Coursera

Statistics for Machine Learning for Investment Professionals (Coursera)

Offered by CFA Institute,
Statistics for Machine Learning for Investment Professionals (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.

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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

Course 2 of 3 in the Data Science for Investment Professionals Specialization.
What You Will Learn

  • Understand the importance of information patterns and the concept of causality in decision making models
  • Assess and apply probability concepts to real-world investing scenarios
  • Describe and use linear and logistic regression modeling for investment decision making
  • Compare simple time-series models and understand their limitations

Syllabus

WEEK 1: Welcome to Statistics for Machine Learning for Investment Professionals; Data and Patterns
WEEK 2: Randomness and Probability
WEEK 3: Linear Regression
WEEK 4: Advanced Regression Concepts
WEEK 5: Time-Series Analysis
WEEK 6: Final Assessment

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