Fundamentals of Machine Learning for Supply Chain from Coursera

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.

Digging Into Data: Common Tools for Data Science

In this next module, we'll dive into the most common tools used for data science: Python, and Numpy. We'll start with Numpy, getting used to np arrays and their main functionality. After getting familiar with loading in data of all types, we'll learn about some basic data description and cleaning techniques. We'll also learn to work with indexes and columns in Dataframes. We'll end with an introduction to plotting and summary statistics. We will use common supply chain data sets for our explorations

Higher Level Data Wrangling and Manipulation

In this third module, we'll take our Pandas and Numpy skills to the next level, learning how to effectively combine and reshape data. We'll learn how to reshape data to fit with our needs through merges and pivots. This setup will help us tackle common data preprocessing steps necessary to run machine learning algorithms, such as one-hot encoding. Finally, we'll encounter the most important tools in our Pandas arsenal (Groupby-Apply-Transform) and explore its transformative functionality.

Course 1 Final Project

In this final project, we'll take collection of various data sets involving warehouse capacities, product demand, and freight rates to optimize cost of producing and shipping products.

Good to know

Know what's good

, what to watch for

, 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

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

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Refresh your mathematics skills to enhance your understanding of supply chain models and algorithms.

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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

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Curate valuable resources to enhance your learning and stay updated on supply chain data analysis.

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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

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Sharpen your Python skills by solving coding challenges related to data analysis and supply chain management.

Browse courses on Python

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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

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Write a blog post or article on supply chain data analysis

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Share your knowledge and insights by creating content that demonstrates your understanding of supply chain data analysis.

Browse courses on Supply Chain Management

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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

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Expand your knowledge by exploring advanced data analysis techniques applicable to supply chain management.

Browse courses on Data Analysis

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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

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Test your skills and learn from others by participating in a data analysis competition focused on supply chain management.

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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

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Apply your knowledge by designing and implementing a supply chain optimization model to solve a real-world problem.

Browse courses on Supply Chain Optimization

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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

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 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

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.

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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.

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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

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

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.

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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.

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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.

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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.

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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.

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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.

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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