Course curriculum

  • 1

    Module 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
  • 2

    Module 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
  • 3

    Module 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
  • 4

    Module 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
  • 5

    Module 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
  • 6

    Module 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
  • 7

    Module 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
  • 8

    Module 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
  • 9

    Module 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
  • 10

    Module 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
  • 11

    Module 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
  • 12

    Module 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
  • 13

    Module 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
  • 14

    Module 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
  • 15

    Module 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
  • 16

    Module 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
  • 17

    Module 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
  • 18

    Module 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
  • 19

    Module 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
  • 20

    Module 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
  • 21

    Module 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
  • 22

    Module 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
  • 23

    Module 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
  • 24

    Module 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