RA: Data Science and Supply chain analytics.(A-Z with R)
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- Curriculum
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made by a supply chainer for supply chainers. A course designed for the modern supply chain profession.
” 20000 Professionals are using inventorize across R & Python. Know how to use it only in this course”
It’s been seven years since I moved from Excel to R and since then I have never looked back! With eleven years between working in Procurement, lecturing in universities, training over 2000 professionals in supply chain and data science with R and python, and finally opening my own business in consulting for two years now. I am extremely excited to share with you this course and learn with you through this unique rewarding course. My goal is that all of you become experts in data science and supply-chain. I have put all the techniques I have learned and practiced in this one sweet bundle of data science and supply chain.
As a consultancy, we develop algorithms for retailers and supply chains to make aggregate and item controllable forecasting, optimize stocks, plan assortment and Maximize profit margin by optimizing prices. 13000 people are already using our free package for supply chain analysis “Inventorize” and we can’t wait to share its capabilities with you so you can start dissecting supply chain problems…for free!
The motivation behind this project is filling the gap of finding a comprehensive course that tackles supply chains using data science. there are courses for data science, forecasting, revenue management, inventory management, and simulation modeling. but here we tackle all of them as a bundle. Lectures, Concepts, codes, exercises, and spreadsheets. and we don’t present the code, we do the code with you, step by step.
the abundance of data from customers, suppliers, products, and transactions have opened the way for making informed business decisions on a bigger and more dynamic scale that can no longer be achieved by spreadsheets. In this course, we learn data science from a supply chain mindset.
Don’t worry If you don’t know how to code, we learn step by step by applying supply chain analysis!
*NOTE: Full course includes downloadable resources and R project files, homework and course quizzes, lifetime access, and a 30-day money-back guarantee.
Who this course is for:
· If you are an absolute beginner at coding, then take this course.
· If you work in supply-chain and want to make data-driven decisions, this course will equip you with what you need.
· If you work as a demand planner and want to make aggregate and item controllable forecasting, take this course.
· If you are an inventory manager and want to optimize inventory for 1000000 products at once, then this course is for you.
· If you work in finance and want to forecast your budget by taking trends, seasonality, and other factors into account then this course is just what you need.
· If you are a seasoned R user, then take this course to get up to speed quickly with R capabilities. You will become a regular R user in no time.
· If you want to take a deep dive (not just talking) in supply chain management, then take this course.
· If you want to apply machine learning techniques for supply -chain, we will walk you through the methods of supervised and unsupervised learning.
· If you are switching from Excel to a data science language. then this course 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 course is for you.
· If you are frustrated about the limitations of data loading and available modules in excel, then Moving to R will make our lives a whole lot easier.
Course Design
the course is designed as experiential learning Modules, the first couple of modules are for supply chain fundamentals followed by R programming fundamentals, this is to level all of the takers of this course to the same pace. and the third part is supply chain applications using Data science which is using the knowledge of the first two modules to apply. while the course delivery method will be a mix of me explaining the concepts on a whiteboard, Presentations, and R-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 supply chain use cases.
Supply chain Fundamentals Module includes:
1- Introduction to supply chain.
2- Supply chain Flows.
3- Data produced by supply chains.
R Programming Fundamentals Module includes:
1- Basics of R
2- Data cleaning and Manipulation.
3- Statistical analysis.
4- Data Visualization.
5- Advanced Programming.
Supply chain Applications Module include :
1- Product segmentations single and Multi-criteria
2- Supplier segmentations.
3- Customers segmentations.
4- Forecasting techniques and accuracy testing.
5- Forecasting aggregation approaches.
6- Pricing and Markdowns optimization Techniques.
7- Inventory Policy and Safety stock Calculations.
8- Inventory simulations.
9- Machine Learning for supply-chain.
10- Product Recommendations for customers.
11- Simulations for optimizing Capacity and Resources.
*NOTE: Many of the concepts and analysis I explain first in excel as I find excel the best way to first explain a concept and then we scale up, improve and generalize with R. By the end of this course, you will have an exciting set of skills and a toolbox you can always rely on when tackling supply chain challenges. The course may take from 12-16 weeks to finish, 4-5 hours of lectures, and practice every week.
*Bonus: one hour webinar of Intro to machine learning where I am the panelist for NOBLE PROG; the host and organizer of the webinar event. the webinar has a demo on how to use orange for data mining.
Happy Supply Chain mining!
Haytham
Rescale Analytics
Feedback from Clients and Training:
“In Q4 2018, I was fortunate to find an opportunity to learn R in Dubai, after hearing about it from indirect references in UK.
I attended a Supply Chain Forecasting & Demand Planning Masterclass conducted by Haitham Omar and the possibilities seemed endless. So, we requested Haitham to conduct a 5-day workshop in our office to train 8 staff members, which opened us up as a team to deeper data analysis. Today, we have gone a step further and retained Haitham, as a consultant, to take our data analysis to the next level and to help us implement inventory guidelines for our business. The above progression of our actions is a clear indication of the capabilities of Haitham as a specialist in R and in data analytics, demand planning, and inventory management.”
Shailesh Mendonca
Commercial lead-in Adventure AHQ- Sharaf Group
“ Haytham mentored me in my Role of Head of Supply Chain efficiency. He is extremely knowledgebase about the supply concepts, latest trends, and benchmarks in the supply chain world. Haytham’s analytics-driven approach was very helpful for me to recommend and implement significant changes to our supply chain at Aster group”
Saify Naqvi
Head of Supply Chain Efficiency
“I participated to the training session called “Supply Chain Forecasting & Management” on December 22nd 2018. This training helped me a lot in my daily work since I am working in Purchase Dpt. Haytham have the pedagogy to explain us very difficult calculations and formula in simple way. I highly recommend this training.”
Djamel BOUREMIZ
Purchasing Manager at Mineral Circles Bearings
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1Why I chose R for this course ?Video lesson
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2Why we should Learn Coding.Video lesson
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3CurriclumVideo lesson
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4Supply chain VisualizationVideo lesson
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5Cost and Service Dynamics.Video lesson
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6Service level and Product CharacteristicsVideo lesson
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7Customer and Supplier CharacteristicsVideo lesson
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8Supply chain ViewsVideo lesson
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9The Financial FlowVideo lesson
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10Why is supply chain ComplicatedVideo lesson
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11IntroductionVideo lesson
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12Types Of Data in supply chainVideo lesson
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13Data From suppliersVideo lesson
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14Data From ProductionVideo lesson
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15Data From StocksVideo lesson
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16Data From Sales & CustomersVideo lesson
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17Why we Need to learn Data Science?Video lesson
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18Analytics TypesVideo lesson
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27IntroductionVideo lesson
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28Different Data Structures and types in RVideo lesson
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29Do arithmetic Calculations in R and write vectorsVideo lesson
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30Creating a listVideo lesson
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31Importing Data in R and basic Exploration functionsVideo lesson
in minute 12:13 I say, Quantity is a character, Quantity is not a character, Quantity is numeric. Sometimes when there are missing data or letters wrongly put in the column of quantity, it becomes a character. but then we have to convert it to numeric.
Thanks, Anis for pointing it out.
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32Selecting Data in dataframe.Video lesson
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33If Else functionVideo lesson
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34ConditionsVideo lesson
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35Function with conditionsVideo lesson
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36for_loopsVideo lesson
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37applying a function inside for loopVideo lesson
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38For loop on a data_frameVideo lesson
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39applying the function on a data frameVideo lesson
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40AssignmentVideo lesson
Welcome to our first assignment.
Please open a new Rscript and call it "section 4 assignment"
in this assignment, we will work on the car's data set. it has the features of 400 cars from horsepower to speed and price.
part 1:
1- How many Rows are in the cars dataset?
2- How many Columns are in the car's data set?
3- How many unique numbers of cylinders we have in the cars dataset?
4- what is the average horsepower of cars? hint: summary function will be helpful.
5- what is the maximum horsepower?
6- what is the most expensive car?
7- change the name of the column "name" to "car name"
part 2:
8- make a subset of the data that has the car name and the price and name the new subsetted data frame car pricing.
9- create a function called pricing category that returns "Budget Car " if the cars are less than 20,000 USD," Suitable Car " is the car is more than 20,000 and less than 35 000 and finally an expensive car for cars more than 35000.
Check the function screenshot "price_category_function.png" in the resource folder please for guidance.
10- create a column named category on the subset using a for loop and pricing category function.
11- How many Budget cars, suitable cars, and expensive cars we have?
As always, please first try to answer on your own and then have a look at the solved script(attached).
All the best,
Haytham
Rescale analytics
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41Assignment section 4 answer Part 1Video lesson
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42Assignment Section 4 Answer Part 2Video lesson
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43SummaryVideo lesson
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44IntroVideo lesson
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45Calculating Measures of Centrality and spread 1Video lesson
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46Calculating Measures of Centrality and spread 2Video lesson
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47Putting the Measures togetherVideo lesson
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48CorrelationsVideo lesson
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49Correlation ThresholdsVideo lesson
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50Calculating CorrelationsVideo lesson
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51Detecting outliersVideo lesson
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52Outliers in RVideo lesson
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53Intro to Linear RegressionVideo lesson
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54Linear RegressionVideo lesson
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55Intro to Distributions.Video lesson
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56Distributions importance in supply chainVideo lesson
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57Chi-Square testsVideo lesson
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58Distributions in ExcelVideo lesson
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59Distributions Chi-Square testVideo lesson
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60Cover for 90% of the Normal DistributionVideo lesson
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61Assignment Distributions in ExcelVideo lesson
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62Assignment answer: Bike Demand Chi-square testVideo lesson
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63Distributions in RVideo lesson
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64AssignmentVideo lesson
Please Make a new Rscript and name it Section 5 assignment.
in this assignment, we will work on pineapple juice data that has the demand and the price every day for this juice.
1- Fit the demand of this Distribution to normal demand and see if the fit is normal.
3-Fit the demand using the fitdist function, is it still normal?
4- Make a linear regression using lm function y~x and outline the coefficients and the intercept.
As always, please first try to answer on your own and then have a look at the solved script.
All the best,
Haytham
Rescale analytics
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65Assignment answerVideo lesson
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66SummaryVideo lesson
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67IntroVideo lesson
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68Intro To DplyrVideo lesson
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69Investigate with DplyrVideo lesson
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70Unique InvoicesVideo lesson
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71Average Invoice Value Per CountryVideo lesson
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72Average Number of items within an Invoice.Video lesson
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73JoiningVideo lesson
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74Changing date time to dateVideo lesson
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75Pivot WiderVideo lesson
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76Pivot LongerVideo lesson
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77Separate and pasteVideo lesson
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78putting it all togetherVideo lesson
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79Assignment: New York airlinesVideo lesson
Newyork air flights in the year 2013 are available at the R package newyork13. the data is segregated as a relational database with keys that you can connect the datasets with. the description of the columns in the data is in the case study description document. we can answer some very interesting questions using dplyr.
I have attached for you information about the data in the link, please read them before you attempt to answer the questions.
Open a script and name it to section 6 assignment and try to tackle the below questions:
#### what is the most popular destination city from NewYork?
### which month is the busiest of the year?
#### which airline is the most punctual?
##### what destination is the longest duration
#### what airline is the worst in terms of delays
### which airline has the highest capacity of seats?
### which airplane model is the highest in use and from which manufacturer?
I have attached a script that has the answers to these questions but before you check the answers, please try to answer for these questions on your own :)
All the Best,
Haytham
Rescale Analytics
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80Assignment question 1 answerVideo lesson
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81Assignment question 2 & 3 answerVideo lesson
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82Assignment Question 4,5 and 6Video lesson
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83Assignment question 7Video lesson
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84SummaryVideo lesson

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