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Python and Statistics for Financial Analysis

Approx. 13 hours to complete

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Course Summary

This course teaches Python programming, statistical analysis, and financial concepts to help you make informed financial decisions. You will learn how to analyze financial data, create visualizations, and build financial models using Python.

Key Learning Points

Learn Python programming for financial analysis
Understand statistical analysis and visualization techniques
Apply financial concepts to make informed decisions

Learning Outcomes

Use Python programming for financial analysis
Analyze financial data and create visualizations
Apply financial concepts to make informed decisions

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Basic understanding of financial concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Hands-on exercises
Real-world case studies

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Description

Course Overview: https://youtu.be/JgFV5qzAYno

Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data. By the end of the course, you can achieve the following using python: - Import, pre-process, save and visualize financial data into pandas Dataframe - Manipulate the existing financial data by generating new variables using multiple columns - Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts - Build a trading model using multiple linear regression model - Evaluate the performance of the trading model using different investment indicators Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.

Outline

Visualizing and Munging Stock Data
Course overview
1.0 Module Introduction
1.1 Packages for Data Analysis
1.2 Importing data
1.3 Basics of Dataframe
1.4 Generate new variables in Dataframe
1.5 Trading Strategy
Grading Criteria
Getting started with Jupyter Notebook
pd.read_csv or pd.DataFrame.from_csv
Quiz 1

Random variables and distribution
2.0 Module Introduction
2.1 Outcomes and Random Variables
2.2 Frequency and Distributions
2.3 Models of Distribution
Quiz 2

Sampling and Inference
3.0 Introduction
3.1 Population and Sample
3.2 Variation of Sample
3.3 Confidence Interval
3.4 Hypothesis Testing
P-value
Quiz 3

Linear Regression Models for Financial Analysis
4.0 Introduction
4.1 Association of random variables
4.2 Simple linear regression model
4.3 Diagnostic of linear regression model
4.4 Multiple linear regression model
4.5 Evaluate the strategy
Please rate this course!
Quiz 4
Post-course survey

Summary of User Reviews

Discover the power of Python and statistics for financial analysis with this highly-rated course on Coursera. Learn how to apply statistical techniques to real-world financial data and make informed decisions. Users rave about the hands-on approach and practical examples provided throughout the course.

Key Aspect Users Liked About This Course

The practical examples and hands-on approach were highly praised by many users.

Pros from User Reviews

The course content is well-structured and easy to follow.
The instructors are knowledgeable and engaging.
The practical examples and exercises help reinforce learning.
The course is suitable for beginners and experienced users alike.
The skills learned in this course are highly applicable in the finance industry.

Cons from User Reviews

Some users found the pace of the course to be too slow.
A few users felt that the course could benefit from more advanced topics.
The course does not cover all aspects of financial analysis.
The examples provided are somewhat simplistic and may not reflect real-world scenarios.
The course may be too basic for those with prior experience in Python or statistics.

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