Python and Statistics for Financial Analysis
Learn Python for financial data analysis: Import, process, and visualize stock data using pandas and statistical concepts.
Learn Python for financial data analysis: Import, process, and visualize stock data using pandas and statistical concepts.
This course combines Python programming with statistical concepts to analyze financial data, particularly stock market information. Students will learn to use Python's pandas library to import, pre-process, and visualize financial data. The curriculum covers key statistical concepts such as random variables, distributions, sampling, inference, and linear regression, all applied to financial contexts. Participants will build a trading model using multiple linear regression and evaluate its performance using investment indicators. The course is designed for intermediate-level learners with basic probability knowledge and includes hands-on practice in Jupyter Notebooks. By the end, students will be able to manipulate financial data, apply statistical methods, and create basic trading models using Python.
4.4
(4,208 ratings)
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Import, pre-process, and visualize financial data using pandas in Python
Manipulate stock data to generate new variables for analysis
Apply statistical concepts such as random variables and distributions to financial data
Build and interpret confidence intervals for stock returns
Perform hypothesis testing on investment claims
Develop a trading model using multiple linear regression
Skills you'll gain
This course includes:
2.1 Hours PreRecorded video
5 quizzes,16 ungraded labs
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course offers a comprehensive introduction to using Python for financial data analysis. The curriculum is structured into four modules, each focusing on key aspects of data analysis and statistical concepts. Students begin by learning to import, manipulate, and visualize stock data using Python's pandas library. They then explore fundamental statistical concepts such as random variables, distributions, and probability, applying these to financial contexts. The course progresses to cover sampling and inference, including confidence intervals and hypothesis testing. In the final module, students learn about linear regression models and their application in building and evaluating trading strategies. Throughout the course, participants engage in hands-on coding exercises using Jupyter Notebooks, ensuring practical application of the concepts learned.
Visualizing and Munging Stock Data
Module 1 · 3 Hours to complete
Random variables and distribution
Module 2 · 2 Hours to complete
Sampling and Inference
Module 3 · 3 Hours to complete
Linear Regression Models for Financial Analysis
Module 4 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Associate Professor
Xuhu Wan is an Associate Professor of Statistics in the Department of Information Systems, Business Statistics, and Operation Management at the Hong Kong University of Science and Technology (HKUST). He earned his Ph.D. in Financial Mathematics from the University of Southern California in 2005. His primary research focuses on dynamic contract theory and information design, with additional interests in parallel and distributed computing as well as low-latency programming.
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4.4 course rating
4,208 ratings
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