- Why I chose R for this course ?
- Why we should Learn Coding.
- Curriclum
- 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
Online
₹ 479 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
RA: Data Science and Supply Chain Analytics. (A-Z with R) online certification was designed by Haytham Omar, a Supply Chain Consultant, and is offered through Udemy which is meant for students who wish to become supply chain data scientists by combining data science and supply chain knowledge. The RA: Data Science and Supply Chain Analytics. (A-Z with R) online course by Udemy is organized into interactive learning modules that begin with supply chain foundations and end with R programming fundamentals.
RA: Data Science and Supply chain analytics. (A-Z with R) online classes cover 38 hours of detailed video lectures along with articles, 56 downloadable resources, and assignments to help students gain practical knowledge of the supply chain applications using data science. Forecasting, revenue management, inventory management, simulation modeling, data manipulation, and data cleaning are all covered in this course. Students who wish to learn everything presented in this course must enroll and pay for the lifetime membership to access the course content.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 38 hours of pre-recorded video content
- 1 article
- 156 downloadable resources
- Assignments
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 38 hours of pre-recorded video content
- 1 article
- 156 downloadable resources
- Assignments
- 30-day money-back guarantee
- Unlimited access
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the RA: Data Science and Supply chain analytics. (A-Z with R) certification course, students will gain a solid understanding of the fundamental concepts of data science as they apply to supply chain analytics. Students will learn how to use machine learning and R programming to perform a variety of data operations, such as data manipulation and data cleaning. Students will study inventory management tactics, sourcing, and forecasting, as well as stock policies. Students will learn how to examine various types of data and make informed supply chain decisions using various methodologies and approaches.
The syllabus
Introduction
Supply chain Data!
- Introduction
- Types Of Data in supply chain
- Data From suppliers
- Data From Production
- Data From Stocks
- Data From Sales & Customers
- Why we Need to learn Data Science?
- Analytics Types
Installation and Overview of R
- Welcome to the World of R!
- What is R statistical Language.
- How to Install R
- How To install Rstudio
- A walk through Tutorial
- setup your Project!
- install Packages!
- Summary
R programming Fundmentals.
- Introduction
- Different Data Structures and types in R
- Do arithmetic Calculations in R and write vectors
- Creating a list
- Importing Data in R and basic Exploration functions
- Selecting Data in dataframe.
- If Else function
- Conditions
- Function with conditions
- for_loops
- applying a function inside for loop
- For loop on a data_frame
- applying the function on a data frame
- Assignment
- Assignment section 4 answer Part 1
- Assignment Section 4 Answer Part 2
- Summary
Supply Chain Statistical Analysis
- Intro
- Calculating Measures of Centrality and spread 1
- Calculating Measures of Centrality and spread 2
- Putting the Measures together
- Correlations
- Correlation Thresholds
- Calculating Correlations
- Detecting outliers
- Outliers in R
- Intro to Linear Regression
- Linear Regression
- Intro to Distributions.
- Distributions importance in supply chain
- Chi-Square tests
- Distributions in Excel
- Distributions Chi-Square test
- Cover for 90% of the Normal Distribution
- Assignment Distributions in Excel
- Assignment answer: Bike Demand Chi-square test
- Distributions in R
- Assignment
- Assignment answer
- Summary
Data Cleaning and Manipulation.
- Intro
- Intro To Dplyr
- Investigate with Dplyr
- Unique Invoices
- Average Invoice Value Per Country
- Average Number of items within an Invoice.
- Joining
- Changing date time to date
- Pivot Wider
- Pivot Longer
- Separate and paste
- putting it all together
- Assignment: New York airlines
- Assignment question 1 answer
- Assignment question 2 & 3 answer
- Assignment Question 4,5 and 6
- Assignment question 7
- Summary
Working with Dates in R.
- Intro.
- Motivation for working with dates
- Parsing dates with R.
- Make inference from dates in R
- Woking with Lubridate
- Modeling inter-arrival time of Customers 1
- modeling inter arrival time of customers 2
- Assignment
- Assignment answer question1 to 4
- Assignment answer question 5 and 6
- Assignment Last Question
- Summary
Visualize with ggplot2 and Plotly.
- Introduction
- line plots
- scatter plots
- Barplots
- Distribution Plots
- Boxplots
- Histograms
- Histograms 2
- Assignment.
- Assignment Solution question 1 and 2
- Assignment solution_part2
- Summary
Suppliers and Products Segmentation.
- Intro
- Intro to segmentation
- Why we need segmentation in our supply chain?
- Multi- Criteria segmentation.
- Transforming the data for excel
- ABC_Analysis_in Excel
- Assignment Explanation
- ABC_analysis in R
- Multi-Criteria ABC analysis
- Multi-Criteria ABC analysis_store level
- Assignment
- assignment answer part 1
- assignment answer part2
- Supplier Segmentation.
- Supplier Segmentation 2
- Krajic in R
- Visualizing Krajic
- Summary
Forecasting Basics.
- Why We Need Forecasts
- Qualitative and Quantitative Forecasting
- Optimistic and Pessimistic Forecasting
- Preparing the Data for regression
- Changing the format of posixct to date.
- Multiple linear regression in Excel Revised
- Part 2
- Assignment Explanation
- fitting forecast with Regression_in_R
- Forecasting with linear regression in R
- Assignment.
- Assignment solution part 1
- Assignment solution part 2
- Summary
Time-Series Forecasting
- Introduction To Time series forecasting
- Converting data to time-series
- Weekly and Daily time_series
- Analyze the time series
- Getting time components and measure strength of trend and seasonality
- Dissecting components inside R
- Measuring strength of trend and seasonality.
- Exponential Smoothing
- Arima and it's components
- Accuracy Measures for forecasting
- Determine Arima Orders
- Training and testing
- Dynamic Harmonic regression
- Measuring accuracy Of new model
- Improving ARIMA: Grid search with SARIMA
- Error handling while Grid search
- Battle of the Arima(s)
- Assignment
- Assignment Answer Part 1
- Assignment answer Part 2
- Summary
Forecasting Aggregation
- Intro
- Hierarchal and Grouping
- Aggregation approaches
- Preparing the data for aggregation
- Hierarchal Structuring
- Aggregate forecasting
- Testing and Accuracy for aggregation
- Comparison between Middle out, Bottom Up and Top Down
- Assignment
- Assignment answer 1
- Assignment answer 2
- Summary
Products segmentation for Demand Planning.
- Intro
- Product's Classifications for forecasting.
- Checking for holidays
- SKU grouping by date
- Customizing a holiday count Function
- For-looping the holiday function
- Calculating Average demand intervals and CV squared.
- Visualizing the classification
- Assignment
- Assignment solution part1
- Assignment Solution Part 2
- Assignment solution part 3
- Summary
Supply chain Simulations
- Intro
- Waiting Line and Queue theory.
- Example Demonstration
- Waiting line in excel
- Simulation assignment
- Waiting lines in R
- Making 400 Simulations At once.
- Waiting Line in call centre
- Defining the Right K
- Simulation With Capacity Constraints
- Assignment
- Assignment solution
- Sequential services in one system
- Many Services
- Multiple Service simulations in R
- Conclusion
- Assignment
- Assignment Solution
- Summary
Inventory Basics
- Intro
- Why we need inventory?
- Inventory Strategies
- Inventory Types and EOQ
- Total Logistics Cost and total relevant cost
- Economic Order Quantity With Excel
- EOQ with quantity discounts
- Eoq Sensitivity
- EOQ with inventorize
- T practical in R
- EOQ with Lead Time
- Practical Example inside R
- Assignment
- Assignment Solution
- Summary
- Summary 2
Inventory with Uncertainty
- Intro
- Uncertainty and Variability in supply chain
- Demand Lead-time and Sigma DL
- Calculating average daily demand
- Method 1 for safety stock calculation.
- Method 2 for safety Stock Calculation
- Preparing SKUs for safety stock calculations.
- Calculating average demand and sd
- Setting Cycle service level in R
- Calculating re-order point with inventorize
- Calculating re-order point with lead time variability
- Lead Time Variability in Excel
- lead Time Variability with Inventorize.
- Wrap for Indeterministic Inventory
- Assignment
- Assignment solution
- Summary
Inventory Simulations
- Introduction to Inventory Policies
- Min Q Demonstration
- Min Q policy
- Min,Q example in excel
- Periodic Review Demonstration.
- Periodic Review Policy
- Excel Example for periodic review.
- Min Max Demonstration.
- Min Max Policy
- Min Max Example in Excel
- Base Stock Demonstration.
- Base Stock Policies
- Base stock Policy_Example
- Assignment
- Simulating (s,Q) Policy
- Increasing the Quantity of S,Q Policy
- Visualizing the simulation
- Poisson Min-Max Simulation
- Visualizing variations of one policy!
- Visualizing all policies.
- Metrics Comparison
- Assignment.
- Assignment Solution
- Summary
Seasonal Inventory
- Introduction
- 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 R with inventorize
- Expected Profit with Inventorize
- Preparing the data for optimum Quantity Calculation
- Creating a margin of error
- Conclusion
- Assignment
- Assignment solution
- Summary
Consumer Behavior and Pricing
- Revenue Management
- Pricing History
- Why is Pricing important?
- Customer's perception of Price.
- Pricing Mechanisms
- Commodities
- Price response Function
- Price Response Function Motivation In R.
- Assignment
- Assignment Solution
- Elasticity Intro
- Elasticity
- Linear Elasticity with Inventorize
- Practical Example: Preparing the data for modeling
- Modeling all retail SKUs at once.
- Optimum Price for All Skus
- Optimum Pricing validation
- Assignment
- Assignment solution
- Summary
Optimizing the Price for a single product.
- intro
- Intro to logistic regression
- Logit modeling with inventorize
- Assignment
- Assignment Solution
Multi-Product Optimization
- Intro
- Competing Products.
- The Co-relation Among products
- Multi-Variate Regression fitting.
- Relation_among_products
- ANOVA
- Updating the model
- Assignment
- Comparing between the two models
- Prediction with Multivariate regression
- multinomial choice models
- Optimizing competing prices with Inventorize
- Results of Optimization
- Applying the Algoritm on 40000 Observations
- Assignment
- Summary
Markdowns and Time-based Discounts
- Intro
- Markdowns
- Why We do Markdowns
- Customers Segments to Markdowns
- Problem Formulation
- Markdowns for Multiple periods.
- Setting Up Solver
- Solver with salvage Value
- Markdowns with forecasting
- Sensitivity analysis
- Markdowns for one period
- Assignment
Customer Segmentations.
- RFM Analysis
- Segmentation of customers based on RFM Analysis.
- Preparing the data.
- Recency
- Joining KPIs together.
- Visualization
- Ranking
- Grouping into tiles.
- 3D scatterplots
- Assignment
Machine Learning
- Intro To machine learning
- Decision tree demo
- Kmeans
- Overfitting
- Kmeans in R
- Total sum of squares
- Silhouette
- Interactive three dimensional scatter plot.
- Assignment
- Supervised learning : Linear regression.
- Supervised Learning : Decision Trees and random forest.
- Comparing Models
- Classification data orientation
- Exploring the data
- Correlation matrix
- Splitting
- Training and Testing
- Control the fitting.
- Logistic Regression Classification.
- Probabilities of the logistic Regression
- Confusion Matrix
- ROC
- Decision tree model
- Assignment
- Conclusion
- Summary
Association Rules
- Introduction
- Intro to Market basket analysis
- Top 10 Products
- Reading Transactions.
- Summary of Transactions
- Apriori
- Top 10 rules
- Subsetting
- Assignment
Bonus Webinar! Noble Prog
- Introduction To Webinar
- Webinar
Instructors
Mr Haytham Omar
Consultant in Supply chain
Freelancer
M.E /M.Tech., Ph.D