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EP1: Learn Python & Retail Fundamentals ,Forecast Retail Sales with Deep learning, Master product placement & Pricing.

What you will learn

Become a Retail Expert planner!

Learn Python

Retail Management

Deep learning

Pricing

Forecasting

Pandas

Retail analytics

Visual Merchandising

Description

“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.


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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 resources and Python project files, homework and Program quizzes, lifetime access, and a 30-day money-back guarantee.

Who this Program is for:

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

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

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

Β· 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 fundamentals 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.

English
language

Content

Introduction
Curriculum
What are retailers?
Introduction
Types of retail
Taking part of the value chain
Verticals
Food and near food retailers
Comparison between food and near food retailers
General Merchandize retailers
Amazon Vs Barnes and noble
Will online replace physical retail ?
Multi-channel enviroment
Verticals types
Quiz on Section 1
Retail management
Merchandise mix
SKU types
Breadth vs Width
Deep Vs Shallow assortment
Retailer Brand
Category Role
Category strategy
Quiz on section 2
pricing
Pricing
Cost based pricing
Value based pricing
Visual Merchandizing
Demand Based Pricing
Importance of Pricing
Intro
Linear Regrression
Price Response function
Logistic Regression
Logistic Price Response function
Linear Price Function Estimation
Logit Price function
Simulating the price.
Elasticity intro
ELASTICITY
Elasticity fo logit and linear
Assignment
Answer
Some examples of elasticity
Willingness to pay
Point of Maximum profit
Summary
Markdowns
Intro
Markdowns
Why we do Markdowns?
Customer segments to markdowns
Problem formulations.
Markdown for multiple periods
setting up solver
Salvage value
Markdowns with forecasting
Sensitivity analysis
Markdowns for one period
Assignment
Intro to python
Python
Downloading Anacoonda
Installing Anacoonda
Spyder overview
Jupiter Notebook overview
Python Libraries
Python programming fundmentals
Intro
Dataframes
Arithmetic calculations with python
Lists
Dictionaries
Arrays
Importing data in python
Subsetting dataframes
Conditions
Writing functions
Mapping
for loops
for looping a function
Mapping on a dataframe
for looping on a dataframe
Summary
Assignment
Assignment answer1
Assignment answer2
Manipulation and data cleaning
Manipulation Inro
Dropping Duplicates and NAs
Conversions Lecture
Conversions
Filterations
Imputations
Indexing tutorial
Slicing index
Manipulation Lecture
Groupby
slicing groupby
Dropping levels
The proper form
Pivot tables
Aggregate functions on pivot table
Melting Data
Left Join
Inner and outer join
Joining in python
Inner, left join and full join (outer)
Summary
Assignment
Assignment Answer1
Assignment Answer2
Assignment Answer3
Assignment Answer4
Assignment Answer5
Dates and measuring frequency of buy for customers.
Dates intro
Datetime
Last purchase date and recency
Recency Histogram
Modeling inter-arrival time
Modeling inter-arrival time 2
Modeling inter-arrival time 3
Resampling
Rolling time series
Rolling time series 2
Summary
Assignment
Assignment answer
Product Positioning inside the store
Pareto Law
ABC Demo in excel
Intro
retail strategies for product placement
Exploring the apparel retailer dataset
Checking the sales per section
Revenue Column
Apparel Profit
Volume drivers
Items_grouped
Inventorize
Capturing Volume drivers
Strategizing the Categories
Assignment Product placment
Product placement conclusion
Forecasting 2020
Forecasting
Process of forecasting
Forecasting approaches
Trend and seasonality
Forecasting prepration
Fitting the model
Forecasting next year demand
Preparing the data for forecasting
Subfamilies
Spreading
Getting the trend
one hot encoding
forecasting Perfumes
Measuring accuracy
Developing a forecasting function
Checking all subfamilies
Updating the data
forecasting the future
Conclusion
Assignment Country Forecasting of Sales
Assignment answer
Deep learning forecasting
Intro
Installing Keras and Tensorflow
How neural networks work?
output value
Activation functions
Slope Calculations
forward and backward propgation
Preparing the data
Developing ANN sequential model
Training and testing
Recurrent neural networks
Comparison 1
Plotting the comparison
Functions for learning
Developing forecast function
Forecasting function trial
Forecasting all subfamilies
Conclusion ANN
Deep learning assignment
Deep learning answer
Middle-out
Intro
Sku-Sales
Data Manipulation
Weekly subfamily_Sales
Getting the proportions
Forecasting Manipulation
Joining part 1
Unifying data
Forecast SKU level
Aggregate cocnlusion
Middle out assignment
Answer
Answer 2
Wrap Up