Shreshth V.
Before 365:
Business Analyst | Junglee Games
After 365:
Marketing Chief | TEDxNSUT
See all reviews
Online Course
Fashion Analytics with Tableau
with
Elitsa Kaloyanova
Leverage data analytics in fashion: unlock business value with data-driven decision-making
4,408 students already enrolled
Skill level:
Intermediate
Duration:
3 hours
-
Lessons (3 hours)
-
Practice exams (28 minutes)
- Lessons (3 hours)
- Practice exams (28 minutes)
CPE credits:
4.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited
certificate
What you learn
- Acquire domain-specific knowledge relevant to the dynamic fashion industry.
- Enhance your marketability with domain expertise for fashion analytics.
- Analyze consumer-driven marketing and online retail sales in fashion.
- Learn key analytics techniques tailored to the fashion industry domain.
- Improve your Tableau skills using complex real-world fashion-sector datasets.
Topics & tools
TheoryData AnalysisCareer DevelopmentFashionTableauDigital analyticsSupply Chain AnalyticsDemand ForecastingPrice OptimizationIndustry SpecializationData Visualization
Your instructor
Senior Data Scientist at 365 Data Science | Computational Biologist | Data Visualization Expert
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Bioinformatics
Course OVERVIEW
Description
CPE Credits: 4.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Fashion analytics with Tableau is all about the exciting world of fashion, as well as the vastly expanding sector of online retail. Data-driven decision-making is crucial in this competitive world, so companies need to make smart decisions and integrate advanced statistical solutions to gain an edge in the market. This course explores a variety of topics in data analytics for fashion, and features a practical use case, where you will analyze fashion retail data and build your own story report in Tableau.
Prerequisites
- Tableau Desktop or Tableau Public
Advanced preparation
Curriculum
41 lessons
3 exams
- 2. Consumer-Driven Marketing17 minThis chapter focuses on the importance of understanding your clients and their needs, as well as creating a seamless customer journey. Here we’ll discuss the main approaches to identifying consumer clusters. You will also gain a better understanding of the main predictors when it comes to consumer scoring, such as preferences, price point, or propensity to buy a product.17 minThis chapter focuses on the importance of understanding your clients and their needs, as well as creating a seamless customer journey. Here we’ll discuss the main approaches to identifying consumer clusters. You will also gain a better understanding of the main predictors when it comes to consumer scoring, such as preferences, price point, or propensity to buy a product.Marketing Fundamentals for Analytics FreeAnalytics Activation in Consumer-Driven Marketing FreeAn Overview of Cluster Analysis and Consumer Scoring Free
- 3. Consumer Analytics – Product Recommendation15 minProduct recommenders are one of the hottest topics in data science and a great way to deliver personalized content, with large-scale platforms such as Amazon and Netflix leading the way. In this section, we’ll discuss the main approaches to product recommendation and the challenges of integrating these solutions for fashion retailers.15 minProduct recommenders are one of the hottest topics in data science and a great way to deliver personalized content, with large-scale platforms such as Amazon and Netflix leading the way. In this section, we’ll discuss the main approaches to product recommendation and the challenges of integrating these solutions for fashion retailers.What Is a Product Recommendation Systems?Collaborative and Content-Based FilteringSimilarity Measures for Product Recommendation Engines
- 4. Digital and Web Analytics19 minThis chapter is all about what consumers experience when they’re on an online platform. You will get familiar with the main problems that can be solved with the help of digital analytics with solutions such as Clickstream analytics, attribution models, A/B testing, and then explore the connection between these solutions and data science.19 minThis chapter is all about what consumers experience when they’re on an online platform. You will get familiar with the main problems that can be solved with the help of digital analytics with solutions such as Clickstream analytics, attribution models, A/B testing, and then explore the connection between these solutions and data science.Introduction to Digital and Web AnalyticsExperience Analytics and Attribution ModelsClickstream Analytics and A/B TestingChallenges and Opportunities in Web AnalyticsPractice exam
- 5. Supply Chain Analytics13 minThis is a major topic that is becoming even more relevant with the rise of online players in the fashion industry. In this chapter, we provide an overview of advanced supply chain analytics methods, which can be used to forecast replenishment times, stockout conditions, and returns, to name just a few.13 minThis is a major topic that is becoming even more relevant with the rise of online players in the fashion industry. In this chapter, we provide an overview of advanced supply chain analytics methods, which can be used to forecast replenishment times, stockout conditions, and returns, to name just a few.Introduction to Supply Chain AnalyticsAdvanced Analytics in the Supply ChainApplications of Analytics in the Supply Chain
- 6. Integrated Demand Forecasting18 minIntegrated Demand Forecasting is crucial for optimizing the entire production and supply chain, which makes it one of the key topics in the course. In this chapter, we’ll introduce the complexity of solving problems in this area, as well as advanced statistical solutions that you can adopt when forecasting demand.18 minIntegrated Demand Forecasting is crucial for optimizing the entire production and supply chain, which makes it one of the key topics in the course. In this chapter, we’ll introduce the complexity of solving problems in this area, as well as advanced statistical solutions that you can adopt when forecasting demand.What Is Integrated Demand Forecasting?Integrated Demand Forecasting in the Fashion IndustryData Science in Demand ForecastingHow Do Industry Leaders Use Using Integrated Demand Forecasting ?
- 7. Pricing Optimization16 minIn this section, we’ll cover the important aspect of finding the optimal price for products. We also provide an overview of metrics like price elasticity and discuss machine learning methods, such as logistic regression and neural networks that help us predict the right price for items.16 minIn this section, we’ll cover the important aspect of finding the optimal price for products. We also provide an overview of metrics like price elasticity and discuss machine learning methods, such as logistic regression and neural networks that help us predict the right price for items.What Is Pricing Optimization? FreeAdvanced Analytics in Pricing Optimization FreeHow Does the Fashion Industry Leverage Optimization in Pricing? Free
- 8. Store Localization, Clustering, and In-store Optimization13 minThis chapter is all about finding the right spot to open a new store, implementing efficient space-planning techniques, and, ultimately, creating the best customer journey within a physical store. You will learn about the parameters that need to be optimized when opening a new store, illustrated by examples of how industry leaders are using advanced analytics for store localization and in-store optimization to improve margins.13 minThis chapter is all about finding the right spot to open a new store, implementing efficient space-planning techniques, and, ultimately, creating the best customer journey within a physical store. You will learn about the parameters that need to be optimized when opening a new store, illustrated by examples of how industry leaders are using advanced analytics for store localization and in-store optimization to improve margins.Introduction to Store Localization, Clustering, and In-store OptimizationAdvanced Analytics in Localization, Clustering, and In-store Optimization
- 9. AI for Predicting Fashion Trends11 minWe’ll take a look at some of the advancements coming from the world of computer vision and see how they’re employed in the fashion world today. You’ll see what a virtual wardrobe is, as well as an example of a neural network predicting fashion trends.11 minWe’ll take a look at some of the advancements coming from the world of computer vision and see how they’re employed in the fashion world today. You’ll see what a virtual wardrobe is, as well as an example of a neural network predicting fashion trends.Introduction to Artificial Intelligence in Fashion FreeImage Recognition and Market Intelligence in Fashion
- 10. Case Study – Building a Fashion Analytics Story in Tableau54 minThis is where you will have the chance to delve into a case study of a company online retailer and analyze data from its customer and a purchase database. You’ll build your own report in Tableau following the consumer journey, starting from Company KPIs, through Customer Journey, and ending with analyzing the Consumer Lifetime Cycle.54 minThis is where you will have the chance to delve into a case study of a company online retailer and analyze data from its customer and a purchase database. You’ll build your own report in Tableau following the consumer journey, starting from Company KPIs, through Customer Journey, and ending with analyzing the Consumer Lifetime Cycle.Introduction to the Case Study FreeGetting to Know the Dataset FreeCreating a Company KPIs Table in Tableau - Net Sales FreeCreating a Company KPIs Table in Tableau - Gross Profit MarginsCreating a Map Chart of Consumer Countries in TableauCreating a Customer KPIs Table in TableauCreating a Bar Chart of Consumer Metrics in TableauBuilding the Frequency Curve in TableauBuilding the Repurchase Curve in TableauCreating the Dashboards for the Fashion Analytics Story in TableauStyling and Formatting the Fashion Analytics ReportInterpretation of the Data Analytics Fashion StoryPractice exam
- 11. Course exam26 min26 minCourse exam
Free lessons
1.1 What does the Course Cover
5 min
1.2 What Is Fashion Analytics?
2 min
1.3 Realizing the Potential of Analytics in Fashion
6 min
1.4 The Breadth of Analytical Options
5 min
1.5 Requirements for Embedding Analytics in Fashion
6 min
2.1 Marketing Fundamentals for Analytics
5 min
#1 most reviewed
AI and data learning platform on Trustpilot.
94%
of AI and data science graduates
successfully change
or advance their careers.
$29,000
average salary increase
after moving to an AI and data science career
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-study learning plan.
How it WORKS
Student REVIEWS
