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John Sandall
and
Narayanan Vaidyanathan
Welcome to Machine learning with Python for finance professionals, provided by ACCA (Association of Chartered Certified Accountants), the global body for professional accountants. This course is part of the FinTech for finance and business leaders professional certificate program.
Read more
Welcome to Machine learning with Python for finance professionals, provided by ACCA (Association of Chartered Certified Accountants), the global body for professional accountants. This course is part of the FinTech for finance and business leaders professional certificate program.
Read more
Welcome to Machine learning with Python for finance professionals, provided by ACCA (Association of Chartered Certified Accountants), the global body for professional accountants. This course is part of the FinTech for finance and business leaders professional certificate program.
This course will provide a view of what lies under the surface of a machine learning output, help to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of machine learning. Digital finance knowledge and skills are essential components of the technology transformation as business becomes increasingly customer focused. And having the skills to understand how these technologies are deployed and integrated into a customer centric business strategy is essential. With 16 Jupyter Notebooks available, alongside corresponding solution notebooks, and bonus exercises you will quickly become skilled in specific time-saving Machine Learning tools
- Access to all end of module quizzes
- Access to the final assessment
Register for this course and see more details by visiting:
OpenCourser.com/course/cclq5h/machine
What's inside
Learning objectives
- Apply to real-world machine learning examples to meet practical objectives such as evaluating and improving the model, and error detection/correction.
- An introduction to python starting from initial setup and explaining foundational concepts like data types, variables, mathematical operators, flow control, and functions
- Using python for data analysis including how to load data from different sources, drill down and segment, create pivot table style aggregations and explore data visualisation libraries.
- Automating excel workflows using python to write macros that can be run at the click of a button using the full power of the python eco-system; and to create template reports that update live with the latest data.
- How to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of machine learning
- Understand the basic workings of a machine learning model and its relationship to data science, big data and artificial intelligence.
- Apply to real-world machine learning examples to meet practical objectives such as evaluating and improving the model, and error detection/correction.
- An introduction to python starting from initial setup and explaining foundational concepts like data types, variables, mathematical operators, flow control, and functions
- Using python for data analysis including how to load data from different sources, drill down and segment, create pivot table style aggregations and explore data visualisation libraries.
- Automating excel workflows using python to write macros that can be run at the click of a button using the full power of the python eco-system; and to create template reports that update live with the latest data.
- How to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of machine learning
- Understand the basic workings of a machine learning model and its relationship to data science, big data and artificial intelligence.
Syllabus
Module 1 – Introduction to Python
In this module, the fundamental principles of coding are introduced using the Python programming language. From taking your first steps in coding to understanding data types to control flows, this module provides the essential elements needed for coding in Python.
Read more
Syllabus
Module 1 – Introduction to Python
In this module, the fundamental principles of coding are introduced using the Python programming language. From taking your first steps in coding to understanding data types to control flows, this module provides the essential elements needed for coding in Python.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Well-suited for learners with no prior Python or machine learning experience
Taught by John Sandall and Narayanan Vaidyanathan, who are recognized for their work in machine learning and finance
Develops skills in Python programming, data analysis, machine learning, and Excel automation, which are core skills for finance professionals
Teaches relevant and practical skills for evaluating and improving machine learning models, detecting and correcting errors, and partnering with data scientists
Provides hands-on experience through Jupyter Notebooks, solution notebooks, and bonus exercises, enabling learners to apply their knowledge directly
May require additional resources for learners with limited programming or data analysis experience
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Reviews summary
Practical python and ml for finance
According to learners, this course is a highly practical introduction to Python and machine learning for finance professionals. Students consistently praise its ability to build a strong foundation in Python, even for those with minimal prior coding experience. A standout feature is the module on automating Excel workflows using Python, which many found immediately applicable in their jobs. While the machine learning module is introductory, it is considered perfectly adequate for understanding financial applications and integrating with data scientists. Some experienced learners, however, found the ML content less in-depth than expected, indicating it's best for a high-level overview rather than advanced proficiency.
Jupyter Notebooks and practical exercises reinforce learning effectively.
"The Jupyter notebooks were incredibly helpful and practical."
"The hands-on coding and projects are the strongest part of the course for me."
"The Jupyter notebooks and practical exercises reinforce learning effectively."
"The hands-on labs were fantastic and really helped cement the concepts."
Excel automation with Python is a standout, highly practical skill.
"The module on automating Excel with xlwings was a game-changer."
"The automation of Excel was a stand-out for me. I can immediately apply these skills in my job."
"I appreciated how it helped automate commonly repeated Excel workflows."
"Learning to control Excel from Python and automate reporting was incredibly useful."
Provides a clear and strong foundation in Python for beginners.
"I came in with very little Python experience, and Module 1 built a strong foundation."
"The initial Python modules were clear and easy to follow."
"Even without much coding background, I found it manageable thanks to the structured approach."
"Very strong on the Python fundamentals and data analysis for finance."
Equips finance professionals with immediately applicable Python skills.
"As a finance professional with basic Python knowledge, this course was exactly what I needed."
"I can immediately apply these skills in my job."
"This course truly bridges the gap between finance and data science."
"I learned so much about applying Python in my daily finance tasks."
Pace may be inconsistent, depending on prior Python experience.
"I found the initial Python parts too slow as I already had some programming experience."
"The pace was also inconsistent, with some modules being very slow and others extremely fast."
"If you're a beginner, it might be fine, but for intermediate users, it felt too superficial."
Covers machine learning conceptually but lacks advanced detail.
"The machine learning part felt a bit rushed, especially if you have no prior ML background."
"The ML concepts were too shallow for someone already familiar with Python and looking to deepen their ML skills."
"I was disappointed with the machine learning content; it barely scratches the surface."
"The ML part, which I was most interested in, was too brief and didn't go into enough detail."
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 Machine learning with Python for finance professionals with these
activities:
Review probability and statistics concepts
Show steps
A strong understanding of probability and statistics is crucial for understanding machine learning algorithms in this course.
Browse courses on
Probability
Show steps
-
Review notes, textbooks, or online resources on probability and statistics
-
Solve practice problems and exercises to test understanding
Review Python data types and operators
Show steps
Reviewing these core concepts will help improve ability to use Python for data analysis and machine learning in this course.
Browse courses on
Python
Show steps
-
Revisit course notes or online resources on Python data types and operators
-
Practice writing simple Python programs to manipulate and operate on data
Solve coding problems on LeetCode
Show steps
Regular practice with coding problems will strengthen programming skills and problem-solving abilities relevant to this course.
Show steps
-
Solve LeetCode coding problems related to topics covered in the course
-
Review solutions and discuss approaches with peers or online communities
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a simple machine learning model with scikit-learn
Show steps
Hands-on experience with scikit-learn will enhance understanding of machine learning model development and implementation.
Browse courses on
scikit-learn
Show steps
-
Follow a guided tutorial on building a machine learning model using scikit-learn
-
Modify and experiment with different model parameters and data to gain insights
Join a study group or online forum for course-related discussions
Show steps
Engaging in discussions and helping others will deepen understanding and foster critical thinking skills.
Show steps
-
Join a study group or online forum dedicated to the course or related topics
-
Actively participate in discussions, ask questions, and share insights
Participate in a Kaggle competition related to course topics
Show steps
Kaggle competitions provide hands-on experience with real-world data and machine learning challenges.
Browse courses on
Kaggle
Show steps
-
Find a Kaggle competition aligned with the course topics
-
Download and explore the competition data
-
Develop and implement a machine learning model to solve the competition task
-
Submit the model and track the results
Write a blog post on a machine learning topic
Show steps
Creating and publishing a blog post will reinforce understanding and provide a platform for sharing knowledge with others.
Browse courses on
Machine Learning
Show steps
-
Choose a topic related to machine learning covered in the course
-
Research and gather information from credible sources
-
Structure and write the blog post in a clear and informative manner
-
Publish and promote the blog post on relevant platforms
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source project provides practical experience and allows for collaboration with the wider machine learning community.
Browse courses on
Open Source
Show steps
-
Identify an open-source machine learning project related to the course topics
-
Review the project documentation and code
-
Contribute to the project by improving documentation, fixing bugs, or adding new features
-
Submit a pull request and collaborate with the project maintainers
Review probability and statistics concepts
Show steps
A strong understanding of probability and statistics is crucial for understanding machine learning algorithms in this course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review notes, textbooks, or online resources on probability and statistics
- Solve practice problems and exercises to test understanding
Review Python data types and operators
Show steps
Reviewing these core concepts will help improve ability to use Python for data analysis and machine learning in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit course notes or online resources on Python data types and operators
- Practice writing simple Python programs to manipulate and operate on data
Solve coding problems on LeetCode
Show steps
Regular practice with coding problems will strengthen programming skills and problem-solving abilities relevant to this course.
Show steps
Show steps
Show steps
- Solve LeetCode coding problems related to topics covered in the course
- Review solutions and discuss approaches with peers or online communities
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a simple machine learning model with scikit-learn
Show steps
Hands-on experience with scikit-learn will enhance understanding of machine learning model development and implementation.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Follow a guided tutorial on building a machine learning model using scikit-learn
- Modify and experiment with different model parameters and data to gain insights
Join a study group or online forum for course-related discussions
Show steps
Engaging in discussions and helping others will deepen understanding and foster critical thinking skills.
Show steps
Show steps
Show steps
- Join a study group or online forum dedicated to the course or related topics
- Actively participate in discussions, ask questions, and share insights
Participate in a Kaggle competition related to course topics
Show steps
Kaggle competitions provide hands-on experience with real-world data and machine learning challenges.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find a Kaggle competition aligned with the course topics
- Download and explore the competition data
- Develop and implement a machine learning model to solve the competition task
- Submit the model and track the results
Write a blog post on a machine learning topic
Show steps
Creating and publishing a blog post will reinforce understanding and provide a platform for sharing knowledge with others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a topic related to machine learning covered in the course
- Research and gather information from credible sources
- Structure and write the blog post in a clear and informative manner
- Publish and promote the blog post on relevant platforms
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source project provides practical experience and allows for collaboration with the wider machine learning community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source machine learning project related to the course topics
- Review the project documentation and code
- Contribute to the project by improving documentation, fixing bugs, or adding new features
- Submit a pull request and collaborate with the project maintainers
Career center
Learners who complete Machine learning with Python for finance professionals will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst examines large amounts of data to uncover trends, patterns, and other useful information. Data Analysts can work in a variety of industries, including finance, healthcare, and retail. This course can help you become a Data Analyst by providing you with the skills you need to clean, analyze, and visualize data. You will also learn how to use Python to automate tasks and create reports.
Financial Analyst
Financial Analyst
A Financial Analyst provides financial advice to individuals and organizations. Financial Analysts can work in a variety of settings, including banks, investment firms, and insurance companies. This course can help you become a Financial Analyst by providing you with the skills you need to understand financial data and make sound investment decisions. You will also learn how to use Python to automate tasks and create reports.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks that could affect an organization. Risk Analysts can work in a variety of industries, including finance, healthcare, and government. This course can help you become a Risk Analyst by providing you with the skills you need to identify, assess, and mitigate risks. You will also learn how to use Python to automate tasks and create reports.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst develops and uses mathematical models to solve problems in business and industry. Operations Research Analysts can work in a variety of industries, including finance, healthcare, and manufacturing. This course can help you become an Operations Research Analyst by providing you with the skills you need to develop and use mathematical models. You will also learn how to use Python to automate tasks and create reports.
Business Analyst
Business Analyst
A Business Analyst helps organizations to improve their performance by identifying and solving problems. Business Analysts can work in a variety of industries, including finance, healthcare, and technology. This course can help you become a Business Analyst by providing you with the skills you need to identify and solve problems. You will also learn how to use Python to automate tasks and create reports.
Data Scientist
Data Scientist
A Data Scientist uses data to solve problems and make predictions. Data Scientists can work in a variety of industries, including finance, healthcare, and technology. This course can help you become a Data Scientist by providing you with the skills you need to collect, analyze, and interpret data. You will also learn how to use Python to build machine learning models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models. Machine Learning Engineers can work in a variety of industries, including finance, healthcare, and technology. This course can help you become a Machine Learning Engineer by providing you with the skills you need to develop and deploy machine learning models. You will also learn how to use Python to automate tasks and create reports.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. Software Engineers can work in a variety of industries, including finance, healthcare, and technology. This course can help you become a Software Engineer by providing you with the skills you need to design, develop, and maintain software applications. You will also learn how to use Python to automate tasks and create reports.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure that is used to store and process data. Data Engineers can work in a variety of industries, including finance, healthcare, and technology. This course can help you become a Data Engineer by providing you with the skills you need to build and maintain data infrastructure. You will also learn how to use Python to automate tasks and create reports.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. Statisticians can work in a variety of industries, including finance, healthcare, and government. This course can help you become a Statistician by providing you with the skills you need to collect, analyze, and interpret data. You will also learn how to use Python to automate tasks and create reports.
Actuary
Actuary
An Actuary uses mathematics to assess financial risk. Actuaries can work in a variety of industries, including insurance, healthcare, and finance. This course may be helpful for those who are interested in becoming an Actuary by providing them with a foundation in mathematics and statistics.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst develops and uses mathematical models to analyze financial data. Quantitative Analysts can work in a variety of industries, including finance, healthcare, and technology. This course may be helpful for those who are interested in becoming a Quantitative Analyst by providing them with a foundation in mathematics and statistics.
Financial Planner
Financial Planner
A Financial Planner helps individuals and families to plan for their financial future. Financial Planners can work in a variety of settings, including banks, investment firms, and independent practices. This course may be helpful for those who are interested in becoming a Financial Planner by providing them with a foundation in financial planning.
Investment Analyst
Investment Analyst
An Investment Analyst researches and recommends investments. Investment Analysts can work in a variety of settings, including banks, investment firms, and pension funds. This course may be helpful for those who are interested in becoming an Investment Analyst by providing them with a foundation in financial analysis.
Personal Financial Advisor
Personal Financial Advisor
A Personal Financial Advisor provides financial advice to individuals and families. Personal Financial Advisors can work in a variety of settings, including banks, credit unions, and independent practices. This course may be helpful for those who are interested in becoming a Personal Financial Advisor by providing them with a foundation in financial planning.
For more career information including salaries, visit:
OpenCourser.com/course/cclq5h/machine
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
Machine learning with Python for finance professionals.
Provides a comprehensive overview of machine learning algorithms and their applications in finance.
Covers data science concepts and techniques relevant to business professionals, including data mining and data visualization.
Provides a practical introduction to Python programming, focusing on automation tasks.
Covers data analysis techniques using the Python programming language.
Introduces machine learning fundamentals using Python, providing a practical approach to building and evaluating models.
Provides a quick and practical introduction to Python programming.
Provides a comprehensive overview of statistical learning methods, including machine learning algorithms.
Provides practical guidance on implementing machine learning algorithms using popular Python libraries.
Covers advanced machine learning topics, including deep learning and neural networks.
Focuses on using Python for financial data analysis and algorithmic trading.
Provides a comprehensive introduction to data science and machine learning from scratch, using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cclq5h/machine
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Rating
4.1
Effort
4 to 5 hours per week for 5 weeks
Level
Intermediate
Via
edX
Institution
ACCA
Instructors
John Sandall
Narayanan Vaidyanathan
Language
English
Transcript
English
This course belongs to a
collection
called
FinTech for Finance and Business Leaders. It's comprised of three courses:
- Machine learning with Python for finance professionals (viewing)
- Manage the cyber threat for finance professionals
- Robotic process and intelligent automation for finance
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