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Data Science

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RA: Data Science and Supply Chain analytics. A-Z with Python

Learn Python, Supply Chain Data Science ,Linear Programming, Forecasting, Pricing and Inventory Management.

4.37 (184 reviews)

Students

37 hours

Content

Apr 2021

Last Update
Regular Price

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What you will learn

A-Z Guide to Mastering Python for Data Science.

Work as A demand Planner.

Become a data driven supply chain manager.

Use linear Programming in python for logistics optimization and Production scheduling.

Set stock policies and safety stocks for all of your Business products.

Revenue management

Segment Customers, Products and suppliers to maximize service levels and reduce costs.

Learn simulations to make informed supply chain decisions.

Become a supply chain data scientist.

Learn Supply chain techniques you will only find in this course. Guaranteed!


Description

“After our Data Science and supply chain analytics with R course being dubbed the highest rated course in supply chain on Udemy, we are pleased to Introduce Data Science and supply chain analytics. A-Z with Python !! “

" 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 data science 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. 20000 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 the 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 Python 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 a supply-chain and want to make data-driven decisions, this course will equip you with what you need.

· 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 python user, then take this course to get up to speed quickly with python capabilities. You will become a regular python 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 python 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 Python 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 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 supply chain use cases.

Supply chain Fundamentals Module includes:

1- Introduction to supply chain.

2- Supply chain Flows.

3- Data produced by supply chains.

Python Programming Fundamentals Module includes:

1- Basics of Python

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- Linear Programming and logistics optimizations.

6- Pricing and Markdowns optimization Techniques.

7- Inventory Policy and Safety stock Calculations.

8- Inventory simulations.

9- Machine Learning for supply-chain.

10- 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 Python. 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.



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 in 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 a simple way. I highly recommend this training.”

Djamel BOUREMIZ

Purchasing Manager at Mineral Circles Bearings



Screenshots

RA: Data Science and Supply Chain analytics. A-Z with Python
RA: Data Science and Supply Chain analytics. A-Z with Python
RA: Data Science and Supply Chain analytics. A-Z with Python
RA: Data Science and Supply Chain analytics. A-Z with Python

Content

Introduction

Intro

Why we need to learn coding?

Curriculum

Supply chain visualization

Cost and service Dynamics

Service level and product characteristics

Customer and supplier characteristics

Supply chain Views

The Financial flow

Why is supply chain complicated

Supply chain Data

intro

Types of Data in supply chain

Data From suppliers

Data from production

Data from stocks

Data from sales and customers

Why we need to learn Data Science

Analytics Types

Welcome to the world of Python

Python

downloading Anaconda

Installing Anaconda

Spyder overview

Jupiter Notebook overview

Python Libraries

Python Programming Fundamentals

Intro

Dataframes

Arithmetic Calculations with Python

Lists

Dictionaries

Arrays

Importing data in Python

Subsetting Data Frames

Conditions

Writing functions

mapping

for loops

for looping a function

Mapping On a data frame

for looping on a data frame

Summary

Assignment

Assignment answer 1

Assignment answer 2

Supply chain statistical analysis

Intro

Measures of centrality and Spread

Calculating the mean

Calculating the median

Measures of spread

Percentiles

Correlations: subsetting Cars dataset

Correlations of continuous variables

Correlation plots

Correlation thresholds

Detecting outliers

Outliers in python

linear regression

intro to linear regression

Linear Regression in python

Fitting the linear model

Importance of distributions in supply chain

Chi- Square tests

Distributions in Excel

Distributions Chi-square tests

cover for 90% of demand

Assignment

Assignment Answer

Distributions in python

Testing for several distributions

Summary

Assignment

Assignment answer

Manipulation and Data cleaning

Manipulation Intro

Dropping Duplicates and NAs

Conversions lecture

Conversions

Filterations

Imputations

Indexing tutorial

slicing index

Manipulation Lecture

Groupby

Slicing the group by

Dropping levels

The proper form

Pivot Tables

Aggregat function in pivot table

Melting the data

Left Join

inner and outer join

Joining in python

Inner, left join and full join (outer)

Summary

Assignment

Assignment answer 1

Assignment answer 2

Assignment answer 3

Assignment answer 4

Assignment answer 5

Working with dates in Python

Date 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

Visualization with matplotlib and seaborn

Intro

Line plot

Line Plot part 2

scatter plot

Countplot

Barplot

Distribution Plots

Boxplots

Histograms

Pairplots

Visualization Summary

Assignment

Assignment answer 1

Assignment Answer 2

Segmentation

Intro

Pareto Law

Importance of ABC analysis

Multi-criteria segmentation

Transforming the data for excel

ABC_analysis in Excel

Assignment

ABC in python

Multi-Criteria ABC analysis

Multi-Criteria ABC analysis with store or department level

Supplier segmentation 1

Supplier segmentation 2

Supplier Segmentation In python

Value_index

Visualizing Krajic

Summary

Assignment ABC

Assignment answer

Forecasting Basics

Why we need forecasts

Qualitative and Quantitative Forecasting

Optimistic and Pessimistic Forecasting

Time Components

Preparing the Data for Regression

Forecasting in Excel

Forecasting in excel 2

Assignment

Regression in python

Regression in python part2

Initializing a date range for forecasting

Forecasting

Summary

Assignment Questions

Assignment

Assignment2

Time-Series Modeling

Time Series Intro

Accuracy Measures

Preparing the data for time-series

Getting the time series components: Lecture

Getting the time series components

components uses

Arima Models

Stationarity test in python

Arima in python

ARIMA diagnostics

Grid search

For looping ARIMA

error handling

fitting the best model

Mean absolute error

Arima Comparison

Exponential smoothing

Exponential smoothing in python

Comparing exponential smoothing models

Time series summary

Assignment.

Assignment Explanation 1

assignment explanation 2

Assignment explanation 3

Assignment Explanation 4

Forecasting Segmentation

Product Classfications

Demand Classification

Holidays

Coefficient of Variation Squared

Preparing for Average Demand interval

Average Demand interval

Durations

Coerce Durations

Classifications

Conclusion

Summary

Assignment

Assignment Explanation

Supply chain simulations

Introduction

Waiting lines

Simulation Example Demo

Simulation Excel

Simulation Assignment

Simulating waiting time in Python

1000 simulations

Downloading R

Installing R

Installing Rpy2

Simulation with queue Computer

Multiple_resources

Getting the optimum number of servers

Capacity_constrains

Multiple service lecture

Multiple service with queue computer

Mean waiting time of the system

Summary

Assignment

Assignment Explanation

Linear Programing in python

Optimization intro

Problem Formulation

Model in Excel

Installing Pulp

Model In Python

Assignment

Assignment Explanation

Transport Problem in Excel Part 1

Transport Problem in in Pulp Part 1

Transport Optimization Part 2

Formulating supply constraint

Solving the model

Assignment

Assignment answer

Linking Constraints

DC Model Intro

DC Model 2

DC model 3

DC model solved

DC Model Conclusion

Setting up the DC model in Python

Setting inbound and outbound flow

Defining objective function

Supply Constraints

Inbound and outbound constraints

Demand Constraints

Model Conclusion

Production scheduling

Production scheduling in Python

Constraints Definition

Model Sensitivity

Summary

Production scheduling assignment

Assignment Explanation

Inventory

Inro

Why we need inventory?

Inventory Strategies

Inventory Types and EOQ

Total Logistics Cost and total relevant cost

Economic Order Quantity with Excel.

EOQ with discounts

EOQ Sensitivity

EOQ in Python

EOQ practical

EOQ with lead-time

EOQ with Lead-time in python

Summary

Summary part 2

Assignment

Assignment Answer1

Assignment Answer 2

Inventory With uncertainty

Into

Variability In supply chain

Demand Lead-time and Sigma Demand Lead-time

Calculating average daily demand

Method 1 for safety stock calculation

Method 2 for safety stock calculation

Preparing the Data for safety stock calculations

Calculating average and standard deviation Per SKU

Segmentation of data for service level

Reorder point for All Skas

Reorder Point Conclusion

leadtime variability

Lead time variability in Python

Summary

Assignment

Assiignment Eplanation

Inventory Simulations

Inventory Policies

Inventory Policies

Min Q Demonstration

Min Q Lecture

Min Q in Excel

Periodic Review Demonstration

Periodic Review Lecture

Periodic Review Demonstration in Excel

Min Max Demonstration

Min Max Lecture

Min Max example in excel

Base stock Demonstration

Base stock policy

Base stock Policy in excel

Assignment

S,Q policy in Python

Min Max Policy

Min Max simulation

Periodic Policy in Python

Hibrid Policy

Base Stock Policy

Comparing all policies

Summary

Inventory simulations assignment

Inventory simulation assignment

Seasonal Inventory

Intro

Seasonal Products

Point of Maximum Profit

How Much I will sell?

Data Table

Critical Ratio

Critical Ratio in Excel

What's actually happening?

Critical Ratio in python

Preparing the Data for MPN

Creating a Margin of error

Applying MPN on all data

Conclusion

Seasonal Inventory Summary

Assignment solution

Seasonal inventory answer

Consumer Behavior and pricing

Introduction.

Pricing History.

Why Pricing is important?

Customers Perception of Price.

Pricing Mechanisms

Commodoties

Price response function

Price response function motivation in python

Simulating the Demand

Point of Maximum Profit

Assignment

Assignment explanation

Elasticity Intro

Elasticity

Linear Elasticity with Inventorize

Parsing Dates

Getting list of unique Skus

For looping Linear Elasticity

Error Handling for linear elasticity

Conclusion

Single Optimization Summary

Assignment

Explanation

Logit price response function

Intro

Logistic Régression

Logistic modeling with inventorize

Comparison between logistic and linear

Logit For looping

Logit assignment

Logit Assignment answer

Multi Product Optimization

Introduction

Competing Products

Relation among Products

Multi-Variate regression in python

Multinomial Choice Models

Multinomial Logit Models

Multi Competing products in python

Summary

Markdowns

Intro

Markdowns

Why we do markdowns

Customers segment to markdowns

Problem formulation

Markdowns for multiple periods

Setting up solver

Salvage Value

Markdowns with forecasting.

Sensitivity analysis.

Markdowns for one period

Assignment

RFM analysis

RFM analysis

Customer Segmentation based on RFM.

Customer Recency in Python

Frequency and Monetary Value

Ranking

Grouping

Creating the categories

Conclusion

Machine Learning

Intro

Decision Tree Demo

Overfitting

Kmeans in Python

Centroids Visualization

Elbow Spree

Preparing the data for regression

Getting the time Components

one hot encoding

Training the models

KNN

KNN Grid Search

Lasso Grid Search

Regularization Importance in Lasso

Regularization Visualization

Classification Problem orientation

Exploring the banking data

Preparing the Data

Logistic Regression without Grid Search

Pre-Processing of Data

Grid Search

Confusion Matrix

AUC

Plotting AUC

Preparing for Pipelines

Pipelines for four models

Grid For Logistic Regression

Grids

For looping Pipelines

Verbose

Pipeline conclusion

Random forest and decision tree comparison

Randomized Search


Reviews

D
Dany18 June 2021

The course content and instructor is really good. This course is far far better than the courses available elsewhere.

E
Erwin4 May 2021

Impressive course. You do need an intermediate general knowledge of programming, statistics & supply chain topics. For Python-based data analysis, this course brings it all together nicely, with an impressive breadth of working examples. If you are looking for a solid theoretical base then look elsewhere. If you want to start applying data analysis to supply chain topics with Python, this is a very solid starting point. The use of a custom module speeds up the learning & video's, but it is a bit like a cooking course with ready-made dinners. Luckily you can easily extract the code from the module installer.

E
Eric28 April 2021

The most practical course by far which demonstrates how data science can be used in the supply chain arena. Very excited I found this course. I can apply the knowledge directly in my job. I strongly recommend to anyone out there who wants to make a huge impact in their supply chain roles. Using python, especially the inventorize library is a life saver to tacking most of the pressing issues without having to grapple with spreadsheets. Now I also understand that learning to code without direct application makes it less interesting.. And instructor is top notch on supply chain topics and python... I wish I could give 10 stars because 5 is not enough for this expertise.. Thank you sir !

S
SandeepModaliar14 April 2021

Excellent Course. Only course on udemy covering all SC concepts & strategies practically and programatically. Superb

S
Swapnil11 April 2021

yes sure, it was great uptil now. by the language of the instructor it is clear that the knowledge that he has in the field of supply chain is immense as well as practical

R
Ramesh1 March 2021

I have a masters degrees in Operations Research and Data Science. I work in the Global supply-chain analytics area for a global fortune company. This is the best course I have taken in the Udemy explaining the concepts, use cases with coding. I wish I could overwrite the ranking with a 10 stars. Great work Haitham Omar. Thanks for putting this together. Hats off to you.

J
JK8 February 2021

As a chemical process industry researcher, this is exactly what I need for a skill set. I planned to generate the supply chain model for fluctuation of productions from the plant (e.g. hydrogen production change according to renewable solar/wind energy) to supply hydrogen to the refinery, ammonia, hydrogen car wherever the field needs the hydrogen most according to demand. Thank you for providing a great course linking supply chain-data science.

R
Raghav27 January 2021

Mr. Haytham's teaching is splendid...He never hoards his knowledge and attempts to deliver as much as possible within the time constraints. I am his loyal student. Registered to all his courses. The conceptual/practical mix is innovative and fulfilling.

S
Sai24 January 2021

The course covers all the major topics in SCM analytics. Easy to follow & is a good starting point for some concepts which would require further indepth study.

n
nandita6 January 2021

Learning supply chain and analytics using coding is wonderful. Looking forward for more classes and learning in depth.

O
Obahi12 November 2020

I love every information I have learned thus as far, it's invaluable. The Subtitle doesn`t correspond in any way to what you saying.

J
Jaume29 October 2020

This course was excellent. Haytham is a great teacher that make the most complex process easy to understand. This could be the best value you could get in a Udemy course. The complete course on Python with 37 hours of videos is total catch! From beginners (like me) to advance level this course is highly recommended!

A
Aniket25 October 2020

Thanks Haytham for this wonderful course of Supply Chain Analytics with Python. You have beautifully explained SCM concepts and Python basics in this course. Appreciate your hard work taken to develop this course.

A
Asli8 October 2020

The course surpassed my expectations! Very thorough and comprehensive materials for those who want to indulge themselves with Python! Even if you are newbie, the instructor Haytham ensures that you learn each and every single point in detail. Highly recommended who wish to up-skill themselves during these tough times.

M
Mohamed7 September 2020

The instructor is knowledgeable about the processes of supply chain and deliver informative lectures which helps me to tackle my daily challenges in supply chain using the power of data analytics and data science.


3391970

Udemy ID

8/4/2020

Course created date

9/15/2020

Course Indexed date
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