Mô tả

"I attended this course with high expectations. And I was not disappointed. It´s incredible to see what is possible with Python in terms of supply chain planning and optimization. Haytham is doing a great job as a trainer. Starting with explanation of basics and ending with presentation of advanced techniques supply chain managers can apply in real life."

Larsen Block

Director Supply Chain Management at Freudenberg Home and Cleaning Solutions GmbH


New update : Forecast for OTB calculation with AutoML is added (Aug 2023)

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 !!

" 60000 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 70000 professionals in supply chain and data science with R and python, and finally opening my own business in consulting for five 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


Bạn sẽ học được gì

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!

Yêu cầu

  • Excel
  • Anaconda
  • spyder

Nội dung khoá học

25 sections

Introduction

11 lectures
Intro
01:39
Why we need to learn coding?
03:57
Curriculum
03:37
Plan of attack
02:09
Supply chain visualization
07:45
Cost and service Dynamics
04:57
Service level and product characteristics
06:41
Customer and supplier characteristics
09:12
Supply chain Views
11:48
The Financial flow
02:59
Why is supply chain complicated
06:38

Supply chain Data

8 lectures
intro
01:28
Types of Data in supply chain
13:46
Data From suppliers
02:56
Data from production
01:36
Data from stocks
01:49
Data from sales and customers
06:49
Why we need to learn Data Science
05:08
Analytics Types
04:50

Welcome to the world of Python

7 lectures
Python
02:36
downloading Anaconda
01:34
Installing Anaconda
01:03
Spyder overview
11:38
Jupiter Notebook overview
02:53
Python Libraries
05:30
Inventorize Package
01:05

Python Programming Fundamentals

19 lectures
Intro
07:24
Dataframes
02:33
Arithmetic Calculations with Python
08:30
Lists
06:34
Dictionaries
05:01
Arrays
03:14
Importing data in Python
07:57
Subsetting Data Frames
08:40
Conditions
04:28
Writing functions
08:00
mapping
02:14
for loops
02:16
for looping a function
04:14
Mapping On a data frame
05:41
for looping on a data frame
08:17
Summary
08:33
Assignment
02:10
Assignment answer 1
05:44
Assignment answer 2
09:58

Supply chain statistical analysis

28 lectures
Intro
03:10
Measures of centrality and Spread
06:23
Calculating the mean
04:36
Calculating the median
01:31
Measures of spread
04:37
Percentiles
02:21
Correlations: subsetting Cars dataset
05:35
Correlations of continuous variables
08:47
Correlation plots
04:59
Correlation thresholds
02:10
Detecting outliers
04:18
Outliers in python
06:48
linear regression
04:34
intro to linear regression
08:38
Linear Regression in python
02:55
Fitting the linear model
06:50
Importance of distributions in supply chain
12:18
Chi- Square tests
02:54
Distributions in Excel
08:21
Distributions Chi-square tests
06:55
cover for 90% of demand
04:49
Assignment
01:02
Assignment Answer
07:38
Distributions in python
05:21
Testing for several distributions
09:11
Summary
01:20
Assignment
00:44
Assignment answer
06:06

Manipulation and Data cleaning

27 lectures
Manipulation Intro
02:22
Dropping Duplicates and NAs
06:46
Conversions lecture
04:29
Conversions
05:11
Filterations
07:49
Imputations
04:49
Indexing tutorial
04:30
slicing index
03:47
Manipulation Lecture
05:07
Groupby
07:15
Slicing the group by
04:34
Dropping levels
02:15
The proper form
03:46
Pivot Tables
07:45
Aggregate function in pivot table
05:30
Melting the data
05:34
Left Join
04:20
inner and outer join
03:41
Joining in python
04:27
Inner, left join and full join (outer)
06:20
Summary
01:52
Assignment
02:28
Assignment answer 1
05:53
Assignment answer 2
05:52
Assignment answer 3
07:43
Assignment answer 4
07:20
Assignment answer 5
14:06

Working with dates in Python

13 lectures
Date Intro
06:33
datetime
08:36
Last purchase date and recency
09:27
recency Histogram
02:02
Modeling inter-arrival time
07:30
Modeling inter_arrival time 2
05:41
Modeling inter arrival time 3
04:35
Resampling
09:56
rolling time series
02:37
rolling Time series 2
03:04
Summary
04:38
Assignment
01:48
Assignment Answer
10:20

Visualization with matplotlib and seaborn

14 lectures
Intro
03:35
Line plot
04:50
Line Plot part 2
06:52
scatter plot
08:20
Countplot
05:03
Barplot
13:00
Distribution Plots
06:17
Boxplots
05:40
Histograms
02:58
Pairplots
06:40
Visualization Summary
01:19
Assignment
03:04
Assignment answer 1
02:06
Assignment Answer 2
04:54

Segmentation

18 lectures
Intro
06:25
Pareto Law
06:31
Importance of ABC analysis
05:44
Multi-criteria segmentation
05:21
Transforming the data for excel
11:54
ABC_analysis in Excel
09:35
Assignment
01:43
ABC in python
04:40
Multi-Criteria ABC analysis
05:01
Multi-Criteria ABC analysis with store or department level
12:19
Supplier segmentation 1
06:51
Supplier segmentation 2
05:23
Supplier Segmentation In python
06:35
Value_index
01:59
Visualizing Krajic
09:03
Summary
06:34
Assignment ABC
01:18
Assignment answer
12:02

Forecasting Basics

16 lectures
Why we need forecasts
04:16
Qualitative and Quantitative Forecasting
04:25
Optimistic and Pessimistic Forecasting
04:12
Time Components
05:10
Preparing the Data for Regression
04:56
Forecasting in Excel
12:08
Forecasting in excel 2
10:10
Assignment
01:36
Regression in python
09:07
Regression in python part2
06:06
Initializing a date range for forecasting
05:00
Forecasting
08:24
Summary
01:51
Assignment Questions
01:01
Assignment
05:16
Assignment2
01:00

Time-Series Modeling

25 lectures
Time Series Intro
06:20
Accuracy Measures
02:29
Preparing the data for time-series
07:16
Getting the time series components: Lecture
03:10
Getting the time series components
01:08
components uses
04:31
Arima Models
08:45
Stationarity test in python
06:32
Arima in python
11:31
ARIMA diagnostics
02:02
Grid search
06:09
For looping ARIMA
10:28
error handling
07:10
fitting the best model
03:32
Mean absolute error
04:00
Arima Comparison
04:02
Exponential smoothing
03:40
Exponential smoothing in python
08:07
Comparing exponential smoothing models
07:07
Time series summary
03:08
Assignment.
03:03
Assignment Explanation 1
04:11
assignment explanation 2
04:24
Assignment explanation 3
03:23
Assignment Explanation 4
02:25

Forecasting Segmentation

13 lectures
Product Classfications
05:04
Demand Classification
05:45
Holidays
04:28
Coefficient of Variation Squared
07:03
Preparing for Average Demand interval
05:13
Average Demand interval
04:58
Durations
02:46
Coerce Durations
03:25
Classifications
08:38
Conclusion
06:18
Summary
02:59
Assignment
01:48
Assignment Explanation
07:02

Supply chain simulations

20 lectures
Introduction
06:17
Waiting lines
06:40
Simulation Example Demo
01:12
Simulation Excel
09:04
Simulation Assignment
01:02
Simulating waiting time in Python
10:07
1000 simulations
05:12
Downloading R
02:46
Installing R
03:46
Installing Rpy2
03:41
Simulation with queue Computer
10:20
Multiple_resources
10:32
Getting the optimum number of servers
05:43
Capacity_constrains
10:39
Multiple service lecture
04:32
Multiple service with queue computer
09:02
Mean waiting time of the system
16:29
Summary
02:24
Assignment
02:30
Assignment Explanation
09:06

Linear Programing in python

22 lectures
Optimization intro
06:42
Problem Formulation
09:21
Model in Excel
09:34
Installing Pulp
01:29
Model In Python
10:03
Assignment
01:10
Assignment Explanation
03:01
Transport Problem in Excel Part 1
10:18
Transport Problem in in Pulp Part 1
08:06
Transport Optimization Part 2
07:32
Formulating supply constraint
05:16
Solving the model
06:30
Assignment
01:25
Assignment answer
03:31
Production planning
04:06
Production scheduling
08:13
Production scheduling in Python
05:38
Constraints Definition
07:57
Model Sensitivity
03:28
Summary
01:26
Production scheduling assignment
01:28
Assignment Explanation
06:03

Inventory

17 lectures
Inro
05:17
Why we need inventory?
09:40
Inventory Strategies
07:23
Inventory Types and EOQ
08:45
Total Logistics Cost and total relevant cost
06:09
Economic Order Quantity with Excel.
04:02
EOQ with discounts
05:11
EOQ Sensitivity
08:03
EOQ in Python
07:55
EOQ practical
03:37
EOQ with lead-time
04:48
EOQ with Lead-time in python
07:13
Summary
02:25
Summary part 2
02:05
Assignment
01:52
Assignment Answer1
02:33
Assignment Answer 2
03:22

Inventory With uncertainty

16 lectures
Intro
07:45
Variability In supply chain
04:23
Demand Lead-time and Sigma Demand Lead-time
03:53
Calculating average daily demand
04:39
Method 1 for safety stock calculation
08:40
Method 2 for safety stock calculation
08:20
Preparing the Data for safety stock calculations
06:29
Calculating average and standard deviation Per SKU
08:26
Segmentation of data for service level
07:47
Reorder point for All Skus
15:01
Reorder Point Conclusion
08:40
leadtime variability
05:58
Lead time variability in Python
01:17
Summary
04:35
Assignment
02:14
Assiignment Eplanation
09:27

Inventory Simulations

25 lectures
Inventory Policies
02:24
Inventory Policies
08:38
Min Q Demonstration
04:42
Min Q Lecture
03:17
Min Q in Excel
05:50
Periodic Review Demonstration
07:35
Periodic Review Lecture
04:28
Periodic Review Demonstration in Excel
04:33
Min Max Demonstration
03:43
Min Max Lecture
04:22
Min Max example in excel
04:47
Base stock Demonstration
04:14
Base stock policy
04:01
Base stock Policy in excel
04:11
Assignment
02:03
S,Q policy in Python
12:34
Min Max Policy
05:09
Min Max simulation
02:26
Periodic Policy in Python
04:53
Hibrid Policy
09:10
Base Stock Policy
06:06
Comparing all policies
06:54
Summary
03:41
Inventory simulations assignment
00:59
Inventory simulation assignment
05:46

Seasonal Inventory

16 lectures
Intro
05:10
Seasonal Products
08:38
Point of Maximum Profit
07:13
How Much I will sell?
01:23
Data Table
05:26
Critical Ratio
04:48
Critical Ratio in Excel
09:11
What's actually happening?
01:47
Critical Ratio in python
09:14
Preparing the Data for MPN
08:16
Creating a Margin of error
04:30
Applying MPN on all data
08:43
Conclusion
03:39
Seasonal Inventory Summary
02:52
Assignment solution
01:10
Seasonal inventory answer
04:25

Consumer Behavior and pricing

23 lectures
Introduction.
03:28
Pricing History.
05:16
Why Pricing is important?
04:38
Customers Perception of Price.
05:35
Pricing Mechanisms
04:04
Commodoties
02:02
Price response function
04:56
Price response function motivation in python
03:40
Simulating the Demand
05:55
Point of Maximum Profit
07:06
Assignment
01:15
Assignment explanation
05:18
Elasticity Intro
04:39
Elasticity
07:35
Linear Elasticity with Inventorize
03:15
Parsing Dates
03:52
Getting list of unique Skus
05:10
For looping Linear Elasticity
06:35
Error Handling for linear elasticity
05:11
Conclusion
04:45
Single Optimization Summary
03:21
Assignment
01:05
Explanation
02:43

Logit price response function

7 lectures
Intro
05:14
Logistic Régression
06:45
Logistic modeling with inventorize
06:16
Comparison between logistic and linear
05:36
Logit For looping
05:31
Logit assignment
01:23
Logit Assignment answer
05:19

Multi Product Optimization

8 lectures
Introduction
05:37
Competing Products
04:15
Relation among Products
02:58
Multi-Variate regression in python
11:04
Multinomial Choice Models
05:17
Multinomial Logit Models
05:01
Multi Competing products in python
04:50
Summary
01:51

Markdowns

12 lectures
Intro
05:08
Markdowns
05:20
Why we do markdowns
04:52
Customers segment to markdowns
02:57
Problem formulation
04:01
Markdowns for multiple periods
07:15
Setting up solver
02:17
Salvage Value
01:17
Markdowns with forecasting.
02:54
Sensitivity analysis.
03:42
Markdowns for one period
01:35
Assignment
01:07

RFM analysis

8 lectures
RFM analysis
04:27
Customer Segmentation based on RFM.
03:51
Customer Recency in Python
08:14
Frequency and Monetary Value
06:36
Ranking
06:35
Grouping
08:01
Creating the categories
03:04
Conclusion
05:23

Machine Learning

33 lectures
Intro
04:50
Decision Tree Demo
08:41
Overfitting
03:27
Kmeans in Python
07:01
Centroids Visualization
03:29
Elbow Spree
06:59
Preparing the data for regression
07:00
Getting the time Components
09:12
one hot encoding
09:50
Training the models
08:07
KNN
04:03
KNN Grid Search
10:33
Lasso Grid Search
08:23
Regularization Importance in Lasso
07:37
Regularization Visualization
02:47
Classification Problem orientation
02:12
Exploring the banking data
11:55
Preparing the Data
10:38
Logistic Regression without Grid Search
05:39
Pre-Processing of Data
06:07
Grid Search
11:14
Confusion Matrix
08:39
AUC
08:46
Plotting AUC
02:43
Preparing for Pipelines
06:37
Pipelines for four models
06:03
Grid For Logistic Regression
05:00
Grids
04:39
For looping Pipelines
08:00
Verbose
02:51
Pipeline conclusion
01:26
Random forest and decision tree comparison
06:23
Randomized Search
06:59

Forecasting-OTB-AutoML Problem- Advanced manipulation with Pandas and Pycaret

18 lectures
Orientation to usecase
05:14
Data Description
05:36
Importing Data
07:37
Transforming the data to monthly
08:26
Getting seasonality feature
09:36
Generating Coherent Time-series
11:50
Getting back old data
09:58
Groupby & Transform
08:49
Trend and Seasonality
05:00
Generating time-series features 1
05:12
Generating Time-series features 2
07:59
Imputing all columns at once
08:16
Splitting the history
10:51
Running the experiment and comparing models
07:21
Creating models and tuning
07:09
blending & Stacking
08:24
Prediction
08:00
Visualization and Conclusion
08:33

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