RA: Data Science and Supply Chain analytics. A-Z with Python
Learn Python, Supply Chain Data Science ,Linear Programming, Forecasting, Pricing and Inventory Management.
Lo que serás capaz de hacer
"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
- 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
Temario
UNIDAD 1 - Introduction · 11 leccionesUNIDAD 1 - Introduction
UNIDAD 1 - Introduction
- Intro
- Why we need to learn coding?
- Curriculum
- Plan of attack
- 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
UNIDAD 2 - Supply chain Data · 8 leccionesUNIDAD 2 - Supply chain Data
UNIDAD 2 - 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
UNIDAD 3 - Welcome to the world of Python · 7 leccionesUNIDAD 3 - Welcome to the world of Python
UNIDAD 3 - Welcome to the world of Python
- Python
- downloading Anaconda
- Installing Anaconda
- Spyder overview
- Jupiter Notebook overview
- Python Libraries
- Inventorize Package
UNIDAD 4 - Python Programming Fundamentals · 19 leccionesUNIDAD 4 - Python Programming Fundamentals
UNIDAD 4 - 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
UNIDAD 5 - Supply chain statistical analysis · 28 leccionesUNIDAD 5 - Supply chain statistical analysis
UNIDAD 5 - 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
UNIDAD 6 - Manipulation and Data cleaning · 27 leccionesUNIDAD 6 - Manipulation and Data cleaning
UNIDAD 6 - 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
- Aggregate 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
UNIDAD 7 - Working with dates in Python · 13 leccionesUNIDAD 7 - Working with dates in Python
UNIDAD 7 - 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
UNIDAD 8 - Visualization with matplotlib and seaborn · 14 leccionesUNIDAD 8 - Visualization with matplotlib and seaborn
UNIDAD 8 - 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
UNIDAD 9 - Segmentation · 18 leccionesUNIDAD 9 - Segmentation
UNIDAD 9 - 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
UNIDAD 10 - Forecasting Basics · 16 leccionesUNIDAD 10 - Forecasting Basics
UNIDAD 10 - 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
UNIDAD 11 - Time-Series Modeling · 25 leccionesUNIDAD 11 - Time-Series Modeling
UNIDAD 11 - 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
UNIDAD 12 - Forecasting Segmentation · 13 leccionesUNIDAD 12 - Forecasting Segmentation
UNIDAD 12 - 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
UNIDAD 13 - Supply chain simulations · 21 leccionesUNIDAD 13 - Supply chain simulations
UNIDAD 13 - Supply chain simulations
- Introduction
- Important update to this section
- 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
UNIDAD 14 - Linear Programing in python · 22 leccionesUNIDAD 14 - Linear Programing in python
UNIDAD 14 - 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
- Production planning
- Production scheduling
- Production scheduling in Python
- Constraints Definition
- Model Sensitivity
- Summary
- Production scheduling assignment
- Assignment Explanation
UNIDAD 15 - Inventory · 17 leccionesUNIDAD 15 - Inventory
UNIDAD 15 - 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
UNIDAD 16 - Inventory With uncertainty · 16 leccionesUNIDAD 16 - Inventory With uncertainty
UNIDAD 16 - Inventory With uncertainty
- Intro
- 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 Skus
- Reorder Point Conclusion
- leadtime variability
- Lead time variability in Python
- Summary
- Assignment
- Assiignment Eplanation
UNIDAD 17 - Inventory Simulations · 25 leccionesUNIDAD 17 - Inventory Simulations
UNIDAD 17 - 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
UNIDAD 18 - Seasonal Inventory · 16 leccionesUNIDAD 18 - Seasonal Inventory
UNIDAD 18 - 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
UNIDAD 19 - Consumer Behavior and pricing · 23 leccionesUNIDAD 19 - Consumer Behavior and pricing
UNIDAD 19 - 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
UNIDAD 20 - Logit price response function · 7 leccionesUNIDAD 20 - Logit price response function
UNIDAD 20 - Logit price response function
- Intro
- Logistic Régression
- Logistic modeling with inventorize
- Comparison between logistic and linear
- Logit For looping
- Logit assignment
- Logit Assignment answer
UNIDAD 21 - Multi Product Optimization · 8 leccionesUNIDAD 21 - Multi Product Optimization
UNIDAD 21 - 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
UNIDAD 22 - Markdowns · 12 leccionesUNIDAD 22 - Markdowns
UNIDAD 22 - 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
UNIDAD 23 - RFM analysis · 8 leccionesUNIDAD 23 - RFM analysis
UNIDAD 23 - RFM analysis
- RFM analysis
- Customer Segmentation based on RFM.
- Customer Recency in Python
- Frequency and Monetary Value
- Ranking
- Grouping
- Creating the categories
- Conclusion
UNIDAD 24 - Machine Learning · 33 leccionesUNIDAD 24 - Machine Learning
UNIDAD 24 - 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
UNIDAD 25 - Forecasting-OTB-AutoML Problem- Advanced manipulation with Pandas and Pycaret · 18 leccionesUNIDAD 25 - Forecasting-OTB-AutoML Problem- Advanced manipulation with Pandas and Pycaret
UNIDAD 25 - Forecasting-OTB-AutoML Problem- Advanced manipulation with Pandas and Pycaret
- Orientation to usecase
- Data Description
- Importing Data
- Transforming the data to monthly
- Getting seasonality feature
- Generating Coherent Time-series
- Getting back old data
- Groupby & Transform
- Trend and Seasonality
- Generating time-series features 1
- Generating Time-series features 2
- Imputing all columns at once
- Splitting the history
- Running the experiment and comparing models
- Creating models and tuning
- blending & Stacking
- Prediction
- Visualization and Conclusion
UNIDAD 26 - Keip · 2 leccionesUNIDAD 26 - Keip
UNIDAD 26 - Keip
- What is Keip ?
- Pitchdeck
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
26 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
Cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Haytham Omar-Ph.D.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 26 unidades · 427 lecciones · 60 h
- 12 meses de acceso
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Haytham Omar-Ph.D; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.


