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Statistics for Machine Learning for Investment Professionals

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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.

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Dive into the "Statistics for Machine Learning for Investment Professionals" course, designed for those in the investment industry. This foundational course, instructed by the CFA Institute on Coursera, covers essential statistical concepts and advanced AI techniques crucial for investment decision-making. Over 1080 minutes, learn to leverage Python and R to identify information patterns, solve investing problems, and interpret various models. Perfect for investment professionals looking to enhance their data science skills, the course is available through a Starter subscription.