RA: Data Science and Supply Chain analytics. A-Z with Python course will introduce Data Science and supply chain analytics for the students. The course will teach students how to use linear Programming in python for logistics optimization and Production scheduling.

The course is a combination of data science and supply chain. It provides algorithms for supply chains and retailers to make aggregate and item controlled forecasting, maximise profit margin by optimising prices, optimise stocks, and plan assortment. The courses is usually available at INR 2,799 on Udemy but you can click now to get 80% off and get RA: Data Science and Supply Chain analytics. A-Z with Python for INR 449.

Who all can opt for this course?

  • Candidates who are absolute beginner at coding
  • Candidates who work in a supply-chain and want to make data-driven decisions, this course will equip them with what they need.
  • Candidates who are working as a inventory manager and want to optimize inventory for 1000000 products at once, then this course is for them.
  • Candidates who work in finance and want to forecast their budget by taking trends, seasonality, and other factors into account.
  • Candidates who are seasoned python user, can take this course to get up to speed quickly with python capabilities.
  • Candidates who want to apply machine learning techniques for supply -chain can take up this course

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 499 (INR 2,79980% off
Duration37 Hours
Student Enrollment5,577 students
InstructorHaytham Omar https://www.linkedin.com/in/haythamomar
Topics CoveredSupply chain fundamentals, Python proramming, Data visualization, and Statistical analysis
Course LevelBeginner
Total Student Reviews660

Learning Outcomes

  • How to use linear programming in python for logistics optimization and production scheduling
  • How to schedule production and optimise logistics and use Python’s linear programming language
  • For all of your business’s products, establish stock regulations and safety stocks
  • To increase service levels and cut expenses, segment customers, products, and suppliers
  • Learn simulations to make knowledgeable supply chain decisions
  • How to become a data scientist for supply chains
  • Discover supply chain strategies that are unique to this course

Course Content

S.No.Module (Duration)Topics
1.Introduction (01 hour 01 minutes)Intro
Why we need to learn coding?
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
2.Supply chain Data (38 minutes)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
3.Welcome to the world of Python (26 minutes)Python
downloading Anaconda
Installing Anaconda
Spyder overview
Jupiter Notebook overview
Python Libraries
Inventorize Package
4.Python Programming Fundamentals (01 hour 51 minutes)Intro
Arithmetic Calculations with Python
Importing data in Python
Subsetting Data Frames
Writing functions
for loops
for looping a function
Mapping On a data frame
for looping on a data frame
Assignment answer 1
Assignment answer 2
5.Supply chain statistical analysis (02 hours 24 minutes)Intro
Measures of centrality and Spread
Calculating the mean
Calculating the median
Measures of spread
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 Answer
Distributions in python
Testing for several distributions
Assignment answer
6.Manipulation and Data cleaning (02 hours 25 minutes)Manipulation Intro
Dropping Duplicates and NAs
Conversions lecture
Indexing tutorial
slicing index
Manipulation Lecture
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)
Assignment answer 1
Assignment answer 2
Assignment answer 3
Assignment answer 4
Assignment answer 5
7.Working with dates in Python (01 hour 16 minutes)Date Intro
Last purchase date and recency
recency Histogram
Modeling inter-arrival time
Modeling inter_arrival time 2
Modeling inter arrival time 3
rolling time series
rolling Time series 2
Assignment Answer
8.Visualization with matplotlib and seaborn (01 hour 14 minutes)Intro
Line plot
Line Plot part 2
scatter plot
Distribution Plots
Visualization Summary
Assignment answer 1
Assignment Answer 2
9.Segmentation (01 hour 58 minutes)Intro
Pareto Law
Importance of ABC analysis
Multi-criteria segmentation
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
Visualizing Krajic
Assignment ABC
Assignment answer
10.Forecasting Basics (01 hour 24 minutes)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
Regression in python
Regression in python part2
Initializing a date range for forecasting
Assignment Questions
11.Time-Series Modeling (02 hours 08 minutes)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 Explanation 1
assignment explanation 2
Assignment explanation 3
Assignment Explanation 4
12.Forecasting Segmentation (01 hour 05 minutes)Product Classfications
Demand Classification
Coefficient of Variation Squared
Preparing for Average Demand interval
Average Demand interval
Coerce Durations
Assignment Explanation
13.Supply chain simulations (02 hours 11 minutes)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
Getting the optimum number of servers
Multiple service lecture
Multiple service with queue computer
Mean waiting time of the system
Assignment Explanation
14.Linear Programing in python (02 hours 02 minutes)Optimization intro
Problem Formulation
Model in Excel
Installing Pulp
Model In Python
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 answer
Production planning
Production scheduling
Production scheduling in Python
Constraints Definition
Model Sensitivity
Production scheduling assignment
Assignment Explanation
15.Inventory (01 hour 30 minutes)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 part 2
Assignment Answer1
Assignment Answer 2
16.Inventory With uncertainty (01 hour 47 minutes)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
Assiignment Eplanation
17.Inventory Simulations (02 hours 06 minutes)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
S,Q policy in Python
Min Max Policy
Min Max simulation
Periodic Policy in Python
Hibrid Policy
Base Stock Policy
Comparing all policies
Inventory simulations assignment
Inventory simulation assignment
18.Seasonal Inventory (01 hour 26 minutes)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
Seasonal Inventory Summary
Assignment solution
Seasonal inventory answer
19.Consumer Behavior and pricing (01 hour 41 minutes)Introduction.
Pricing History.
Why Pricing is important?
Customers Perception of Price.
Pricing Mechanisms
Price response function
Price response function motivation in python
Simulating the Demand
Point of Maximum Profit
Assignment explanation
Elasticity Intro
Linear Elasticity with Inventorize
Parsing Dates
Getting list of unique Skus
For looping Linear Elasticity
Error Handling for linear elasticity
Single Optimization Summary
20.Logit price response function (36 minutes)Intro
Logistic Régression
Logistic modeling with inventorize
Comparison between logistic and linear
Logit For looping
Logit assignment
Logit Assignment answer
21.Multi Product Optimization (40 minutes)Introduction
Competing Products
Relation among Products
Multi-Variate regression in python
Multinomial Choice Models
Multinomial Logit Models
Multi Competing products in python
22.Markdowns (42 minutes)Intro
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
23.RFM analysis (46 minutes)RFM analysis
Customer Segmentation based on RFM.
Customer Recency in Python
Frequency and Monetary Value
Creating the categories
24.Machine Learning (03 hours 37 minutes)Intro
Decision Tree Demo
Kmeans in Python
Centroids Visualization
Elbow Spree
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
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
Plotting AUC
Preparing for Pipelines
Pipelines for four models
Grid For Logistic Regression
For looping Pipelines
Pipeline conclusion
Random forest and decision tree comparison
Randomized Search

Resources Required

  • Knowledge of Excel
  • Basic understanding of Anaconda and spyder programming platforms

Featured Review

Udemy User (5/5) : An excellent course for someone who wants to understand the applications of data science in supply chain. A comprehensive course which also teaches python from scratch! Highly recommended


  • Ramesh Natarajan (5/5) : This is the best course I have taken in the Udemy explaining the concepts, use cases with coding.
  • Jaume Cazorla Milla (5/5) : This could be the best value you could get in a Udemy course.
  • Aniket Dharwadkar (5/5) : You have beautifully explained SCM concepts and Python basics in this course.
  • Erwin Kerstens (4/5) : For Python-based data analysis, this course brings it all together nicely, with an impressive breadth of working examples.


  • Louis Chew Yi Jie (2.5/5) : pretty much no explanation, could be better if more elements of supply chain examples are integrated into the course
  • Wong Kok Hoong (1/5) : Poorly presented. Incoherent
  • Ashish C. (2/5) : The sound and video quality is very poor, which makes it difficult to stay focused.
  • Sakshi B. (2/5) : Lots of repetitions. Things are not well explained. The same things are being repeated multiple times. Besides, a lack of coherence and clarity.

About the Author

The instructor of this course is Haytham Omar who is a Consultant-Supply chain. With 4.3 Instructor Rating and 2,072 Reviews on Udemy, he/she offers 14 Courses and has taught 58,585 Students so far.

  • Instructor is a Developer, Consultant, and Trainer  in Business intelligence and supply chain management
  • Instructor is a founder of Dubai-based consultant, Rescale Analytics
  • Instructor is now leading data science and supply chain workshops and seminars, in addition to consulting projects for Sephora, Sharaf Group, and Aster Pharmacy
  • In the past three years, Haytham has led more than 70 supply chain and data science seminars throughout the UAE
  • Haytham is working with Kedge business school as part of the team CSIT for partnership
  • Haytham developed the Inventorize package in R mainly used for supply chain analytics with more than 50000 Downloads so far.

Comparison Table

ParametersRA: Data Science and Supply Chain analytics. A-Z with PythonPython for Machine Learning with Numpy, Pandas & MatplotlibDatabases with Python: MySQL, SQLite & MongoDB with Python
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration37 hours7 hours3 hours
Rating4.4 /54.3 /54.4 /5
Student Enrollments5,576104,63548,924
InstructorsHaytham OmarCode WarriorsSDE Arts | Octavo
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