Machine learning with Python for finance professionals from edX

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

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.

Topics covered: ****

● Introduction to the Python programming language

● Using the Jupyter Notebook environment to run Python code

● Data types: strings, integers and floats; how information is created in Python

● Variables and containers: how information is stored in Python

● Mathematical operators and calculation in Python

● Control flows, logic and writing functions: how to automate processes using code.

Module 2 – Python for Data Analysis ****

Learn the basics of using Python for working with data. This module introduces pandas, a Python library that is widely used for powerful yet easy data manipulation. Learn to load data from different sources, drill down and segment, create pivot table style aggregations and explore various data visualisation libraries.

● Introduction to working with third-party Python packages

● Working efficiently with large datasets using NumPy for numerical analysis

● Using pandas to read in, manipulate and analyse data

● Data visualisation using matplotlib and Seaborn

● Merging datasets and techniques for handling missing data.

Module 3 – Automating Excel using Python ****

Automation helps businesses to make regular reporting more efficient. In this module you will learn to automate commonly repeated Excel workflows using the xlwings Python library. Learn how to control Excel from Python and create template reports that update live with the latest data.

Topics covered :

● Introduction to xlwings: a Python library for interacting with live Excel spreadsheets

● Using pandas and xlwings to automate the generation of Excel-based business intelligence reporting

● Learn to automate Excel and give your Excel-based workflows access to the full power of the Python data scientific ecosystem of tools

Module 4 *– * Machine learning with Python

Introductory hands-on module covering the essentials of implementing a real-world machine learning project. Understand the basics of ML theory and its relationship to data science, Big Data and Artificial Intelligence.

Topics covered:

Build a classifier algorithm for RFM modelling

Build a machine learning system for category classification using decision trees, random forests and natural language processing

Learn how to evaluate and tune machine learning algorithms, and how to prevent overfitting using cross-validation.

Good to know

Know what's good

, what to watch for

, 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

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

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A strong understanding of probability and statistics is crucial for understanding machine learning algorithms in this course.

Browse courses on Probability

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Review notes, textbooks, or online resources on probability and statistics
Solve practice problems and exercises to test understanding

Review Python data types and operators

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Reviewing these core concepts will help improve ability to use Python for data analysis and machine learning in this course.

Browse courses on Python

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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

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Regular practice with coding problems will strengthen programming skills and problem-solving abilities relevant to this course.

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Solve LeetCode coding problems related to topics covered in the course
Review solutions and discuss approaches with peers or online communities

Five other activities

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Build a simple machine learning model with scikit-learn

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Hands-on experience with scikit-learn will enhance understanding of machine learning model development and implementation.

Browse courses on scikit-learn

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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

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Engaging in discussions and helping others will deepen understanding and foster critical thinking skills.

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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

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Kaggle competitions provide hands-on experience with real-world data and machine learning challenges.

Browse courses on Kaggle

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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

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Creating and publishing a blog post will reinforce understanding and provide a platform for sharing knowledge with others.

Browse courses on Machine Learning

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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

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Contributing to an open-source project provides practical experience and allows for collaboration with the wider machine learning community.

Browse courses on Open Source

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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

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Risk 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.

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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.

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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.

See salaries and explore the career path for Data Scientist

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Personal Financial Advisor