RA: Retail Management, Analytics with Excel & Python.

RETAIL ANALYTICS · RETAIL MANAGEMENT · PRICING ANALYTICS · DEEP LEARNING · ANN · RNN · PRODUCT PLACEMENT · PYTHON · SALES FORECASTING · ELASTICITY · MERCHANDISING

★ Udemy Premium Course — 4.5 Stars, 10,900+ Students, Part of the RA Retail Series

This course carries Udemy’s Premium badge and a sustained 4.5-star rating from 10,900+ enrolled students — retail managers, buyers, data analysts, and demand planners who enrolled and stayed for the full 18.5 hours. It is Episode 1 of the three-part RA Retail Series: the foundational program that builds the retail domain knowledge and Python skills needed for everything that follows in EP2 (Retail Planning & Assortment Analytics) and EP3 (Customer Analytics & Trade Area Modelling).

★ Deep Learning for Retail Sales Forecasting — ANN and RNN with Keras and TensorFlow

This is the only retail management course on Udemy that teaches deep learning forecasting. Sections 11 and 12 go well beyond classical regression: you will build Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) in Keras and TensorFlow to forecast retail sales at subfamily and SKU level. You will compare ANN accuracy against classical methods, develop a generalised forecasting function, and scale it to forecast all product subfamilies simultaneously. Then Section 12 applies middle-out hierarchical aggregation to reconcile SKU-level forecasts across the product hierarchy.

★ Pricing Analytics Taught with Real Economic Models — Elasticity, Logit, and Markdown Optimisation

Most retail courses mention pricing as a concept. This course teaches it as a discipline. Sections 3–5 build the full pricing analytics toolkit: linear and logistic price response functions, elasticity calculation, the logit price model, polynomial response variants, willingness-to-pay analysis, point of maximum profit simulation, and multi-period markdown optimisation with Excel Solver. These are the quantitative tools that pricing analysts and revenue managers use to set and defend prices with data.

★ Built by a Retail Industry Consultant — Co-Founder of Keip, a SaaS Platform for Retail Management

Haytham is an active retail and supply chain consultant who works with retailers including Sephora France and Sharaf Group Dubai, and is co-founder of Keip — a SaaS platform built specifically for retail management and analytics. Every retail framework, pricing model, product placement strategy, and forecasting technique in this course has been applied in live client engagements. No textbook retail theory — practitioner knowledge from day one.

★ Episode 1 of 3 — The RA Retail Series: The Most Comprehensive Retail Analytics Program on Udemy

This course is Episode 1 of the three-part RA Retail Series — a complete retail analytics curriculum that takes you from retail fundamentals through to advanced planning, customer analytics, and trade area modelling. EP1 (this course) builds the foundation: retail management, pricing, deep learning forecasting, and product placement. EP2 (Retail Planning & Assortment Analytics — Highest Rated) adds budgeting, OTB, assortment optimisation, and AutoML. EP3 (Customer Analytics & Trade Area Modelling) adds churn prediction, customer segmentation, and geographic trade modelling. Each episode stands alone. Together they form a complete retail data science program.

Retail is one of the most data-rich industries in the world — and one of the least analytically equipped. This course changes that. It is Episode 1 of the RA Retail Series: an 18.5-hour program that takes you from what a retailer is, through pricing science, visual merchandising, product placement optimisation, and Python data manipulation, all the way to building Artificial Neural Network and Recurrent Neural Network models in Keras and TensorFlow to forecast retail sales at product and SKU level.

The course is structured in four phases. The first builds retail domain expertise: types of retail, merchandise mix, category roles, and the strategic decisions that separate successful retailers from struggling ones — with the Amazon vs Barnes & Noble case as the anchor. The second applies pricing as a quantitative discipline: price response functions, elasticity, logit models, markdown optimisation with Excel Solver. The third builds Python from scratch and applies it to retail data manipulation, product placement strategy with Inventorize, and regression-based sales forecasting. The fourth goes further than any other retail course: ANN and RNN deep learning forecasting in Keras and TensorFlow, with middle-out hierarchical aggregation to reconcile forecasts from SKU level to subfamily.

No Python experience is needed — a complete crash course is included. No retail background is needed — the course starts from first principles. This is Episode 1 of 3 in the RA Retail Series. EP2 continues with retail planning, budgeting, OTB, and assortment optimisation (Highest Rated on Udemy, 15,400+ students). EP3 adds customer analytics, churn prediction, and trade area modelling. Each stands alone. Together they are the most complete retail analytics program available on any online learning platform.

THE RA RETAIL SERIES — EPISODE 1 OF 3

This course is the entry point to a three-part retail analytics program. Each episode can be taken independently. Together they cover the complete retail analytics stack — from fundamentals to deep learning forecasting, from OTB budgeting to customer segmentation, from product placement to trade area modelling.

EP2 — RETAIL PLANNING & ASSORTMENT ANALYTICS

★ Highest Rated

RA: Retail Planning, Assortment Analytics with Excel & Python

Retail metrics, OTB planning, assortment optimisation & ML forecasting in Excel and Python. 15,400+ students · 19.5 hrs · 208 lectures · Highest Rated on Udemy

EP3 — CUSTOMER ANALYTICS & TRADE AREA MODELLING

★ Premium

RA: Retail Customer Analytics and Trade Area Modeling

Customer analytics, churn prediction, customer segmentation, and trade area modelling in Python. 14,100+ students · 15.5 hrs · 4.4 stars

WHAT MAKES THIS COURSE DIFFERENT

[ DEEP ]

ANN + RNN forecasting — unique in retail

The only retail course on Udemy with deep learning forecasting. Build ANN and RNN models in Keras and TensorFlow, compare against classical methods, and scale across all product subfamilies.

[ PRICE ]

Pricing as a science, not a concept

Linear, logistic, and logit price response functions; elasticity; markdown optimisation with Solver. The full analytical pricing toolkit — in Excel and Python.

[ A-Z ]

Retail fundamentals to ML forecasting

13 sections that take an absolute beginner from what a retailer is, through to product placement strategy, hierarchical forecasting, and deep learning models — all in one program.

TOOLS COVERED IN THIS COURSE

Microsoft Excel | Python | Keras & TensorFlow | Excel Solver | Inventorize | Jupyter / Anaconda

WHAT YOU WILL LEARN

✓ Understand the retail landscape: types of retail, verticals, food vs near-food, general merchandise, online vs physical, and multi-channel strategy

✓ Apply merchandise mix principles: SKU types, breadth vs width, deep vs shallow assortment, retailer brand, and category role and strategy

✓ Understand cost-based and value-based pricing, visual merchandising, and the strategic importance of price positioning

✓ Build linear and logistic price response functions, calculate price elasticity, model the logit price function, and simulate polynomial price variants

✓ Calculate willingness to pay, identify the point of maximum profit, and optimise multi-period markdowns with Excel Solver

✓ Write Python from scratch for retail analytics: data structures, pandas, loops, functions, and the Inventorize package — complete crash course included

✓ Master advanced pandas data manipulation on real retail data: pivot tables, group-by, joins, filtering, imputation, and datetime handling

✓ Apply Pareto/ABC analysis and product placement strategy using Python and Inventorize on a real apparel retailer dataset

✓ Build regression-based retail sales forecasting models in Python: trend and seasonality, subfamilies, spreading, one-hot encoding, and accuracy measurement

✓ Build ANN (Artificial Neural Network) and RNN (Recurrent Neural Network) models in Keras and TensorFlow for retail sales forecasting

✓ Compare deep learning forecast accuracy against classical regression methods and develop a generalised forecasting function for all product subfamilies

✓ Apply middle-out hierarchical aggregation: forecast at SKU level, get proportions, and reconcile upward to subfamily level

COURSE CONTENT — 13 SECTIONS · 194 LECTURES · 18.5 HOURS · 55 DOWNLOADABLE RESOURCES

PHASE 1 — RETAIL DOMAIN KNOWLEDGE

SECTION 1:The retail environment and retail formats

What is retail, and why does it matter analytically? Understand the full retail landscape: types of retail (food, near-food, general merchandise, specialty), verticals, the value chain position of retailers, and the multi-channel environment. Study the Amazon vs Barnes & Noble case — one of the most instructive retail strategy stories in modern business — and examine whether online will replace physical retail. Graded quiz.

Concepts

SECTION 2: Merchandise mix and category strategy

Every retail decision starts with the right product range. Understand SKU types, breadth vs width of assortment, deep vs shallow assortment trade-offs, retailer brand strategy, category roles, and category strategy frameworks. These are the conceptual foundations that underpin every practical model in the sections that follow. Graded quiz.

Concepts

PHASE 2 — PRICING ANALYTICS & MARKDOWN OPTIMISATION

SECTION 3: Pricing fundamentals and visual merchandising

Understand the two dominant pricing philosophies — cost-based and value-based — and when each applies. Learn visual merchandising principles and the strategic importance of price positioning in driving customer behaviour and category performance.

Concepts Excel

SECTION 4: Price response functions, elasticity, and revenue maximisation

Pricing as a science. Build linear and logistic price response functions from first principles. Estimate the logit price function. Calculate price elasticity for linear and logit models. Explore polynomial response function variants. Model willingness to pay, simulate price scenarios, and identify the point of maximum profit for any product. Includes a graded assignment.

Excel Python

SECTION 5: Markdown optimisation with Excel Solver

Markdowns are one of the most consequential retail decisions — and one of the most commonly mismanaged. Understand why markdowns happen and how customer segments respond differently to them. Formulate single-period and multi-period markdown problems, set up and solve with Excel Solver, account for salvage value, integrate forecasting into markdown decisions, and run sensitivity analysis on your solutions. Graded assignment.

Excel Solver

PHASE 3 — PYTHON FOR RETAIL ANALYTICS

SECTION 6: Python crash course for retail professionals

No coding experience? No problem. Install Anaconda, explore Jupyter Notebook and Spyder, and build Python foundations from scratch with a retail analytics mindset: dataframes, arithmetic, lists, dictionaries, arrays, data import, subsetting, conditions, functions, mapping, for loops, and the Inventorize package. Two-part graded assignment.

Python Anaconda Inventorize

SECTION 7: Advanced data manipulation in Python for retail

Build the pandas toolkit that every retail data analyst needs. Apply to real retail data: dropping duplicates and nulls, conversions, filtering, imputation, indexing, slicing, group-by, pivot tables with aggregate functions, melting, and all join types. Five-part graded assignment — the most comprehensive data manipulation assessment in the course.

Python

SECTION 8: Working with dates and time in Python

Retail data is always time-indexed. Master datetime objects in Python: parse dates, calculate recency, model customer inter-arrival times, resample time series to weekly and monthly frequencies, and apply rolling window calculations. Graded assignment.

Python

SECTION 9: Product placement strategy with Python and Inventorize

Where a product sits in a store drives how much of it you sell. Apply Pareto law and ABC analysis to a real apparel retailer dataset. Explore sales per section, build revenue and profit columns, identify volume drivers, group items by category, and use Inventorize to strategise category placement. Build and optimise a product placement model that maximises revenue across retail space. Graded assignment.

Python Inventorize Excel

PHASE 4 — SALES FORECASTING: REGRESSION TO DEEP LEARNING

SECTION 10: Retail sales forecasting with regression in Python

The foundation of all retail planning. Understand the forecasting process, approaches, and the role of trend and seasonality. Prepare data, fit regression models, forecast next year’s demand by subfamily, apply spreading across SKUs, use one-hot encoding for category variables, measure accuracy, develop a generalised forecasting function, and scale it across all subfamilies. Graded assignment.

Python Excel

SECTION 11: Deep learning forecasting: ANN and RNN with Keras and TensorFlow

The most technically advanced section of the course — and the one no other retail course offers. Install Keras and TensorFlow. Understand how neural networks work: forward and backward propagation, activation functions, slope calculations. Build a sequential ANN model, train and test it on retail sales data, then build a Recurrent Neural Network (RNN) to capture temporal dependencies in sales patterns. Compare ANN accuracy against classical regression, plot the results, develop a generalised forecasting function, and forecast all product subfamilies simultaneously. Graded deep learning assignment.

Python Keras TensorFlow

SECTION 12: Middle-out hierarchical forecasting

Reconcile forecasts across the product hierarchy. Build SKU-level sales data, calculate weekly subfamily sales, derive proportions for disaggregation, apply middle-out forecasting logic, join all components, and forecast at the SKU level from the subfamily forecasts. Graded two-part assignment.

Python

SECTION 13: Wrap-up and preview of EP2

A closing section summarising the course and previewing Episode 2 of the RA Retail Series — Retail Planning, Assortment Analytics with Excel & Python — which builds on this foundation with retail budgeting, OTB planning, assortment optimisation, competing product pricing, and AutoML forecasting with PyCaret.

Discussion

THIS COURSE IS NOT FOR YOU IF...

✗ You are looking for a retail operations or store management course — this course focuses on retail analytics, pricing science, and data-driven forecasting; store operations is a separate topic

✗ You want an advanced machine learning course only — this course builds from retail fundamentals through to deep learning; students wanting ML only may prefer to start at Section 10

✗ You need a retail ERP or POS system tutorial — this course builds analytical models in Excel and Python, not software configuration skills

✗ You want EP2 or EP3 content specifically — OTB budgeting and assortment optimisation are in EP2 (Retail Planning & Assortment Analytics); customer segmentation and trade area modelling are in EP3

WHAT STUDENTS SAY

“This is the best course for a person who wants a career as a data analyst in the retail domain. All the concepts are neatly explained with a good number of examples.”

Rajesh — Verified Udemy student

“I come from a corporate buying background and have worked in the retail industry for 10+ years so these concepts aren’t new to me. But this is a great refresher and succinct summary to refocus on the foundational retail business concepts before starting my new role in merchandise planning.”

Sarah — Verified Udemy student — 10+ years retail buying

“Haytham mentored me in my role of Head of Supply Chain Efficiency. He is extremely knowledgeable about supply chain concepts, latest trends, and benchmarks. His analytics-driven approach was very helpful to recommend and implement significant changes to our supply chain.”

Saify Naqvi — Head of Supply Chain Efficiency, Aster Group

WHO THIS COURSE IS FOR

Retail managers and buyers

You manage categories, products, or stores and want a data-driven foundation — pricing analytics, product placement models, and Python forecasting — to make decisions that are faster, more accurate, and easier to defend to leadership.

Retail analysts and demand planners

You produce sales forecasts and category reports manually and want to automate them in Python — including deep learning models that outperform classical regression on seasonal and trend-driven product lines.

Pricing and revenue analysts

You set or recommend retail prices and want the quantitative toolkit — price response functions, elasticity, logit models, markdown optimisation — to connect pricing decisions to revenue and margin outcomes.

Data analysts entering retail

You know Python or data analytics and want a structured program that applies those skills to real retail problems: product placement optimisation, pricing elasticity, ANN/RNN forecasting, and hierarchical aggregation.

Students and career changers into retail

You are building a career in retail and want a complete portfolio of working models — pricing simulators, product placement tools, deep learning forecasting scripts — to stand out in retail analytics and buying job applications.

Excel users moving into Python

You rely on spreadsheets for retail analysis and want to automate your work in Python — with a full crash course included and every concept applied to real retail data from the first section.

REQUIREMENTS

● Motivation to learn — this is the only formal requirement listed on the course page, and intentionally so. No retail background, no Python, no statistics experience assumed.

● No Python experience required — Section 6 is a complete Python crash course built for retail professionals, covering everything from installation to for loops.

● Basic familiarity with Excel is helpful for Sections 3–5 (pricing and markdown) — all Excel models are built step by step from scratch.

● A computer capable of running Anaconda and Jupyter Notebook (both free) — setup is fully guided in Section 6.

● Keras and TensorFlow (both free) are installed in Section 11 with a guided setup walkthrough before any code is written.

WHAT IS INCLUDED

● 13 sections, 194 lectures, and 18.5 hours of on-demand content covering the complete retail analytics foundation: management, pricing, Python, forecasting, and deep learning

● 55 downloadable resources: Excel workbooks, Python project files, retail datasets, and pricing model templates

● Graded assignments in every section — all on real retail use cases, including a real apparel retailer dataset used in Sections 9, 10, 11, and 12

● Deep learning forecasting module (Section 11): ANN and RNN in Keras and TensorFlow — unique among all retail analytics courses on Udemy

● Middle-out hierarchical aggregation (Section 12): SKU-to-subfamily forecast reconciliation in Python

● Episode 1 of the RA Retail Series — continue with EP2 (Highest Rated: budgeting, OTB, assortment) and EP3 (customer analytics, trade area modelling)

● Lifetime access to all content and any future updates

● 30-day money-back guarantee — no questions asked

● Certificate of completion upon finishing the course

YOUR INSTRUCTOR

Haytham Omar, Ph.D.

Supply Chain & Business Intelligence Consultant · Developer · Trainer — UAE & France · Co-Founder, Keip (SaaS for retail management)

Haytham holds a Ph.D. in Supply Chain and Forecasting from the University of Bordeaux and a Master of Science in Global Supply Chain Management from Bordeaux École de Management. He is co-founder of Keip — a SaaS platform for retail management and analytics — and an active consultant who works with retailers including Sephora France and Sharaf Group Dubai.

He has trained over 70,000 supply chain and retail professionals across 70+ workshops in the UAE. Additional clients include Aster Group, DNO, PWC Training Academy Dubai, Qarar, and the Higher College of Technology. He is also the creator of the Inventorize package for Python and R — used by over 90,000 supply chain and retail professionals worldwide.

This course is Episode 1 of the three-part RA Retail Series. It builds the retail domain knowledge and Python foundation that the series builds on — from retail fundamentals and pricing analytics through to deep learning sales forecasting and middle-out hierarchical aggregation. EP2 and EP3 are available separately and together form the most complete retail analytics curriculum on any online learning platform.

Start your retail analytics journey. The most complete retail program on Udemy begins here.

13 sections · 18.5 hours · Excel + Python · ANN & RNN forecasting · Pricing analytics · EP1 of 3 · Lifetime access