minimizes cluster inertia for accurate dataset grouping.

#8 about 5 min

Interpreting automated clustering results for actionable business insights

Translating complex model categorizations into practical marketing strategies improves audience profitability.

#9 about 3 min

Utilizing market basket analysis for product positioning strategies

Applying probability theorems to discover item correlations optimizes physical and digital cross-selling placements.

#10 about 1 min

Applying Naive Bayes algorithms for content filtering applications

Leveraging foundational statistical models automates spam identification and personalizes dynamic social media feeds.

#11 about 3 min

Predicting customer lifetime value using advanced linear regression

Identifying high-value demographic cohorts through variable correlation maximizes long-term brand retention.

#12 about 3 min

Leveraging BigQuery ML for scalable SQL-based segmentation experiments

Using enterprise data warehouses empowers analytics teams to train machine learning models directly via standard database queries.

#13 about 4 min

Scaling machine learning pipelines from prototypes to petabytes

Structuring robust data engineering workflows addresses the transition from subset analysis to large-scale production deployments.

#14 about 6 min

Navigating machine learning operations and maturity level frameworks

Automating data pipelines via continuous testing protocols streamlines high-volume scheduling management.

#15 about 4 min

Exploring specialized career paths within the data science ecosystem

Familiarity with specific ML stages opens opportunities for specialized engineering, advocacy, and product management roles.

#16 about 15 min

Addressing audience inquiries on analytical implementation and career growth

Practical insights clarify anomaly detection algorithms, diagnostic tests, security protocols, and essential team communication skills.

Matching moments

3:05 min

Applying predictive and generative models in ecommerce environments

2:20 min

Structuring agile data science teams for rapid deployment

Christoph Fassbach Christoph Fassbach +1 Β· WWC 2024

2:55 min

Determining the future of emotional and intelligent retail

Alejandro Saucedo Alejandro Saucedo +2 Β· WWC 2025

4:42 min

Predicting future retail experiences and avoiding technology hype

Alejandro Saucedo Alejandro Saucedo +2 Β· WWC 2025

3:16 min

Identifying business value through targeted data science

Lukas KΓΆlbl Β· LIVE

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt Β· LIVE

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