Statistics for Machine Learning for Investment Professionals from Coursera

One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary). The motivation is to demonstrate the elegance — and speed — simple programming brings to the investment decision-making process. The reading material in this course offers in-practice insights curated from the blogs of CFA Institute as well as other leading publications. After taking this course you will be able to: - Describe the importance of identifying information patterns for building models - Explain probability concepts for solving investing problems - Explain the use of linear regression and interpret related Python and R code - Describe gradient descent, explain logistic regression, and interpret Python and R code - Describe the characteristics and uses of time-series models This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by industry experts CFA, Shrivenivas Kunte and CIPM

Introduces learners to machine learning in finance

Emphasizes practical applications and real-world examples

Offers hands-on experience with Python and R programming

Part of a specialization in data science for investment professionals

Requires some knowledge of the investment industry

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 Statistics for Machine Learning for Investment Professionals with these activities:

Seek guidance from industry professionals

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Accelerate your progress by connecting with experienced professionals in the field of machine learning and investment

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Research and identify potential mentors who align with your interests
Reach out to your chosen mentors and request guidance

Review Probability and Statistics Concepts

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Refresh your understanding of foundational statistical concepts to enhance your grasp of machine learning techniques

Browse courses on Probability

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Review key probability concepts, such as conditional probability and Bayes' theorem
Recall basic statistical measures, including mean, median, and standard deviation

Practice Linear Regression and Logistic Regression

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Reinforce your understanding of linear regression and logistic regression through focused practice

Browse courses on Linear Regression

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Solve 10 practice problems on linear regression
Apply linear regression to a real-world data set
Solve 5 practice problems on logistic regression
Apply logistic regression to a real-world data set

Four other activities

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Follow tutorials on gradient descent and optimization algorithms

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Deepen your understanding of optimization algorithms and their role in machine learning

Browse courses on Gradient Descent

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Find online tutorials or courses that cover gradient descent and optimization algorithms
Follow the tutorials and complete the exercises to gain practical experience

Review 'Machine Learning for Investment Professionals'

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Strengthen your foundation in machine learning and its applications in investment decision-making

View Advances in Financial Machine Learning on Amazon

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Read Chapters 1-3 to understand the basics of machine learning
Work through the examples and exercises in Chapters 4-6 to practice applying machine learning techniques

Develop a Python project using a time-series model

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Enhance your practical skills and deepen your understanding of time-series modeling by creating a project that utilizes Python

Browse courses on Python

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Identify a real-world data set suitable for time-series analysis
Choose and apply appropriate time-series models to the data set
Evaluate the performance of your models and present your findings

Contribute to an open-source machine learning project

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Expand your knowledge and gain hands-on experience by actively participating in the open-source machine learning community

Browse courses on Machine Learning

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Identify a project that aligns with your interests and skills
Contribute to the project by reporting bugs, suggesting improvements, or writing code

Career center

Learners who complete Statistics for Machine Learning for Investment Professionals will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use data to collect, analyze, interpret, and present data. The course, Statistics for Machine Learning for Investment Professionals, provides a strong foundation in foundational statistical concepts and advanced AI techniques used by Statisticians. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Statistics.

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Machine Learning Engineer

Machine Learning Engineers use data to build and deploy machine learning models. The course, Statistics for Machine Learning for Investment Professionals, provides a strong foundation in foundational statistical concepts and advanced AI techniques used by Machine Learning Engineers. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Machine Learning Engineering.

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Quantitative Analyst

Quantitative Analysts (QAs) use mathematical and statistical models to analyze financial data and make investment decisions. The course, Statistics for Machine Learning for Investment Professionals, provides a strong foundation in foundational statistical concepts and advanced AI techniques used by QAs. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Quantitative Analysis.

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Investment Analyst

Investment Analysts use data to make investment recommendations and decisions. The course, Statistics for Machine Learning for Investment Professionals, provides a strong foundation in foundational statistical concepts and advanced AI techniques used by Investment Analysts. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Investment Analysis.

See salaries and explore the career path for Investment Analyst

Financial Analyst

Financial Analysts use data to make investment recommendations and decisions. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Financial Analysts. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Financial Analysis.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use data to identify and assess financial risks. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Risk Analysts. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Risk Analysis.

See salaries and explore the career path for Risk Analyst

Portfolio Manager

Portfolio Managers use data to make investment decisions and manage investment portfolios. The course, Statistics for Machine Learning for Investment Professionals, provides a strong foundation in foundational statistical concepts and advanced AI techniques used by Portfolio Managers. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Portfolio Management.

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Data Analyst

Data Analysts use data to gather, clean, and analyze data. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Data Analysts. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Data Analysis.

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Financial Consultant

Financial Advisors use data to provide financial advice to clients. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Financial Advisors. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Financial Consulting.

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Actuary

Actuaries use data to assess financial risks and make recommendations on insurance policies. The course, Statistics for Machine Learning for Investment Professionals, provides a strong foundation in foundational statistical concepts and advanced AI techniques used by Actuaries. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Actuarial Science.

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Business Analyst

Business Analysts use data to analyze business processes and make recommendations for improvements. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Business Analysts. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Business Analysis.

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Data Scientist

Data Scientists gather information from diverse sources, using statistical techniques to classify and analyze data. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts underpinning machine learning, and demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. This course may be useful for those seeking to enter the field of Data Science, as it introduces key concepts and techniques used by Data Scientists.

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Data Architect

Data Architects use data to design and build data systems. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Data Architects. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Data Architecture.

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Operations Analyst

Operations Analysts use data to analyze business operations and make recommendations for improvements. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Operations Analysts. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Operations Analysis.

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Software Engineer

Software Engineers use data to build and deploy software applications. The course, Statistics for Machine Learning for Investment Professionals, provides a solid foundation in foundational statistical concepts and advanced AI techniques used by Software Engineers. The course also demonstrates core modeling frameworks along with carefully selected real-world investment practice examples, making it particularly relevant for those seeking to enter the field of Software Engineering.

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