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  1. Courses
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  3. Using R for Data Science and Supply Chain Analytics

Using R for Data Science and Supply Chain Analytics

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.

Haytham Omar
Haytham Omar
Data Science | core | 37 hours |   Published: Aug 2021
In partnership with:  Coursera

    Discussions

Overview

1.7KSTUDENTS*
94.9%RECOMMEND*

This course includes: 

  • 37 hours on-demand video 
  • 126 downloadable resources 
  • Full lifetime access 
  • Certificate of completion
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: 
  • Introduction to supply chain. 
  • Supply chain Flows. 
  • Data produced by supply chains. 
Python Programming Fundamentals Module includes: 
  • Basics of python 
  • Data cleaning and manipulation. 
  • Statistical analysis. 
  • Data visualization. 
  • Advanced programming. 
Supply chain Applications Module include : 
  • Product segmentations single and multi-criteria. 
  • Supplier segmentations. 
  • Customers segmentations. 
  • Forecasting techniques and accuracy testing. 
  • Linear Programming and logistics optimizations. 
  • Pricing and markdowns optimization techniques. 
  • Inventory policy and safety stock calculations. 
  • Inventory simulations. 
  • Machine learning for supply-chain. 
  • Simulations for optimizing capacity and resources. 

Skills You Will Gain

Data Science
Inventory Management
Linear Programming
Python
Supply chain

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

  • 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! 

Prerequisites

  • Basic knowledge of excel. 

Who Should Attend

  •  If you are an absolute beginner at coding. 
  • 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 R 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

Instructors

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*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
Haytham Omar

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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1Welcome to the course!
2Module 1: Introduction
3Module 2: Supply Chain Data
4Module 3: Installation and Overview of R
5Module 4: R Programming fundamentals
6Module 5: Supply Chain Statistical Analysis
7Module 6: Data Cleaning and Manipulation
8Module 7: Working with Dates in R
9Module 8: Visualize with ggplot2 and Plotly
10Module 9: Supplier and Products Segmentation
11Module 10: Forecasting Basics
12Module 11: Time Series Forecasting
13Module 12: Forecasting Aggregation
14Module 13: Product Segmentation for Demand Planning
15Module 14: Supply Chain Simulations
16Module 15: Inventory Basics
17Module 16: Inventory with Uncertainty
18Module 17: Inventory Simulations
19Module 18: Seasonal Inventory
20Module 19: Consumer Behavior and Pricing
21Module 20: Optimizing the Price for Single Product
22Module 21: Multi Product Optimization
23Module 22: Markdowns and Time Based Discounts
24Module 23: Customer Segmentation
25Module 24: Machine Learning

Segment - 11 - Introduction

uSegment - 12 - Types of Data in Supply Chain

Segment - 13 - Data from Suppliers

Segment - 14 - Data from Production

Segment - 15 - Data from Stocks

Segment - 16 - Data from Sales and Customers

Segment - 17 - Why We Need to Learn Data Science

Segment - 18 - Analytics Types

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

Segment - 205 - Inventory Basics

Segment - 206 - Why we Need Inventory

Segment - 207 - Inventory Strategies

Segment - 208 - Inventory Types and EOQ

Segment - 209 - Total Logistics Cost

Segment - 210 - Economic Order Quantity with Excel

Segment - 211 - EOQ with Quantity Discounts

Segment - 213 - EOQ with Inventorize

Segment - 214 - T Practical in R

Segment - 215 - EOQ with Leadtime

Segment - 216 - Practical Example inside R

Segment - 217 - Assignment Solution

Segment - 218 - Summary 1

Segment - 212 - EOQ Senstivity

Segment - 219 - Summary 2

Segment - 27 - Introduction

Segment - 28 - Different Data Structures and Types in R

Segment - 29 - Do Arithmetic Calculations in R and Write Functions

Segment - 30 - Creating a List

Segment - 31 - Importing Data in R and Basic Exploration

Segment - 32 - Selecting Data in Dataframe

Segment - 33 - If Else Function

Segment - 34 - Conditions

Segment - 36 - For Loops

Segment - 35 - Functions With Conditions

Segment - 37 - Applying a Function Inside a For Loop

Segment - 38 - For Loop on a Dataframe

Segment - 39 - Applying the Function on a Dataframe

Segment - 40 - Assignment

Segment - 41 - Assignment Section 4 Answer Part 1

Segment - 42 - Assignment Section 4 Answer Part 2

Segment - 43 - Summary

Segment - 173 - Introduction

Segment - 174 - Product Classification for Forecasting

Segment - 175 - Checking for Holidays

Segment - 176 - Sku Grouping by Date

Segment - 177 - Customizing a Holiday Count Function

Segment - 178 - For Looping the Holiday Function

Segment - 179 - Calculating Average Demand Intervals

Segment - 180 - Visualizing the Classification

Segment - 181 - Assignment

Segment - 182 - Assignment Solution Part 1

Segment - 183 - Assignment Solution Part 2

Segment - 184 - Assignment Solution Part 3

Segment - 185 - Summary

Segment - 66 - Introduction

Segment - 67 - Introduction to Dplyr

Segment - 68 - Investigate with Dplyr

Segment - 69 - Unique Invoices

Segment - 70 - Average Invoice Value Per Country

Segment - 71 - Average Number of Items with in an Invoice

Segment - 72 - Joining

Segment - 73 - Changing Date Time to Date

Segment - 74 - Pivot Wider

Segment - 75 - Pivot Longer

Segment - 76 - Separate and Paste

Segment - 77 - Putting it all Together

Segment - 78 - Assignment New York Airlines

Segment - 79 - Assignment Question 1 Answer

Segment - 80 - Assignment Question 2 and 3 Answer

Segment - 81 - Assignment Question 4,5 and 6

Segment - 82 - Assignment Question 7

Segment - 83 - Summary

Segment - 44 - Introduction to Statistical Analysis

Segment - 45 - Calculating Measures of Centrality and Spread

Segment - 46 - Putting the Measures Together

Segment - 47 - Correlations

Segment - 48 - Correlations Thresholds

Segment - 49 - Calculating Correlations

Segment - 50 - Detecting Outliers

Segment - 51 - Outliers in R

Segment - 52 - Introduction to Linear Regression

Segment - 53 - Linear Regression

Segment - 54 -Introduction to Distributions

Segment - 55 - Distributions Importance in Supply Chain

Segment - 56 -Chi-Square Tests

Segment - 57 -Distributions in Excel

Segment - 58 - Distributions Chi_Square Test

Segment - 59 - Cover of 90% of the Distribution

Segment - 60 - Assignments Distribution in Excel

Segment - 61 - Assignment Answer

Segment - 62 - Distributions in R

Segment - 63 - Assignment

Segment - 64 - Assignment Answer

Segment - 65 - Summary

Segment - 297 - Introduction

Segment - 298 - Introduction to Logistic Regression

Segment - 299 - Logit Modeling with Inventorize

Segment - 300 - Assignment

Segment - 301 - Assignment Solution

Segment - 01 - Why I Chose R for this Course

Segment - 02 - Why We Should Learn Coding

Segment - 03 - Curriculum

Segment - 04 - Supply Chain Visualization

Segment - 05 - Cost and Service Dynamics

Segment - 06 - Service Level and Product Characteristics

Segment - 07 - Customer and Supplier Characteristics

Segment - 08 - Supply Chain Views

Segment - 09 - The Financial Flow

Segment - 10 - Why is Supply Chain Complicated

Segment - 84 - Introduction

Segment - 85 - Motivation for Working with Dates

Segment - 86 - Parsing Dates with R

Segment - 87 - Make Inference from Dates in R

Segment - 88 - Working with Lubridate

Segment - 89 - Modeling Inter Arrival Time of Customers 1

Segment - 90 - Modeling Inter Arrival Time of Customers 2

Segment - 91 - Assignment

Segment - 92 - Assignment Answer Question 1 to 4

Segment - 93 - Assignment Answer Question 5 and 6

Segment - 94 - Assignment Last Question

Segment - 95 - Summary

Segment - 108 - Introduction

Segment - 109 - Introduction to Segmentation

Segment - 110 - why We Need Segmentation

Segment - 111 - Multi-Criteria Segmentation

Segment - 112 - Transforming the Data for Excel

Segment - 113 - ABC Analysis in Excel

Segment - 114 - Assignment Explanation

Segment - 115 - ABC Analysis in R

Segment - 116 - Multi Criteria ABC Analysis

Segment - 118 - Assignment

Segment - 119 - Assignment Answer 1

Segment - 120 - Assignment Answer 2

Segment - 121 - Supplier Segmentation 1

Segment - 122 - Supplier Segmentation 2

Segment - 123 - Krajic in R

Segment - 124 - Visualizing Krajic

Segment - 117 - Multi-Criteria ABC Analysis Store Level

Segment - 125 - Summary

Segment - 161 - Introduction

Segment - 162 - Hierarchical Forecasting

Segment - 163 - Aggregation Approaches

Segment - 165 - Hierarchical Structuring

Segment - 166 - Aggregate Forecasting

Segment - 167 - Testing and Accuracy for Aggregation

Segment - 168 - Comparison between Middle Out, Bottom Up and Top Down

Segment - 169 - Assignment

Segment - 170 - Assignment Answer 1

Segment - 171 - Assignment Answer 2

Segment - 164 - Preparing the Data for Aggregation

Segment - 172 - Summary

Segment - 340 - Introduction to Machine Learning

Segment - 341 - Decision Tree Demo

Segment - 342 - Kmeans

Segment - 343 - Overfitting

Segment - 344 - Kmeans in R

Segment - 345 - Total sum of Squares

Segment - 346 - Silhouette

Segment - 347 - Interactive three dimensional Scatter Plot

Segment - 348 - Assignment

Segment - 350 - Decision Tree

Segment - 351 - Comparing Models

Segment - 352 - Classification Data orientation

Segment - 353 - Exploring the Data

Segment - 354 - Correlation Matrix

Segment - 355 - Splitting

Segment - 356 - Training and Splitting

Segment - 357 - Control the Fitting

Segment - 358 - Logistic Regression Classification

Segment - 359 - Probabilities of the Logistic Regression

Segment - 360 - Confusion Matrix

Segment - 361 - ROC

Segment - 362 - Tree Model

Segment - 363 - Assignment

Segment - 364 - Conclusion

Segment - 349 - Supervised Learning Linear Regression

Segment - 365 - Summary

Segment - 19 - Welcome to the World of R

Segment - 20 - What is R Statistical Language

Segment - 21 - How to Install R

Segment - 22 - How to Install R Studio

Segment - 23 - A Walk Through Tutorial

Segment - 24 - Setup your Project

Segment - 25 - Install Packages!

Segment - 26 - Summary

Segment - 302 - Introduction

Segment - 303 - Competing Products

Segment - 304 - The Correlation among Products

Segment - 305 - Multi-Variate Regression Fitting

Segment - 306 - Relation among Products

Segment - 307 - Annova

Segment - 308 - Updating the Model

Segment - 309 - Assignment

Segment - 310 - Comparing between two Models

Segment - 311 - Prediction with Multivariate Regression

Segment - 312 - Multinomial choice Models

Segment - 313 - Optimizing Competing prices with Inventorize

Segment - 314 - Results of Optimization

Segment - 315 - Applying. the algorithm on 40000 Observations

Segment - 316 - Assignment

Segment - 317 - Competing Products

Segment - 261 - Introduction

Segment - 262 - Seasonal Products

Segment - 263 - Point of Maximum Profit

Segment - 264 - How much I will Sell?

Segment - 265 - Data Table

Segment - 266 - Critical Ratio

Segment - 267 - Critical Ratio in Excel

Segment - 268 - Whats Actually Happening

Segment - 269 - Critical Ratio in R with Inventorize

Segment - 270 - Expected Profit with Inventorize

Segment - 271 - Preparing the Data for Optimum Quantity

Segment - 272 - Creating Margin of Error

Segment - 273 - Conclusion

Segment - 274 - Assignment

Segment - 275 - Assignment Solution

Segment - 276 - Summary

Segment - 96 - Introduction

Segment - 97 - Line Plots

Segment - 98 - Scatter Plots

Segment - 99 - Bar Plots

Segment - 100 - Distribution Pots

Segment - 101 - Box Plots

Segment - 102 - Histograms 1

Segment - 103 - Histograms 2

Segment - 104 - Assignment

Segment - 105 - Assignment Answer 1 and 2

Segment - 106 - Assignment Solution Part 2

Segment - 107 - Summary

Segment - 140 - Introduction to Time Series Forecasting

Segment - 141 - Converting Data to Time Series

Segment - 142 - Weekly and Daily Time Series

Segment - 143 - Analyze the Time Series

Segment - 144 - Getting Time Components and Measurements

Segment - 145 - Dissecting Components inside R

Segment - 146 - Strength of Trend and Seasonality

Segment - 147 - Exponential Smoothing

Segment - 148 - Arima and it's Components

Segment - 149 - Accuracy Measure for Forecasting

Segment - 150 - Determine Arima Orders

Segment - 151 - Training and Testing

Segment - 152 - Dynamic Harmonic Regression

Segment - 153 - Measuring Accuracy of New Model

Segment - 154 - Improving Arima with Grid Search

Segment - 155 - Error Handling with Grid Search

Segment - 156 - Battle of the Arima(s)

Segment - 157 - Assignment

Segment - 158 - Assignment Answer Part 1

Segment - 159 - Assignment Answer Part 2

Segment - 160 - Summary

Segment - 330 - RFM Analysis

Segment - 331 - Segmentation of Customers Based on RFM

Segment - 332 - Preparing the Data

Segment - 333 - Recency

Segment - 334 - Joining KPIs together

Segment - 335 - Visualization

Segment - 337 - Grouping into Tiles

Segment - 338 - 3-D Scatter Plots

Segment - 336 - Ranking

Segment - 339 - Assignment

Segment - 186 - Introduction

Segment - 187 - Waiting Line and Queue Theory

Segment - 188 - Demonstration

Segment - 189 - Waiting Line in Excel

Segment - 190 - Simulation Assignment

Segment - 191 - Waiting Lines in R

Segment - 192 - Making 400 Simulations at once

Segment - 193 - Waiting line in a Call Centre

Segment - 194 - Defining the Right K

Segment - 195 - Simulation with Capacity Constraints

Segment - 196 - Assignment

Segment - 197 - Assignment Solution

Segment - 198 - Sequential Services in One System

Segment - 199 - Many Services

Segment - 200 - Multiple Service Simulations in R

Segment - 201 - Conclusion

Segment - 202 - Assignment

Segment - 203 - Assignment Solution

Segment - 204 - Summary

Segment - 220 - Introduction

Segment - 221 - Uncertainty in Supply Chain

Segment - 222 - Demand Lead time and Sigma DL

Segment - 223 - Calculating Average Daily Demand

Segment - 224 - Method 1 for Safety Stock Calculation

Segment - 225 - Method 2 for Safety Stock Calculations

Segment - 226 - Preparing SKUs for Safety Stock Calculations

Segment - 227 - Calculating Average Demand and Sd

Segment - 228 - Setting Cycle Service Level in R

Segment - 229 - Calculating Reorder Point with Inventorize

Segment - 230 - Calculating Leadtime

Segment - 231 - Leadtiime Variability in Excel

Segment - 232 - Leadtime Variability with Inventorize

Segment - 233 - Wrap for Indeterministic Inventory

Segment - 234 - Assignment

Segment - 235 - Assignment Solution

Segment - 236 - Summary

Segment - 237 - Introduction to Inventory Policies

Segment - 239 - Min Q Policy

Segment - 240 - MIN,Q Example in Excel

Segment - 241 - Periodic Review Demonstration

Segment - 242 - Periodic Review Policy

Segment - 243 - Periodic Example

Segment - 244 - Min Max Demonstration

Segment - 245 - Min Max Policy

Segment - 246 - Min Max Example in Excel

Segment - 247 - Base Stock Demonstration

Segment - 248 - Base Stock Policies

Segment - 249 - Base Stock Policy Example

Segment - 250 - Assignment

Segment - 251 - Simulating SQ Policy

Segment - 252 - Increasing the Quantity of SQ Policy

Segment - 253 - Visualizing the Simulation

Segment - 254 - Poisson Min-Max Simulation

Segment - 256 - Visualizing all Policies

Segment - 255 - Variations of One Policy

Segment - 257 - Metrics Comparison

Segment - 258 - Assignment

Segment - 259 - Assignment Answer

Segment - 238 - MIN Q Demonstration

Segment - 260 - Summary

Segment - 318 - Introduction

Segment - 319 - Markdowns

Segment - 320 - Why we do markdowns

Segment - 321 - Customers segments to markdowns

Segment - 322 - Problem Formulation

Segment - 323 - Markdowns for Multiple Periods

Segment - 324 - Setting up Solver

Segment - 325 - Solver with Salvage Value

Segment - 326 - Markdowns with Forecasting

Segment - 327 - Sensitivity Analysis

Segment - 328 - Markdowns for One Period

Segment - 329 - Assignment

Segment - 277 - Revenue Management

Segment - 278 - Pricing History

Segment - 279 - Why is Pricing Important

Segment - 280 - Customer Perception of Price

Segment - 281 - Pricing Mechanisms

Segment - 282 - Commodities

Segment - 283 - Price Response Function

Segment - 284 - Price response Function Motivation in R

Segment - 285 - Assignment

Segment - 286 - Assignment Solution

Segment - 287 - Elasticity Introduction

Segment - 288 - Elasticity

Segment - 289 - Linear Elasticity with Inventorize

Segment - 290 - Practical Example

Segment - 291 - Modelling all Retail SKUs at Once

Segment - 292 - Optimum Price for all SKUs

Segment - 293 - Optimum Pricing Validation

Segment - 294 - Assignment

Segment - 295 - Assignment Solution

Segment - 296 - Summary

Segment - 126 - Why We Need Forecasts

Segment - 127 - Qualitative and Quantitative Forecasting

Segment - 128 - Optimistic and Pessimistic Forecasting

Segment - 129 - Preparing the Data for Regression

Segment - 130 - Changing the Format to Date

Segment - 131 - Multiple Linear Regression in Excel

Segment - 132 - Part 2

Segment - 133 - Assignment Explanation

Segment - 134 - Fitting Forecast with Regression in R

Segment - 135 - Forecasting with Linear Regression in R

Segment - 136 - Assignment

Segment - 137 - Assignment Solution Part 1

Segment - 138 - Assignment Solution Part 2

Segment - 139 - Summary