4.37 (184 reviews)
☑ 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!
“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.
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!
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."
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”
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.”
Purchasing Manager at Mineral Circles Bearings
Why we need to learn coding?
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
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
Welcome to the world of Python
Jupiter Notebook overview
Python Programming Fundamentals
Arithmetic Calculations with Python
Importing data in Python
Subsetting Data Frames
for looping a function
Mapping On a data frame
for looping on a data frame
Assignment answer 1
Assignment answer 2
Supply chain statistical analysis
Measures of centrality and Spread
Calculating the mean
Calculating the median
Measures of spread
Correlations: subsetting Cars dataset
Correlations of continuous variables
Outliers in python
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
Distributions in python
Testing for several distributions
Manipulation and Data cleaning
Dropping Duplicates and NAs
Slicing the group by
The proper form
Aggregat function in pivot table
Melting the data
inner and outer join
Joining in python
Inner, left join and full join (outer)
Assignment answer 1
Assignment answer 2
Assignment answer 3
Assignment answer 4
Assignment answer 5
Working with dates in Python
Last purchase date and recency
Modeling inter-arrival time
Modeling inter_arrival time 2
Modeling inter arrival time 3
rolling time series
rolling Time series 2
Visualization with matplotlib and seaborn
Line Plot part 2
Assignment answer 1
Assignment Answer 2
Importance of ABC analysis
Transforming the data for excel
ABC_analysis in Excel
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
Why we need forecasts
Qualitative and Quantitative Forecasting
Optimistic and Pessimistic Forecasting
Preparing the Data for Regression
Forecasting in Excel
Forecasting in excel 2
Regression in python
Regression in python part2
Initializing a date range for forecasting
Time Series Intro
Preparing the data for time-series
Getting the time series components: Lecture
Getting the time series components
Stationarity test in python
Arima in python
For looping ARIMA
fitting the best model
Mean absolute error
Exponential smoothing in python
Comparing exponential smoothing models
Time series summary
Assignment Explanation 1
assignment explanation 2
Assignment explanation 3
Assignment Explanation 4
Coefficient of Variation Squared
Preparing for Average Demand interval
Average Demand interval
Supply chain simulations
Simulation Example Demo
Simulating waiting time in Python
Simulation with queue Computer
Getting the optimum number of servers
Multiple service lecture
Multiple service with queue computer
Mean waiting time of the system
Linear Programing in python
Model in Excel
Model In Python
Transport Problem in Excel Part 1
Transport Problem in in Pulp Part 1
Transport Optimization Part 2
Formulating supply constraint
Solving the model
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
Inbound and outbound constraints
Production scheduling in Python
Production scheduling assignment
Why we need inventory?
Inventory Types and EOQ
Total Logistics Cost and total relevant cost
Economic Order Quantity with Excel.
EOQ with discounts
EOQ in Python
EOQ with lead-time
EOQ with Lead-time in python
Summary part 2
Assignment Answer 2
Inventory With uncertainty
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
Lead time variability in Python
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
S,Q policy in Python
Min Max Policy
Min Max simulation
Periodic Policy in Python
Base Stock Policy
Comparing all policies
Inventory simulations assignment
Inventory simulation assignment
Point of Maximum Profit
How Much I will sell?
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
Seasonal Inventory Summary
Seasonal inventory answer
Consumer Behavior and pricing
Why Pricing is important?
Customers Perception of Price.
Price response function
Price response function motivation in python
Simulating the Demand
Point of Maximum Profit
Linear Elasticity with Inventorize
Getting list of unique Skus
For looping Linear Elasticity
Error Handling for linear elasticity
Single Optimization Summary
Logit price response function
Logistic modeling with inventorize
Comparison between logistic and linear
Logit For looping
Logit Assignment answer
Multi Product Optimization
Relation among Products
Multi-Variate regression in python
Multinomial Choice Models
Multinomial Logit Models
Multi Competing products in python
Why we do markdowns
Customers segment to markdowns
Markdowns for multiple periods
Setting up solver
Markdowns with forecasting.
Markdowns for one period
Customer Segmentation based on RFM.
Customer Recency in Python
Frequency and Monetary Value
Creating the categories
Decision Tree Demo
Kmeans in Python
Preparing the data for regression
Getting the time Components
one hot encoding
Training the models
KNN Grid Search
Lasso Grid Search
Regularization Importance in Lasso
Classification Problem orientation
Exploring the banking data
Preparing the Data
Logistic Regression without Grid Search
Pre-Processing of Data
Preparing for Pipelines
Pipelines for four models
Grid For Logistic Regression
For looping Pipelines
Random forest and decision tree comparison
The course content and instructor is really good. This course is far far better than the courses available elsewhere.
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.
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 !
Excellent Course. Only course on udemy covering all SC concepts & strategies practically and programatically. Superb
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
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.
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.
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.
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.
Learning supply chain and analytics using coding is wonderful. Looking forward for more classes and learning in depth.
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.
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!
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.
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.
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.