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Fundamentals of Machine Learning for Supply Chain

Rajvir Dua and Neelesh Tiruviluamala

This course will teach you how to leverage the power of Python to understand complicated supply chain datasets. Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases.

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What's inside

Syllabus

Introduction to Programming Concepts and Python Practices
Welcome to the course! In this first module, we’ll learn about the fundamentals of programming and Python. We’ll start with basic data structures, functions, and loops and then some time becoming familiar with importing modules and libraries. Finally, we'll put our new skills to the test by optimizing a supply constraint problem using linear programming techniques.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches Python programming basics and advanced techniques for data analysis and manipulation, catering to both beginners and intermediate learners
Utilizes real-world supply chain datasets, making the lessons practical and industry-relevant
Emphasizes exploratory data analysis (EDA) techniques for understanding complex datasets
Covers advanced data wrangling and manipulation techniques using Pandas and Numpy, providing learners with essential data science tools
Taught by experienced instructors in the field, Rajvir Dua and Neelesh Tiruviluamala
Includes a final project that challenges learners to apply their skills to real-world supply chain optimization scenarios

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Reviews summary

Practical python & ml for supply chain

According to learners, this course provides a solid foundation in using Python, Numpy, and Pandas for exploratory data analysis within a supply chain context. Students appreciate the practical application of concepts and the clear explanations, particularly for those new to programming. While it excels at data wrangling and manipulation, some reviews suggest that the machine learning aspects are foundational rather than deeply analytical, making it more suited for beginners. The final project is highlighted as a valuable opportunity to apply learned skills to real-world supply chain problems.
Instructors clearly explain complex topics, making learning accessible.
"The instructor breaks down difficult concepts into manageable pieces."
"Lectures were engaging and easy to follow, even for abstract topics."
"I appreciated the step-by-step guidance provided throughout the modules."
Well-suited for learners new to Python or data science in a supply chain context.
"As someone new to Python, this course was perfectly paced and very understandable."
"If you're a supply chain pro looking to get into data, this is a great start."
"For those with prior Python experience, some early modules might feel slow."
Concepts are immediately applicable to real-world supply chain scenarios.
"The supply chain datasets were very relevant and helped me see practical uses."
"I can apply these optimization techniques directly to my work in logistics."
"The final project simulating warehouse and freight optimization was fantastic."
Offers a robust introduction to Python, Numpy, and Pandas for data manipulation.
"I really got a solid grip on Pandas and Numpy for cleaning and shaping data."
"The course clearly lays out Python basics, making it easy to follow even as a beginner."
"Learned effective techniques for data wrangling, especially merges and pivots."
Offers a basic understanding of ML concepts, leaning more towards data preparation.
"The ML part felt more like an introduction to preprocessing than deep algorithm work."
"Good for understanding data for ML, but don't expect advanced modeling techniques."
"Could use more in-depth coverage on actual machine learning models beyond basics."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Fundamentals of Machine Learning for Supply Chain with these activities:
Review mathematics
Refresh your mathematics skills to enhance your understanding of supply chain models and algorithms.
Browse courses on Calculus
Show steps
  • Review basic algebra and calculus concepts.
  • Practice solving linear equations and matrices.
  • Explore online resources for mathematics refreshers.
Gather resources on supply chain data analysis
Curate valuable resources to enhance your learning and stay updated on supply chain data analysis.
Show steps
  • Identify and collect relevant articles, books, and online courses.
  • Organize them into a structured compilation.
  • Share your compilation with others.
Work on Python coding challenges
Sharpen your Python skills by solving coding challenges related to data analysis and supply chain management.
Browse courses on Python
Show steps
  • Find online coding challenges platforms.
  • Start with easy challenges and gradually increase difficulty.
  • Focus on solving challenges related to data manipulation, visualization, and supply chain optimization.
  • Seek support from online forums or mentors if needed.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a blog post or article on supply chain data analysis
Share your knowledge and insights by creating content that demonstrates your understanding of supply chain data analysis.
Browse courses on Supply Chain Management
Show steps
  • Choose a specific topic related to supply chain data analysis.
  • Research the topic thoroughly.
  • Write a well-structured and informative blog post or article.
  • Publish your content on a relevant platform.
Explore advanced data analysis techniques
Expand your knowledge by exploring advanced data analysis techniques applicable to supply chain management.
Browse courses on Data Analysis
Show steps
  • Identify specific areas of interest within data analysis.
  • Find online tutorials or courses on these topics.
  • Follow the tutorials and apply the techniques to supply chain datasets.
  • Share your findings and insights with others.
Participate in a data analysis competition
Test your skills and learn from others by participating in a data analysis competition focused on supply chain management.
Browse courses on Data Analysis
Show steps
  • Identify and register for a relevant competition.
  • Form a team or work individually.
  • Analyze the competition data and develop a solution.
  • Submit your solution and compete for prizes.
Design a supply chain optimization model
Apply your knowledge by designing and implementing a supply chain optimization model to solve a real-world problem.
Browse courses on Supply Chain Optimization
Show steps
  • Identify a specific supply chain problem to address.
  • Gather and analyze relevant data.
  • Develop a mathematical model to represent the problem.
  • Implement the model using appropriate software.
  • Evaluate the results and make recommendations.

Career center

Learners who complete Fundamentals of Machine Learning for Supply Chain will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
As a Machine Learning Engineer, you will build and deploy machine learning models to solve real-world problems. This course will provide you with a comprehensive understanding of machine learning concepts and techniques used in the supply chain domain, such as linear programming and optimization.
See salaries and explore the career path for Machine Learning Engineer
Operations Research Analyst
As an Operations Research Analyst, you will leverage machine learning to tackle challenges in supply chain management. This course will equip you with skills in Python, linear programming, and other optimization techniques commonly used in this field.
See salaries and explore the career path for Operations Research Analyst
Data Analyst
As a Data Analyst, you may often work in the field of supply chain management and logistics. Taking this course will prepare you to work with complex datasets and use tools like Python and Numpy to make data-driven decisions.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to solve complex problems in finance and other industries. This course will equip you with the skills to apply machine learning techniques to supply chain data to make informed decisions.
See salaries and explore the career path for Quantitative Analyst
Data Scientist
Data Scientists use machine learning to solve complex problems in various industries, including supply chain management. This course will lay the groundwork for your journey as a Data Scientist by providing you with a solid foundation in Python, data wrangling, and machine learning.
See salaries and explore the career path for Data Scientist
Logistics Analyst
As a Logistics Analyst, you will model supply chains from scratch. Taking this course in Fundamentals of Machine Learning for Supply Chain will help build a foundation for the machine learning component used in building models to optimize supply chain efficiency and cost.
See salaries and explore the career path for Logistics Analyst
Supply Chain Manager
In this role, you will be responsible for overseeing supply chains and using data to make decisions. Taking Fundamentals of Machine Learning for Supply Chain will give you the technical skills needed in this role to use a programming language, Python, to solve business problems.
See salaries and explore the career path for Supply Chain Manager
Industrial Engineer
Industrial Engineers work on improving supply chains. This course will teach you Python programming, optimization techniques, and other useful supply chain and engineering concepts.
See salaries and explore the career path for Industrial Engineer
Software Developer
As a Software Developer, you can specialize in building machine learning solutions for the supply chain industry. This course will teach you Python, Numpy, Pandas, and other essential tools and techniques used in this domain.
See salaries and explore the career path for Software Developer
Risk Analyst
In the supply chain, Risk Analysts use data and machine learning techniques to identify and mitigate risks. This course can help you build a strong foundation in data analysis, machine learning, and supply chain management, which are essential for this role.
See salaries and explore the career path for Risk Analyst
Business Analyst
Business Analysts use data to solve supply chain-related business problems. Taking this course will prepare you for working with Python, Numpy, Pandas, and other tools to perform data munging, exploration, and analysis.
See salaries and explore the career path for Business Analyst
Financial Analyst
Financial Analysts may work in the supply chain industry, using data to make informed decisions. This course will teach you Python, data analysis techniques, and other skills necessary to succeed in this role.
See salaries and explore the career path for Financial Analyst
Business Intelligence Analyst
As a Business Intelligence Analyst, you may work in the supply chain industry, analyzing data to identify trends and make recommendations. This course will provide you with the skills to manipulate and analyze supply chain data using Python and other tools.
See salaries and explore the career path for Business Intelligence Analyst
Management Consultant
Management Consultants often work with supply chain companies, helping them to improve their operations. This course will equip you with the skills to analyze supply chain data, identify areas for improvement, and make recommendations for optimizing efficiency.
See salaries and explore the career path for Management Consultant
Project Manager
As a Project Manager in the supply chain field, you may be responsible for managing projects related to implementing machine learning solutions. This course will give you a solid understanding of the fundamentals of machine learning and its applications in supply chain management.
See salaries and explore the career path for Project Manager

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Fundamentals of Machine Learning for Supply Chain.
Cover image
Machine Learning in Action
Save
Great introduction to applied machine learning, using Python, and offers valuable insights into its applications in diverse domains, including supply chain management.
Machine Learning in Action
Paperback
$$
(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
Kindle Edition
$
Cover image
Python for Data Analysis
Save
If you are new to Python and data analysis, this book will provide you with a solid foundation in Python programming and data manipulation techniques, which are essential for working with supply chain data.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Linear Programming and Network Flows
Save
This advanced textbook explores linear programming techniques, including integer programming, which are used in supply chain optimization problems, providing a deeper understanding of the course's optimization module.
Linear Programming and Network Flows
Hardcover
$$$
Linear Programming and Network Flows
Hardcover
$$
Linear Programming and Network Flows
Hardcover
$$
Linear Programming and Network Flows
Kindle Edition
$$$$
Cover image
Data Science for Business
Save
Introduces data science concepts and techniques, emphasizing their application in business contexts, complementing the course's focus on supply chain applications.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
This classic textbook provides a comprehensive overview of statistical learning techniques, including regression, classification, and clustering, offering a deeper understanding of the statistical foundations of machine learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Deep Learning
Save
If you are interested in exploring advanced machine learning topics, this book provides a comprehensive introduction to deep learning, which has shown promising applications in supply chain management.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Tidy Modeling with R
Save
Provides a practical guide to predictive modeling, including model selection, evaluation, and deployment, complementing the course's focus on machine learning techniques.
Tidy Modeling with R
Paperback
Check
Tidy Modeling with R
Kindle Edition
Check
Cover image
Optimization in Operations Research
Save
This textbook provides a comprehensive review of optimization techniques, including linear programming and network optimization, offering a deeper understanding of the mathematical foundations of the course's optimization module.
Optimization in Operations Research
Paperback
Check
Optimization in Operations Research
Kindle Edition
Check
Cover image
Time Series Analysis
Save
Provides a comprehensive overview of time series analysis techniques, including forecasting, which can be applied to demand forecasting in supply chain management.
Time Series Analysis: Forecasting and Control...
Hardcover
$$$$
Time Series Analysis: Forecasting and Control...
Kindle Edition
$$$$
Cover image
Logistics and Supply Chain Management
Save
Provides a comprehensive overview of supply chain management concepts and practices, offering a broader perspective on the domain in which the course's machine learning techniques are applied.
Logistics and Supply Chain Management: Logistics &...
Paperback
$$$
Logistics and Supply Chain Management: Logistics &...
Kindle Edition
$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive introduction to reinforcement learning, which is an advanced machine learning technique that has potential applications in supply chain management, offering an opportunity to explore cutting-edge research.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$

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Course image
Effort
4 weeks of study, 2-5 hours a week
Level
Introductory
Via
Coursera
Institution
LearnQuest
Instructors
Rajvir Dua
Neelesh Tiruviluamala
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова Bahasa Indonesia ไทย қазақша
This course belongs to a collection called Machine Learning for Supply Chains. It's comprised of four courses:
  1. Advanced AI Techniques for the Supply Chain
  2. Capstone Project: Predicting Safety Stock
  3. Demand Forecasting Using Time Series
  4. Fundamentals of Machine Learning for Supply Chain (viewing)
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches Python programming basics and advanced techniques for data analysis and manipulation, catering to both beginners and intermediate learners
Utilizes real-world supply chain datasets, making the lessons practical and industry-relevant
Emphasizes exploratory data analysis (EDA) techniques for understanding complex datasets
Covers advanced data wrangling and manipulation techniques using Pandas and Numpy, providing learners with essential data science tools
Taught by experienced instructors in the field, Rajvir Dua and Neelesh Tiruviluamala
Includes a final project that challenges learners to apply their skills to real-world supply chain optimization scenarios
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