Using Python for Data Science and Supply Chain Analytics

Learn Python, Supply Chain Data Science, Linear Programming, Forecasting, Pricing and Inventory Management.

Haytham Omar

Data Science | core | 37 hours | Published: Aug 2021

Discussions

Overview

1.8KSTUDENTS^*

97.1%RECOMMEND^*

This course includes:

37 hours of on-demand video
24 modules
Core level
Direct access/chat with the instructor
100% self-paced online
Many downloadable resources
Shareable certificate of completion

This 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.

Skills You Will Gain

Linear Programming

Pricing and Inventory Management

Python

Supply chain

Learning Outcomes (At the end of this program you will be able to)

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.

Prerequisites

Basic knowledge of excel.

Who Should Attend

 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.

Curriculum

1Welcome to the course!

About this course: Overview, Learning Outcomes, Who Should Enroll...

2Module 1: Course Introduction

Segment - 01 - Introduction

Segment - 02 - Why we Need to Learn Coding

Segment - 03 - Curriculum

Segment - 04 - Plan of Attack

Segment - 05 - Supply Chain Visualization

Segment - 06 - Cost and Service Dynamics

Segment - 07 - Service Level and Product Characteristics

Segment - 08 - Customer and Supplier Characteristics

Segment - 09 - Supply Chain Views

Segment - 10 - The Financial Flow

Segment - 11 - Why is Supply Chain Complicated

3Module 2: Supply Chain Data

Segment - 12 - Introduction

Segment - 13 - Types Of Data in Supply Chain

Segment - 14 - Data From Suppliers

Segment - 15 - Data From Production

Segment - 16 - Data From Stocks

Segment - 17 - Data From Sales and Customers

Segment - 18 - Why we Need to Learn Data Science

Segment - 19 - Analytics Types

4Module 3: Welcome to World of Python

Segment - 20 - Python

Segment - 21 - Downloading Anaconda

Segment - 22 - Installing Anaconda

Segment - 23 - Spyder Overview

Segment - 24 - Jupiter Notebook Overview

Segment - 25 - Python Libraries

Segment - 26 - Inventory Package

5Module 4: Python Programming Fundamentals

Segment - 27 - Introduction

Segment - 28 - Dataframes

Segment - 29 - Arithmetic Calculations with Python

Segment - 30 - Lists

Segment - 31 - Dictionaries

Segment - 32 - Arrays

Segment - 33 - Importing Data in Python

Segment - 34 - Subsetting Data Frames

Segment - 35 - Conditions

Segment - 36 - Writing functions

Segment - 37 - Mapping

Segment - 38 - For Loops

Segment - 39 - For Looping a Function

Segment - 40 - Mapping on a Data Frame

Segment - 41 - For Looping on a Data Frame

Segment - 42 - Summary

Segment - 43 - Assignment

Segment - 44 - Assignment Answer 1

Segment - 45 - Assignment Answer 2

6Module 5: Supply Chain Statistical Analysis

Segment - 46 - Introduction

Segment - 47 - Measures of Centrality and Spread

Segment - 48 - Calculating the Mean

Segment - 49 - Calculating the Median

Segment - 50 - Measures of Spread

Segment - 51 - Percentiles

Segment - 52 - Correlations: Subsetting Cars Dataset

Segment - 53 - Correlations of Continuous Variables

Segment - 54 - Correlation Plots

Segment - 55 - Correlation Thresholds

Segment - 56 - Detecting Outliers

Segment - 57 - Outliers in Python

Segment - 58 - Linear Regression

Segment - 59 - Introduction to Linear Regression

Segment - 60 - Linear Regression in Python

Segment - 61 - Fitting the Linear Model

Segment - 62 - Importance of Distributions in Supply Chain

Segment - 63 - Chi- Square Tests

Segment - 64 - Distributions in Excel

Segment - 65 - Distributions Chi-Square Tests

Segment - 66 - Cover for 90% of Demand

Segment - 67 - Assignment

Segment - 68 - Assignment Answer

Segment - 69 - Distributions in Python

Segment - 70 - Testing for Several Distributions

Segment - 71 - Summary

Segment - 72 - Assignment

Segment - 73 - Assignment Answer

7Module 6: Manipulation and Data Cleaning

Segment - 74 - Manipulation Introduction

Segment - 75 - Dropping Duplicates and NAs

Segment - 77 - Conversions

Segment - 76 - Conversions Lecture

Segment - 78 - Filtration

Segment - 79 - Imputations

Segment - 80 - Indexing Tutorial

Segment - 81 - Slicing Index

Segment - 82 - Manipulation Lecture

Segment - 83 - Groupby

Segment - 84 - Slicing the Groupby

Segment - 85 - Dropping Levels

Segment - 86 - The Proper Form

Segment - 87 - Pivot Tables

Segment - 88 - Aggregate Function in Pivot Table

Segment - 89 - Melting the Data

Segment - 90 - Left Join

Segment - 91 - Inner and Outer Join

Segment - 92 - Joining in Python

Segment - 93 - Inner, Left Join and Full Join (outer)

Segment - 94 - Summary

Segment - 95 - Assignment

Segment - 96 - Assignment Answer 1

Segment - 97 - Assignment Answer 2

Segment - 98 - Assignment Answer 3

Segment - 99 - Assignment Answer 4

Segment - 100 - Assignment Answer 5

8Module 7: Working with Dates with Python

Segment - 101 - Date Introduction

Segment - 102 - Datetime

Segment - 103 - Last Purchase Date and Recency

Segment - 104 - Recency Histogram

Segment - 105 - Modeling Inter-Arrival Time

Segment - 106 - Modeling Inter Arrival Time 2

Segment - 107 - Modeling Inter Arrival Time 3

Segment - 108 - Resampling

Segment - 109 - Rolling Time Series

Segment - 110 - Rolling Time Series 2

Segment - 111 - Summary

Segment - 112 - Assignment

Segment - 113 - Assignment Answer

9Module 8: Visualization with Matplotlib and Seaborn

Segment - 114 - Introduction

Segment - 115 - Line Plot Part 1

Segment - 116 - Line Plot Part 2

Segment - 117 - Scatter Plot

Segment - 118 - Count Plot

Segment - 119 - Bar Plot

Segment - 120 - Distribution Plots

Segment - 121 - Box Plots

Segment - 122 - Histograms

Segment - 123 - Pair Plots

Segment - 124 - Visualization Summary

Segment - 125 - Assignment

Segment - 126 - Assignment Answer 2

10Module 9: Segmentation

Segment - 127 - Introduction

Segment - 128 - Segmentation

Segment - 129 - Importance of ABC Analysis

Segment - 130 - Multi Criteria Segmentation

Segment - 131 - Transforming the Data for Excel

Segment - 132 - ABC Analysis in Excel

Segment - 133 - Assignment

Segment - 134 - ABC in Python

Segment - 135 - Multi-Criteria ABC Analysis

Segment - 136 - Multi-Criteria ABC in Python

Segment - 137 - Supplier Segmentation 1

Segment - 138 - Supplier Segmentation 2

Segment - 139 - Supplier Segmentation In Python

Segment - 140 - Value Index

Segment - 141 - Visualizing Krajic

Segment - 142 - Summary

Segment - 143 - Assignment ABC

Segment - 144 - Assignment answer

11Module 10: Forecasting Basics

Segment - 145 - Why We Need Forecasts

Segment - 146 - Qualitative and Quantitative Forecasting

Segment - 147 - Optimistic and Pessimistic Forecasting

Segment - 148 - Time Components

Segment - 149 - Preparing the Data for Regression

Segment - 150 - Multi Linear Regression in Excel Part 1

Segment - 152 - Assignment

Segment - 153 - Regression in Python Part 1

Segment - 154 - Regression in python Part 2

Segment - 155 - Initializing a Date Range for Forecasting

Segment - 156 - Forecasting

Segment - 157 - Forecasting Summary

Segment - 158 - Assignment Questions

Segment - 151 - Multi Linear Regression in Excel Part 2

Segment - 159 - Assignment 1

Segment - 160 - Assignment 2

12Module 11: Time Series Modeling

Segment - 161 - Time Series Introduction

Segment - 162 - Accuracy Measures

Segment - 163 - Preparing the Data for Time-series

Segment - 164 - Getting the Time Series Components: Lecture

Segment - 165 - Getting the Time Series Components

Segment - 166 - Components Uses

Segment - 167 - Arima Models

Segment - 168 - Stationarity Test in Python

Segment - 169 - Arima in Python

Segment - 170 - Arima Diagnostics

Segment - 171 - Grid Search

Segment - 172 - For Looping ARIMA

Segment - 173 - Error Handling

Segment - 174 - Fitting the Best Model

Segment - 175 - Mean Absolute Error

Segment - 176 - Arima Comparison

Segment - 177 - Exponential Smoothing

Segment - 178 - Exponential Smoothing in Python

Segment - 179 - Comparing Exponential Smoothing Models

Segment - 180 - Time Series Summary

Segment - 181 - Assignment

Segment - 182 - Assignment Explanation 1

Segment - 183 - Assignment Explanation 2

Segment - 184 - Assignment Explanation 3

Segment - 185 - Assignment Explanation 4

13Module 12: Forecasting Segmentation

Segment - 186 - Product Classifications

Segment - 187 - Demand Classification

Segment - 188 - Holidays

Segment - 189 - Coefficient of Variation Squared

Segment - 190 - Preparing for Average Demand Interval

Segment - 191 - Average Demand Interval

Segment - 192 - Durations

Segment - 193 - Coerce Duration

Segment - 195 - Conclusion

Segment - 196 - Summary

Segment - 197 - Assignment

Segment - 194 - Classifications

Segment - 198 - Assignment Explanation

14Module 13: Supply Chain Simulations

Segment - 199 - Introduction

Segment - 200 - Waiting Lines

Segment - 201 - Simulation Example Demo

Segment - 202 - Simulation Excel

Segment - 203 - Simulation Assignment

Segment - 204 - Simulating Waiting Time in Python

Segment - 205 - 1000 Simulations

Segment - 206 - Downloading R

Segment - 207 - Installing R

Segment - 208 - Installing Rpy2

Segment - 209 - Simulation with Queue Computer

Segment - 210 - Multiple Resources

Segment - 211 - Getting the Optimum Number of Servers

Segment - 212 - Capacity Constrains

Segment - 213 - Multiple Service Lecture

Segment - 214 - Multiple Service with Queue Computer

Segment - 216 - Summary

Segment - 217 - Assignment

Segment - 215 - Mean Waiting Time of the System

Segment - 218 - Assignment Explanation

15Module 14: Linear Programming in Python

Segment - 219 - Optimization Introduction

Segment - 220 - Problem Formulation

Segment - 221 - Model in Excel

Segment - 222 - Installing Pulp

Segment - 223 - Model In Python

Segment - 224 - Assignment

Segment - 225 - Assignment Explanation

Segment - 226 - Transport Problem in Excel

Segment - 227 - Transport Problem in in Pulp Part 1

Segment - 228 - Transport Optimization Part 2

Segment - 229 - Formulating Supply Constraint

Segment - 230 - Solving the Model

Segment - 231 - Assignment

Segment - 232 - Assignment Answer

Segment - 233 - Production Planning

Segment - 234 - Production Scheduling

Segment - 235 - Production Scheduling in Python

Segment - 236 - Constraints Definition

Segment - 237 - Model Sensitivity

Segment - 238 - Summary

Segment - 239 - Production Scheduling Assignment

Segment - 240 - Assignment Explanation

16Module 15: Inventory

Segment - 241 - Introduction

Segment - 242 - Why We Need Inventory?

Segment - 243 - Inventory Strategies

Segment - 244 - Inventory Types and EOQ

Segment - 245 - Total Logistics Cost and Total Relevant Cost

Segment - 246 - Economic Order Quantity with Excel

Segment - 247 - EOQ with Discounts

Segment - 248 - EOQ Sensitivity

Segment - 249 - EOQ in Python

Segment - 250 - EOQ Practical

Segment - 251 - EOQ with Lead-Time

Segment - 252 - EOQ with Lead-Time in Python

Segment - 253 - Summary Part 1

Segment - 254 - Summary Part 2

Segment - 255 - Assignment

Segment - 256 - Assignment Answer 1

Segment - 257 - Assignment Answer 2

17Module 16: Inventory with Uncertainty

Segment - 258 - Introduction

Segment - 259 - Variability In Supply Chain

Segment - 260 - Demand Lead-Time and Sigma Demand Lead-Time

Segment - 261 - Calculating Average Daily Demand

Segment - 262 - Method 1 for Safety Stock Calculation

Segment - 263 - Method 2 for Safety Stock Calculation

Segment - 264 - Preparing the Data for Safety Stock Calculations

Segment - 265 - Calculating Average Demand

Segment - 266 - Segmentation of Data for Service Level

Segment - 267 - Reorder Point for All Skus

Segment - 268 - Reorder Point Conclusion

Segment - 269 - Lead-Time variability

Segment - 270 - Lead-Time Variability in Python

Segment - 271 - Summary

Segment - 272 - Assignment

Segment - 273 - Assignment Explanation

18Module 17: Inventory Simulations

Segment - 274 - Inventory Policies-1

Segment - 275 - Inventory Policies-2

Segment - 276 - Min Q Demonstration

Segment - 277 - Min Q Lecture

Segment - 278 - Min Q in Excel

Segment - 279 - Periodic Review Demonstration

Segment - 280 - Periodic Review Lecture

Segment - 281 - Periodic Review Demonstration in Excel

Segment - 282 - Min Max Demonstration

Segment - 283 - Min Max Policy

Segment - 284 - Min Max Example in Excel

Segment - 285 - Base Stock Demonstration

Segment - 286 - Base Stock Policy

Segment - 287 - Base Stock Policy in Excel

Segment - 288 - Assignment

Segment - 289 - S,Q Policy in Python

Segment - 290 - Min Max Policy

Segment - 291 - Min Max Simulation

Segment - 292 - Periodic Policy in Python

Segment - 293 - Hybrid Policy

Segment - 294 - Base Stock Policy

Segment - 295 - Comparing All Policies

Segment - 296 - Summary

Segment - 297 - Inventory Simulations Assignment-1

Segment - 298 - Inventory Simulation Assignment-2

19Module 18: Seasonal Inventory

Segment - 299 - Introduction

Segment - 300 - Seasonal Products

Segment - 301 - Point of Maximum Profit

Segment - 302 - How Much I will Sell?

Segment - 303 - Data Table

Segment - 304 - Critical Ratio

Segment - 305 - Critical Ratio in Excel

Segment - 306 - What's Actually Happening?

Segment - 307 - Critical Ratio in Python

Segment - 308 - Preparing the Data for MPN

Segment - 309 - Creating a Margin of Error

Segment - 310 - Applying MPN on All Data

Segment - 311 - Conclusion

Segment - 312 - Seasonal Inventory Summary

Segment - 313 - Assignment Solution

Segment - 314 - Seasonal Inventory Answer

20Module 19: Consumer Behavior and Pricing

Segment - 315 - Introduction.

Segment - 316 - Pricing History

Segment - 317 - Why Pricing is Important?

Segment - 318 - Customers Perception of Price

Segment - 319 - Pricing Mechanisms

Segment - 320 - Commodities

Segment - 321 - Price Response Function

Segment - 322 - Price Response Function in Python

Segment - 323 - Simulating the Demand

Segment - 324 - Point of Maximum Profit

Segment - 325 - Assignment

Segment - 326 - Assignment Explanation

Segment - 327 - Elasticity Introduction

Segment - 328 - Elasticity

Segment - 329 - Linear Elasticity with Inventory

Segment - 330 - Parsing Dates

Segment - 331 - Getting List of Unique Skus

Segment - 332 - Linear Elasticity

Segment - 333 - Error Handling for Linear Elasticity

Segment - 334 - Conclusion

Segment - 335 - Single Optimization Summary

Segment - 336 - Assignment

Segment - 337 - Explanation

21Module 20: Logit Price Response Function

Segment - 338 - Introduction

Segment - 339 - Logistic Regression

Segment - 340 - Logistic Modeling with Inventory

Segment - 341 - Comparison between Logistic and Linear

Segment - 342 - Logit For Looping

Segment - 343 - Logit Assignment

Segment - 344 - Logit Assignment Answer

22Module 21: Multi Product Optimization

Segment - 345 - Introduction

Segment - 346 - Competing Products

Segment - 347 - Relation Among Products

Segment - 348 - Multi-Variate Regression in Python

Segment - 349 - Multinomial Choice Models

Segment - 350 - Multinomial Logit Models

Segment - 351 - Multi Competing Products in Python

Segment - 352 - Summary

23Module 22: Markdowns

Segment - 353 - Introduction

Segment - 354 - Markdowns

Segment - 355 - Why we do Markdowns

Segment - 356 - Customers Segment to Markdowns

Segment - 357 - Problem Formulation

Segment - 358 - Markdowns for Multiple Periods

Segment - 359 - Setting up Solver

Segment - 360 - Salvage Value

Segment - 361 - Markdowns with Forecasting.

Segment - 362 - Sensitivity Analysis.

Segment - 363 - Markdowns for One Period

Segment - 364 - Assignment

24Module 23: RFM Analysis

Segment - 365 - RFM Analysis

Segment - 366 - Customer Segmentation Based on RFM.

Segment - 367 - Customer Recency in Python

Segment - 368 - Frequency and Monetary Value

Segment - 369 - Ranking

Segment - 370 - Grouping

Segment - 371 - Creating the Categories

Segment - 372 - Conclusion

25Module 24: Machine Learning

Segment - 373 - Introduction to Machine Learning

Segment - 374 - Decision Tree Demo

Segment - 375 - Overfitting

Segment - 376 - Kmeans in Python

Segment - 377 - Centroids Visualization

Segment - 378 - Elbow Spree

Segment - 379 - Preparing the Data for Regression

Segment - 380 - Getting the Time Components

Segment - 381 - Encoding

Segment - 382 - Training the Models

Segment - 383 - KNN

Segment - 384 - KNN Grid Search

Segment - 385 - Lasso Grid Search

Segment - 386 - Regularization Importance in Lasso

Segment - 387 - Regularization Visualization

Segment - 388 - Case Study

Segment - 389 - Exploring the Banking Data

Segment - 390 - Preparing the Data

Segment - 391 - Logistic Regression without Grid Search

Segment - 392 - Pre Processing of Data

Segment - 393 - Grid Search

Segment - 394 - Confusion Matrix

Segment - 395 - AUC

Segment - 396 - Area Under Curve

Segment - 397 - Preparing for Pipelines

Segment - 398 - Pipelines for Four Models

Segment - 399 - Grid For Logistic Regression

Segment - 400 - Grids

Segment - 401 - For Looping Pipelines

Segment - 402 - Verbose

Segment - 403 - Pipeline Conclusion

Segment - 404 - Random Forest and Decision Tree Comparison

Segment - 405 - Randomized Search

Instructors

Haytham Omar

He brings a wealth of expertise in supply chain management and data science to the online education platform. With a deep-rooted background in strategic consulting and academic instruction, he has held pivotal roles in optimizing global supply chains and pioneering data-driven solutions. Holding a doctoral degree focused on basket data-driven forecasting and inventory management, his research has significantly advanced understanding in omni-channel logistics. He has cultivated this knowledge through extensive hands-on experience and academic pursuits, including a Master’s in Global Supply Chain Management from KEDGE Business School and a MicroMasters in Supply Chain Management from MITx on edX.

Throughout his career, he has championed numerous innovative projects in collaboration with international institutions and corporations. His contributions extend beyond theoretical frameworks to practical implementations that enhance operational efficiency and profitability. Notably, he has developed specialized courses and workshops covering diverse aspects of supply chain management, from purchasing and inventory control to logistics optimization and lean management. His teaching approach blends rigorous academic insights with real-world applications, empowering learners to navigate complex supply chain challenges with confidence.

As an instructor on the platform, he is dedicated to equipping students with essential skills in supply chain analytics, data-driven forecasting, and strategic management. His courses are designed to foster critical thinking and problem-solving abilities, essential for navigating the dynamic landscapes of global business operations. Whether aspiring supply chain professionals, business owners seeking to optimize logistics, or data enthusiasts looking to delve into practical applications, learners gain invaluable insights and actionable knowledge from his expertise.

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