Data Science for Investment Professionals 專項課程

Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.

In this course you will learn how to: - Explain basic statistical measures and their application to real-life data sets - Calculate and interpret measures of dispersion and explain deviations from a normal distribution - Understand the use and appropriateness of different distributions - Compare and contrast ways of visualizing data and create them using Python (no prior knowledge of Python necessary) - Explain sampling theory and draw inferences about population parameters from sample statistics - Formulate hypotheses on investment problems This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.

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.