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.
During this online course, you will have the opportunity to gain practical experience through immersive code labs that draw on real-world scenarios.
Course modules include:
- Module 1: Measures of Central Tendency
- Module 2: Measures of Dispersion
- Module 3: Introduction to Distributions
- Module 4: Data Visualization Techniques
- Module 5: Sampling Theory
- Module 6: Hypothesis Testing
Prerequisites
This course is part of a data science series. It is suggested, but not required, that learners complete the courses in the recommended order below to ensure uniform foundational knowledge.
Recommended order:
- Data and Statistics Foundation for Investment Professionals
- Statistics for Machine Learning for Investment Professionals
- Machine Learning for Investment Professionals
- Natural Language Processing for Investment Professionals
- Mitigating Biases in the Data Science Pipeline for Investment Professionals
What You’ll Learn
Upon completion of this online course you will be able to:
- Explain basic statistical measures and their application to real-life data sets
- Calculate and interpret the measures of dispersion and explain the deviations from a normal distribution
- Understand the use and appropriateness of different distributions
- Compare and contrast the 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
Topics Covered
- Fintech
- Quantitative Methods
