RA: Retail Management, Analytics with Excel & Python.
RA: Retail Management, Analytics with Excel & Python., available at $64.99, has an average rating of 4.93, with 194 lectures, 2 quizzes, based on 138 reviews, and has 10759 subscribers.
You will learn about Become a Retail Expert planner! Learn Python Retail Management Deep learning Pricing Forecasting Pandas Retail analytics Visual Merchandising This course is ideal for individuals who are retail managers or Retail analyst or Data analyst or Demand Planners It is particularly useful for retail managers or Retail analyst or Data analyst or Demand Planners.
Enroll now: RA: Retail Management, Analytics with Excel & Python.
Summary
Title: RA: Retail Management, Analytics with Excel & Python.
Price: $64.99
Average Rating: 4.93
Number of Lectures: 194
Number of Quizzes: 2
Number of Published Lectures: 194
Number of Published Quizzes: 2
Number of Curriculum Items: 196
Number of Published Curriculum Objects: 196
Original Price: $84.99
Quality Status: approved
Status: Live
What You Will Learn
- Become a Retail Expert planner!
- Learn Python
- Retail Management
- Deep learning
- Pricing
- Forecasting
- Pandas
- Retail analytics
- Visual Merchandising
Who Should Attend
- retail managers
- Retail analyst
- Data analyst
- Demand Planners
Target Audiences
- retail managers
- Retail analyst
- Data analyst
- Demand Planners
“This is one of the three courses in the Retail Series by RA, each course can be taken independently.”
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.
Retail Management, Analytics with Excel & Python.
1- Understanding the retail environment.
2- Retail Formats.
3- Retail Fundamentals
2- Manipulation of Data with Pandas.
2-Working with Python for analytics.
3- Developing Forecasts with Excel.
4- Adjusting Product Prices to maximize revenue.
5- Forecasting In python with Deep learning and regression techniques, ANN and RNN.
6- Product placement strategies inside the stores.
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 fundamentalsfollowed 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 Fundmentals
Lecture 1: Curriculum summary
Lecture 2: Curriculum Layout
Lecture 3: What are retailers?
Lecture 4: Introduction
Lecture 5: Types of retail
Lecture 6: Taking part of the value chain
Lecture 7: Verticals
Lecture 8: Food and near food retailers
Lecture 9: Comparison between food and near food retailers
Lecture 10: General Merchandize retailers
Lecture 11: Amazon Vs Barnes and noble
Lecture 12: Will online replace physical retail ?
Lecture 13: Multi-channel enviroment
Lecture 14: Verticals types
Chapter 2: Retail management
Lecture 1: Merchandise mix
Lecture 2: SKU types
Lecture 3: Breadth vs Width
Lecture 4: Deep Vs Shallow assortment
Lecture 5: Retailer Brand
Lecture 6: Category Role
Lecture 7: Category strategy
Chapter 3: pricing
Lecture 1: Pricing
Lecture 2: Cost based pricing
Lecture 3: Value based pricing
Lecture 4: Visual Merchandizing
Chapter 4: Demand Based Pricing
Lecture 1: Importance of Pricing
Lecture 2: Intro
Lecture 3: Linear Regrression
Lecture 4: Price Response function
Lecture 5: Logistic Regression
Lecture 6: Logistic Price Response function
Lecture 7: Linear Price Function Estimation
Lecture 8: Correction
Lecture 9: Logit Price function
Lecture 10: Simulating the price.
Lecture 11: Elasticity intro
Lecture 12: ELASTICITY
Lecture 13: Elasticity fo logit and linear
Lecture 14: Assignment
Lecture 15: Answer
Lecture 16: Response function variants- Polynomial
Lecture 17: Some examples of elasticity
Lecture 18: Willingness to pay
Lecture 19: Point of Maximum profit
Lecture 20: Summary
Chapter 5: Markdowns
Lecture 1: Intro
Lecture 2: Markdowns
Lecture 3: Why we do Markdowns?
Lecture 4: Customer segments to markdowns
Lecture 5: Problem formulations.
Lecture 6: Markdown for multiple periods
Lecture 7: setting up solver
Lecture 8: Salvage value
Lecture 9: Markdowns with forecasting
Lecture 10: Sensitivity analysis
Lecture 11: Markdowns for one period
Lecture 12: Assignment
Chapter 6: Intro to python
Lecture 1: Python
Lecture 2: Downloading Anacoonda
Lecture 3: Installing Anacoonda
Lecture 4: Spyder overview
Lecture 5: Jupiter Notebook overview
Lecture 6: Python Libraries
Lecture 7: Inventorize Package
Chapter 7: 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 dataframes
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 dataframe
Lecture 15: for looping on a dataframe
Lecture 16: Summary
Lecture 17: Assignment
Lecture 18: Assignment answer1
Lecture 19: Assignment answer2
Chapter 8: Retail Manipulation and data cleaning
Lecture 1: Manipulation Inro
Lecture 2: Dropping Duplicates and NAs
Lecture 3: Conversions Lecture
Lecture 4: Conversions
Lecture 5: Filterations
Lecture 6: Imputations
Lecture 7: Indexing tutorial
Instructors
-
Haytham Omar-Ph.D
Consultant-Supply chain
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 4 votes
- 3 stars: 15 votes
- 4 stars: 29 votes
- 5 stars: 87 votes
Frequently Asked Questions
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Can I take my courses with me wherever I go?
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