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 Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 499 (INR 2,799) 80% off |

Duration | 37 Hours |

Rating | 4.4/5 |

Student Enrollment | 5,577 students |

Instructor | Haytham Omar https://www.linkedin.com/in/haythamomar |

Topics Covered | Supply chain fundamentals, Python proramming, Data visualization, and Statistical analysis |

Course Level | Beginner |

Total Student Reviews | 660 |

## 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? | ||

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 | ||

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 |

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 | ||

5. | Supply chain statistical analysis (02 hours 24 minutes) | 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 | ||

6. | Manipulation and Data cleaning (02 hours 25 minutes) | 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 | ||

7. | Working with dates in Python (01 hour 16 minutes) | 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 | ||

8. | Visualization with matplotlib and seaborn (01 hour 14 minutes) | 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 | ||

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 | ||

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 | ||

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 | ||

Assignment | ||

Regression in python | ||

Regression in python part2 | ||

Initializing a date range for forecasting | ||

Forecasting | ||

Summary | ||

Assignment Questions | ||

Assignment | ||

Assignment2 | ||

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. | ||

Assignment Explanation 1 | ||

assignment explanation 2 | ||

Assignment explanation 3 | ||

Assignment Explanation 4 | ||

12. | Forecasting Segmentation (01 hour 05 minutes) | 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 | ||

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 | ||

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 | ||

14. | Linear Programing in python (02 hours 02 minutes) | 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 | ||

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 | ||

Summary part 2 | ||

Assignment | ||

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 | ||

Summary | ||

Assignment | ||

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 | ||

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 | ||

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 | ||

Conclusion | ||

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 | ||

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 | ||

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 | ||

Summary | ||

22. | Markdowns (42 minutes) | 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 | ||

23. | RFM analysis (46 minutes) | RFM analysis |

Customer Segmentation based on RFM. | ||

Customer Recency in Python | ||

Frequency and Monetary Value | ||

Ranking | ||

Grouping | ||

Creating the categories | ||

Conclusion | ||

24. | Machine Learning (03 hours 37 minutes) | 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 |

## 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

## Pros

**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.

## Cons

**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

Parameters | RA: Data Science and Supply Chain analytics. A-Z with Python | Python for Machine Learning with Numpy, Pandas & Matplotlib | Databases with Python: MySQL, SQLite & MongoDB with Python |
---|---|---|---|

Offers | INR 455 (87% off | INR 455 (87% off | INR 455 (87% off |

Duration | 37 hours | 7 hours | 3 hours |

Rating | 4.4 /5 | 4.3 /5 | 4.4 /5 |

Student Enrollments | 5,576 | 104,635 | 48,924 |

Instructors | Haytham Omar | Code Warriors | SDE Arts | Octavo |

Register Here | Apply Now! | Apply Now! | Apply Now! |

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