RA: Retail Planning,Assortment Analytics with Excel & Python
RA: Retail Planning,Assortment Analytics with Excel & Python, available at $74.99, has an average rating of 4.63, with 206 lectures, 2 quizzes, based on 297 reviews, and has 14580 subscribers.
You will learn about Make a retail Budget from A-Z OTB Learn Python and Analytics merchandising Assortment planning Optimization Python Excel Metrics Operations Conversion rate Merchandise Retail Inventory Consignment Budget Budgeting Assortment This course is ideal for individuals who are Retail Managers or Retail Buyers or Analysts or Supply chain or Planners or merchandiser It is particularly useful for Retail Managers or Retail Buyers or Analysts or Supply chain or Planners or merchandiser.
Enroll now: RA: Retail Planning,Assortment Analytics with Excel & Python
Summary
Title: RA: Retail Planning,Assortment Analytics with Excel & Python
Price: $74.99
Average Rating: 4.63
Number of Lectures: 206
Number of Quizzes: 2
Number of Published Lectures: 206
Number of Published Quizzes: 2
Number of Curriculum Items: 208
Number of Published Curriculum Objects: 208
Original Price: $84.99
Quality Status: approved
Status: Live
What You Will Learn
- Make a retail Budget from A-Z
- OTB
- Learn Python and Analytics
- merchandising
- Assortment planning
- Optimization
- Python
- Excel
- Metrics
- Operations
- Conversion rate
- Merchandise
- Retail
- Inventory
- Consignment
- Budget
- Budgeting
- Assortment
Who Should Attend
- Retail Managers
- Retail Buyers
- Analysts
- Supply chain
- Planners
- merchandiser
Target Audiences
- Retail Managers
- Retail Buyers
- Analysts
- Supply chain
- Planners
- merchandiser
New update : Forecast for OTB calculation with AutoML is added (Aug 2023)
“This is one of the three courses in the Retail Series by RA, each course can be taken independently.”
Master Retail planning 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.
Retail Planning and assortment analytics with Excel & Python.
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 resourcesand Python project files,homework andProgram quizzes,lifetime access, and a30-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 metricsfollowed 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.
Course Curriculum
Chapter 1: Retail Metrics
Lecture 1: Introduction
Lecture 2: Curriculum
Lecture 3: Intro To Benchmarking
Lecture 4: Benchmarking
Lecture 5: Retail Metrics
Lecture 6: Operational metrics
Lecture 7: Operational metrics excel
Lecture 8: Inventory metrics
Lecture 9: Inventory Metrics in excel
Lecture 10: Inventory turnover
Lecture 11: Tesco- Walmart – Kroger
Lecture 12: Gross Margin and Net Profit
Lecture 13: Assets Metrics
Lecture 14: Cash to Cash Cycle time Intro
Lecture 15: Cash to cash cycle time
Lecture 16: Cash to Cash in excel
Lecture 17: Keys to Improve
Lecture 18: Data Orientation
Lecture 19: Summary
Chapter 2: Welcome to Python
Lecture 1: Python
Lecture 2: Downloading Anaconda
Lecture 3: Installing Anaconda
Lecture 4: Spyder Overview
Lecture 5: Jupiter Notebook Overview
Lecture 6: Python Libraries
Lecture 7: Inventories Package
Chapter 3: Python Crash Course
Lecture 1: Intro
Lecture 2: Dataframes
Lecture 3: Arithmetic Calculations with python
Lecture 4: Lists
Lecture 5: Dictionaries
Lecture 6: Arrays
Lecture 7: Importing data in python
Lecture 8: Subsetting Data frames
Lecture 9: Conditions
Lecture 10: Writing functions
Lecture 11: Mapping
Lecture 12: for loops
Lecture 13: For looping a function
Lecture 14: Mapping on a data frame
Lecture 15: For looping on a dataframe
Lecture 16: Summary
Lecture 17: Assignment
Lecture 18: Answer Part 1
Lecture 19: Assignment answer 2
Chapter 4: Manipulation and cleaning of Retail Data.
Lecture 1: Intro
Lecture 2: Dropping Duplicates and NAs
Lecture 3: Conversions lecture
Lecture 4: Conversions
Lecture 5: Filterations
Lecture 6: Imputations
Lecture 7: Indexing toutorial
Lecture 8: Slicing index
Lecture 9: Manipulation Lecture
Lecture 10: Group by
Lecture 11: Slicing the group by
Lecture 12: Dropping levels
Lecture 13: The Proper form
Lecture 14: Pivot tables
Lecture 15: Aggregate functions in pivot table
Lecture 16: Melting the data
Lecture 17: Left join
Lecture 18: Inner and outer join
Lecture 19: Joining in python
Lecture 20: Inner, left and full join
Lecture 21: Summary
Lecture 22: Assignment
Lecture 23: Answer 1
Lecture 24: Answer 2
Lecture 25: Answer 3
Lecture 26: Answer 4
Lecture 27: Answer 5
Chapter 5: Metrics in Python
Lecture 1: Intro
Lecture 2: Preparing data for conversion rate
Lecture 3: Conversion rate
Lecture 4: Daily orders
Lecture 5: Dictionary for orders
Lecture 6: Average transaction value and average selling price
Lecture 7: Operational Metrics conclusion
Lecture 8: Stocks Discovery
Lecture 9: Daily Sales
Lecture 10: Creating Common Keys
Lecture 11: Creating a year week key
Lecture 12: Average weekly
Lecture 13: Joining sales with stocks
Lecture 14: Calculating inventory turnover
Lecture 15: Conclusion
Lecture 16: Section 2 Assignment
Lecture 17: Assignment answer
Lecture 18: Summary
Chapter 6: Managing Retail through metrics
Lecture 1: Mark-ups and Margins
Lecture 2: Top line and Bottom line
Lecture 3: Sales per squarefoot
Instructors
-
Haytham Omar-Ph.D
Consultant-Supply chain
Rating Distribution
- 1 stars: 4 votes
- 2 stars: 8 votes
- 3 stars: 18 votes
- 4 stars: 77 votes
- 5 stars: 190 votes
Frequently Asked Questions
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Can I take my courses with me wherever I go?
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