“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. in this course, we focus on the customer analytics part of retail. Understanding the customer is key for maintaining loyalty and developing products to boost retail business and profitability.
RA: Retail Customer Analytics and Trade Area Modeling.
1- Understanding the importance of customer analytics in retail.
2- Manipulation of Data with Pandas.
3-Working with Python for analytics.
5- Trade area modeling
6- Recommendation systems
7- Customer lifetime value prediction
8- Market Basket analytics
9- Churn prediction
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 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.
Installing Python
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1Introduction
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2Tesco and Andrew Pole
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3False Positives
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4Walmart
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5Notable mentions
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6Why Customer analytics
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7Curriculum
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8The retail Customer
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9types of retail customers
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10Types of retail customrs
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11Why we need customer analytics
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12types of retail Data
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13Sales Data Vs Market basket Data
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14Retail Data structre
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15Customer analytics and machine learning applications
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16Quiz on section 1
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17Summary
Python Programming Fundmentals
Manipulation of Retail Data
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24Intro
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25Data Frames
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26Arithmetic Calculations in Python
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27Lists
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28Dictionaries
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29Arrays
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30Importing Data in Python
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31Subsetting DataFrames
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32Conditions
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33Writing Functions
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34Mapping
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35For Loops
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36For looping a function
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37Mapping on Dataframe
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38For Looping a DataFrame
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39Summary
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40Assignment
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41Assignment Answer 1
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42Assignment Answer 2
Trade Area Modeling
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43Inro
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44Dropping Duplicates and NAs
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45Conversions lecture
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46Conversions
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47Filterations
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48Imputations
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49Indexing Tutorial
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50Slicing index
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51Manipulation lecture
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52Groupby
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53Slicing the Groupby
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54Dropping levels
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55The proper form
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56Pivot tables
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57Aggregate function in pivot table
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58Melting the Data
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59Left join
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60inner & outer join
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61Joining in Python
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62inner, left join and full join(outer)
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63Summary
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64Assignment
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65Assignment answer 1
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66Assignment answer 2
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67Assignment answer 3
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68Assignment answer 4
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69Assignment answer 5
Customer RFM analysis
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70Tade Area Modelling
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71Introduction
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72Different trade area modelling
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73Drive time and Zip codes
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74The huff model
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75Some considerations about trade area modeling
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76Summary of a Huff model
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77Huff Model
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78Example Demonstration
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79Scaling attractiveness
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80Developing Huff model
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81The Huff model in Python
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82Reading the data in python
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83Getting the upper term
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84Probability per Customer Community
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85Where should I locate my store ?
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86Assignment
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87Assignment Answer
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88Summary