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2021-02-24 05:19:04
30-Day Money-Back Guarantee
Business Business Analytics & Intelligence Retail Business

RA: Retail Budgeting,Metrics and Buying with Excel & Python.

Learn Python, Retail Budgeting and Metrics, OTB planning, Inventory and Assortment of retail space and price.
New
Rating: 0.0 out of 50.0 (0 ratings)
9 students
Created by Haytham Omar
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • OTB
  • Retail Planning
  • Budgeting
  • merchandising
  • Assortment planning
  • Optimization
  • Python
  • Excel
  • Metrics
  • Operations
  • Conversion rate
  • Merchandise
  • Retail
  • Inventory
  • Consignment

Requirements

  • Excel and python Knowledge.
  • No Need for Prerequisite 1 if you already know Python.

Description




Master Retail management and analytics with Excel and Python

Retailers face fierce competition every day and keeping up with the new trends and customer preferences is a guarantee for excellence in the modern retail environment. one Keyway to excel in retail management is utilizing the data that is produced every day. It is estimated that We produce an overwhelming amount of data every day, roughly 2.5 quintillion bytes. According to an IBM study, 90% of the world’s data has been created in the last two years.


Retail analytics is the field of studying the produced retail data and making insightful data-driven decisions from it. as this is a wide field, I have split the Program into three parts.


EP 2: Budgeting, Buying, and Metrics in Retail.

1- Retail Metrics

2- Metrics in Python.

3- Budgeting.

4- Retail Planning in Python.

5- Retail Buying.

6- Assortment Optimization.

7- Managing Retail Inventory with Python.

8- Managing stocks based on Types.




Don't worry If you don't know how to code, we learn step by step by applying retail analysis!

*NOTE: Full Program includes downloadable resources and Python project files, homework and Program quizzes, lifetime access, and a 30-day money-back guarantee.

Who this Program is for:

· If you are an absolute beginner at coding, then take this Program.

· If you work in Retail and want to make data-driven decisions, this Program will equip you with what you need.

· If you are switching from Excel to a data science language. then this Program will fast-track your goal.

· If you are tired of doing the same analysis again and again on spreadsheets and want to find ways to automate it, this Program is for you.


Program Design

the Program is designed as experiential learning Modules, the first couple of modules are for retail metrics followed by Python programming fundamentals, this is to level all of the takers of this Program to the same pace. and the third part is retail applications using Data science which is using the knowledge of the first two modules to apply. while the Program delivery method will be a mix of me explaining the concepts on a whiteboard, Presentations, and Python-coding sessions where you do the coding with me step by step. there will be assessments in most of the sections to strengthen your newly acquired skills. all the practice and assessments are real retail use cases.





Who this course is for:

  • Retail Managers
  • Retail Buyers
  • Analysts
  • Supply chain
  • Planners
  • merchandiser

Course content

11 sections • 188 lectures • 16h 56m total length

  • Preview02:16
  • Preview03:19
  • Preview04:49
  • Benchmarking
    07:01
  • Preview02:00
  • Operational metrics
    05:56
  • Operational metrics excel
    08:29
  • Inventory metrics
    04:47
  • Inventory Metrics in excel
    04:48
  • Inventory turnover
    01:52
  • Tesco- Walmart - Kroger
    01:50
  • Gross Margin and Net Profit
    04:30
  • Assets Metrics
    05:19
  • Cash to Cash Cycle time Intro
    06:02
  • Cash to cash cycle time
    12:02
  • Cash to Cash in excel
    05:26
  • Keys to Improve
    05:45
  • Data Orientation
    04:30
  • Quiz on metrics
    9 questions
  • Summary
    02:14

  • Python
    02:36
  • Downloading Anaconda
    01:34
  • Installing Anaconda
    01:03
  • Spyder Overview
    11:38
  • Jupiter Notebook Overview
    02:54
  • Python Libraries
    05:30
  • Inventories Package
    01:05

  • Intro
    07:24
  • Dataframes
    02:33
  • Arithmetic Calculations with python
    08:30
  • Lists
    06:34
  • Dictionaries
    05:01
  • Arrays
    03:14
  • Importing data in python
    07:57
  • Subsetting Data frames
    08:40
  • Conditions
    04:28
  • Writing functions
    08:00
  • Mapping
    02:14
  • for loops
    02:16
  • For looping a function
    04:14
  • Mapping on a data frame
    05:41
  • For looping on a dataframe
    08:17
  • Summary
    08:34
  • Assignment
    02:10
  • Answer Part 1
    05:44
  • Assignment answer 2
    09:58

  • Intro
    02:22
  • Dropping Duplicates and NAs
    06:46
  • Conversions lecture
    04:29
  • Conversions
    05:11
  • Filterations
    07:49
  • Imputations
    04:49
  • Indexing toutorial
    04:31
  • Slicing index
    03:47
  • Manipulation Lecture
    05:17
  • Group by
    07:15
  • Slicing the group by
    04:34
  • Dropping levels
    02:15
  • The Proper form
    03:46
  • Pivot tables
    07:45
  • Aggregate functions in pivot table
    05:30
  • Melting the data
    05:34
  • Left join
    04:20
  • Inner and outer join
    03:41
  • Joining in python
    04:27
  • Inner, left and full join
    06:20
  • Summary
    01:52
  • Assignment
    02:28
  • Answer 1
    05:53
  • Answer 2
    05:52
  • Answer 3
    07:43
  • Answer 4
    07:20
  • Answer 5
    14:06

  • Intro
    01:46
  • Preparing data for conversion rate
    05:28
  • Conversion rate
    11:34
  • Daily orders
    04:51
  • Dictionary for orders
    02:53
  • ATV, UPT and ASP
    05:36
  • Operational Metrics conclusion
    05:27
  • Stocks Discovery
    03:34
  • Daily Sales
    07:06
  • Creating Common Keys
    05:40
  • Creating a year week key
    07:53
  • Average weekly
    08:00
  • Joining sales with stocks
    03:20
  • Calculating inventory turnover
    09:04
  • Conclusion
    06:51
  • Section 2 Assignment
    02:23
  • Assignment answer
    08:35
  • Summary
    01:34

  • Preview03:57
  • Top line and Bottom line
    07:33
  • Sales per squarefoot
    05:04
  • Actual stocks vs Budgeted Stocks
    05:13
  • Conversion Rate explanation
    05:03
  • Average selling price and shrinkage rate
    05:08
  • ROI
    02:36
  • 30 cents on the dollar
    05:53
  • Mark-ups vs Magins
    03:26
  • Supplier Retailer Margin Dynamics
    05:44
  • Practical example inside python
    03:18
  • Retail Price, actual price and Dollar Markdowns
    04:41
  • Calculating Margin and Markups
    10:02
  • Markdowns Percentage
    03:02
  • Weeks of stocks
    08:05
  • Weeks of stocks part2
    05:19
  • VMC
    07:26
  • Quiz
    7 questions
  • Summary
    02:39

  • Budgeting Intro
    06:14
  • Budgeting Introduction
    05:47
  • Forecasting is the trigger point
    03:32
  • Budgeting Components
    02:55
  • Top Down Forecasting for budget
    05:01
  • Bottom Up
    04:09
  • OTB
    06:27
  • Budget - Art of retail buying
    04:30
  • Budget Markdowns and Stock turns
    05:12
  • End to month stocks
    07:46
  • Conclusion
    02:30
  • Budgeting-Seasonality
    06:04
  • OTB planning
    13:17
  • Preview05:33
  • OTB planning method 2
    07:05
  • Variations to plan
    06:47
  • Assignment budgeting
    02:08
  • Assignment Answer
    03:39
  • Summary
    03:44

  • Assortment Intro
    03:22
  • Assortment planning
    02:54
  • Brand Assortment Planning
    02:49
  • Store Persona and Planograms
    04:39
  • Seasonal Calendar Assortment
    02:40
  • Space Planning
    03:14
  • Space assortment Modeling
    06:16
  • Modeling sales against retail space
    04:55
  • Predicting sales with log model
    06:13
  • Building assortment optimization model
    10:04
  • Maximizing Retail Space Profit
    03:38
  • Preview09:01
  • Coefficients
    03:58
  • Developing the Optimization Function
    14:25
  • Optimizing
    07:33
  • Assortment assignment
    01:36
  • Assortment answer
    05:42

  • Inventorize Package
    01:05
  • Introduction
    05:37
  • Competing Products
    04:15
  • Relation among products
    02:58
  • Multi-variate Regression in python
    11:04
  • Multinomial choice models
    05:17
  • Multinomial Logit models
    05:01
  • Multi-competing Products in Python
    04:50
  • Competing Products Summary
    01:51

  • Intro
    03:23
  • Preview08:00
  • Seasonal Products
    05:10
  • Seasonal Products
    08:38
  • Points of maximum profits
    07:13
  • How much I will sell?
    01:23
  • Data Table
    05:26
  • Critical Ratio
    04:48
  • Critical ratio in excel
    09:11
  • What is actually happening
    01:47
  • Critical ratio in python
    09:14
  • Preparing Data for MPN
    08:16
  • Creating Margin pf error in python
    04:31
  • Applying MPN on all data
    08:43
  • Conclusion
    03:39
  • Seasonal inventory Summary
    02:52
  • Assignment
    01:10
  • Assignment answer
    04:25

Instructor

Haytham Omar
Consultant-Supply chain
Haytham Omar
  • 4.5 Instructor Rating
  • 323 Reviews
  • 1,639 Students
  • 8 Courses

"Never follow a book by its cover, In a world that changes every second, we must be resilient and proactive. " Consultant / Developer /Trainer


Consultant in Supply Chain Management & Business Intelligence

Founder - Rescale Analytics – Dubai

• PhD student at the University of Bordeaux.

• Supply chain and data science consultant for several national and multinational clients in the UAE and France.

• Data Scientist, Master of Science in Global Supply Chain Management from Bordeaux Ecole de Management, Bordeaux, France


He is currently conducting workshops and seminars in Supply chain and data science as well as consultancy projects for Sephora, Sharaf group and aster pharmacy.

• Haytham conducted more than 70 workshops in UAE in the last three years in supply chain and data science.

Clients :

Sharaf Group

Aster Group

Sephora France

DNO

Qarar for financial analytics

Lamprell

Noble Prog

PWC training Academy

Higher College of Technology

Partners: Edusphere dubai- Limar Tech Saudi arabia – sl-tech Abu Dhabi

Projects :AHQ-SHARAF GROUP:

developing an algorithm for the replenishment of stocks to Sharaf group -adventure HQ . the algorithm is being used since November 2019 with notable results.


Developing and deploying an algorithm for revenue maximization based on elasticity techniques for adventure zones service provided by AHQ.


Projects: Sephora – France


Haytham is working with Kedge business school as part of the team CSIT for partnership

The collaboration of Omni-channel optimization with Sephora France. As a part of his Ph.D. project.

Development: Haytham developed the Inventorize package in R mainly used for supply chain analytics with more than 12000 Downloads so far.

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