Python and Statistics for Financial Analysis from Coursera

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.

What's inside

Syllabus

Visualizing and Munging Stock Data

Why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? What makes Python one of the most popular tools for financial analysis? You are going to learn basic python to import, manipulate and visualize stock data in this module. As Python is highly readable and simple enough, you can build one of the most popular trading models - Trend following strategy by the end of this module!

Random variables and distribution

In the previous module, we built a simple trading strategy base on Moving Average 10 and 50, which are "random variables" in statistics. In this module, we are going to explore basic concepts of random variables. By understanding the frequency and distribution of random variables, we extend further to the discussion of probability. In the later part of the module, we apply the probability concept in measuring the risk of investing a stock by looking at the distribution of log daily return using python. Learners are expected to have basic knowledge of probability before taking this module.

Sampling and Inference

In financial analysis, we always infer the real mean return of stocks, or equity funds, based on the historical data of a couple years. This situation is in line with a core part of statistics - Statistical Inference - which we also base on sample data to infer the population of a target variable.In this module, you are going to understand the basic concept of statistical inference such as population, samples and random sampling. In the second part of the module, we shall estimate the range of mean return of a stock using a concept called confidence interval, after we understand the distribution of sample mean.We will also testify the claim of investment return using another statistical concept - hypothesis testing.

Linear Regression Models for Financial Analysis

In this module, we will explore the most often used prediction method - linear regression. From learning the association of random variables to simple and multiple linear regression model, we finally come to the most interesting part of this course: we will build a model using multiple indices from the global markets and predict the price change of an ETF of S&P500. In addition to building a stock trading model, it is also great fun to test the performance of your own models, which I will also show you how to evaluate them!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a strong foundation for beginners who want to learn how to analyze financial data using Python

Develops skills in importing, pre-processing, and visualizing financial data

Teachers how to manipulate existing financial data to generate new variables

Explores important statistical concepts, including random variables, distributions, and hypothesis testing

Covers how to build trading models using multiple linear regression

Teaches how to evaluate the performance of trading models using investment indicators

Reviews summary

Python for financial analysis

Learners say this four-week course on Python for Financial Analysis is largely positive. The instructor is engaging and clearly explains advanced statistical concepts, resulting in a good balance between difficulty and comprehensibility. Some learners note the course could be paced more evenly, saying the last week's material is noticeably more demanding. Overall, learners recommend this course to those with some basic knowledge of Python, statistics, and finance. Those without this background knowledge may find it difficult to follow. Here are some of the key takeaways from reviews: * **Concepts**: * Statistical analysis and modeling * Time series analysis * Asset valuation * **Skills**: * Implementing financial analysis with Python libraries like Pandas, NumPy, and Matplotlib * **Prerequisites**: * Basic knowledge of Python, statistics, and finance is recommended * **Workload**: Students report spending approximately 5-10 hours per week on the course * **Certificate**: A certificate of completion is available for purchase upon completion of the course

The pace of the course is largely positive, with some learners noting that the last week is more demanding than the first three.

"You get insight in finance, statistics and python. But it is not a pure python course, so you would want to find something else if you want to learn particularly python."

"The course has offer me a insight in Python in Statistics and how I can implement in the field of Finance.Overall difficulty was moderate to high, Week 4 was way to difficulty, I would suggest that a person with Knowledge on Statistics should apply to this course"

The instructor's expertise and communication skills are largely positive.

"Very good course! Explanations are clear and the application via the Jupiter notebooks are excellent.The accent is sometimes a bit difficult to follow but it's OK, the instructor did overall an excellent job."

"In overall great course! As a Data Engineer and a Economics major I thought the course would be a little bit better, but in overall it covers all the minimal aspects of econometrics and basic python manipulation. If you never used Data at some big level or econometrics this course is for you, because covers all the basic that you learn in college"

The integration of Python coding within the lessons for financial modeling is largely positive, but many learners note that some prior Python knowledge is helpful.

"Python and Statistics for Financial Analysis. Below is data about the 811 reviews written for a course titled"

"This course is very good for those of you who want a career in financial analysis, For starters it might be a little difficult and already have to understand her tools first. For the whole course process. Very good."

"Overall good, the professor is delicated and responds to the forum actively. But the course could be better designed. Even though I have learned the knowledge of statistics, econometric, and python and got a 100% certificate, the course is still difficult for me to digest. I have to pause the video and think 5-8 times per video. The pace is so fast that some usages of the python or applicaions of finance equations lack sufficient illustration."

Students say the implementation of statistical models and using them to derive actionable insights is largely positive.

"Very good course which gives a good basis on statistics. Thank you for enriching our knowledge, I am really delighted to have made your acquaintance my professor."

"The most impressive part was how concisely the statistic concepts was explained."

Many students say that this course requires students to have some prior statistical knowledge and Python experience to fully understand the content.

"It assumes that this course is a review but then took me into places I hadn't been yet. I'm still going over it."

"I would recommend to have a little basic finance background and to have some ideas about statistics as these concepts are only vaguely explained during the course."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python and Statistics for Financial Analysis with these activities:

Review key concepts of probability

Show steps

Reviewing key concepts of probability will help you understand the statistical concepts applied in financial data analysis.

Browse courses on Probability

Show steps

Re-familiarize yourself with random variables and their distributions.
Review the concept of probability and its applications in finance.

Connect with professionals in the financial industry

Show steps

Connecting with professionals will provide valuable insights and guidance in the financial industry.

Browse courses on Networking

Show steps

Attend industry events or conferences to meet financial professionals.
Reach out to professionals through LinkedIn or other online platforms.
Seek out mentors who can guide you in your career.

Practice Data Munging

Show steps

Build a solid foundation in data manipulation by practicing data munging exercises. This will help you navigate and handle financial data more efficiently in the course.

Browse courses on Python

Show steps

Import a stock dataset into a Pandas DataFrame.
Cleanse the data by removing duplicates and handling missing values.
Calculate new features from existing columns.
Visualize the data to identify trends and patterns.

Ten other activities

Expand to see all activities and additional details

Show all 13 activities

Learn about Random Variables and Distribution

Show steps

Develop a deeper understanding of random variables and their distribution. This knowledge will enable you to analyze and interpret financial data more effectively.

Browse courses on Random Variables

Show steps

Review the concepts of probability and frequency.
Explore different types of random variables, such as discrete and continuous variables.
Calculate the probability of an event using probability distribution functions.
Apply these concepts to analyze the distribution of stock returns.

Practice using Python for data manipulation

Show steps

Practicing Python coding will help you master the data manipulation skills needed for financial analysis.

Browse courses on Python

Show steps

Follow online tutorials or documentation to learn the basics of Python.
Complete coding exercises to practice data manipulation tasks.

Analyze Stock Data

Show steps

Practice importing, manipulating, and visualizing stock data using Python to solidify your understanding of data analysis techniques.

Show steps

Import and load stock data from a data source.
Clean and preprocess the data to remove outliers and missing values.
Create visualizations to explore the distribution and trends in the data using Python libraries.

Assist other students with financial data analysis

Show steps

Helping others will strengthen your understanding of financial data analysis concepts.

Browse courses on Data Analysis

Show steps

Join online forums or discussion boards related to financial data analysis.
Participate in mentoring programs or volunteer your time to assist students.

Prepare a presentation on financial data analysis techniques

Show steps

Preparing a presentation will help you synthesize your understanding of financial data analysis techniques and practice your communication skills.

Browse courses on Data Analysis

Show steps

Research and gather information on financial data analysis techniques.
Develop a clear and concise presentation structure.
Practice your presentation and seek feedback from others.

Build a Trading Model Using Linear Regression

Show steps

Gain hands-on experience in building a trading model. This practical activity will enhance your ability to make informed investment decisions.

Browse courses on Linear Regression

Show steps

Choose a stock or ETF to predict its price movement.
Identify relevant indices from global markets as predictor variables.
Build a linear regression model using these indices.
Evaluate the performance of the model using metrics such as R-squared and mean absolute error.
Make predictions on future price movements based on the model.

Build a Stock Trading Model

Show steps

Develop a stock trading model using multiple linear regression to predict stock price changes and evaluate its performance using investment indicators.

Browse courses on Python

Show steps

Identify relevant financial indicators and features for model inputs.
Train a multiple linear regression model using historical data.
Evaluate the model's performance using metrics such as R-squared, Sharpe ratio, and return on investment.

Explore additional resources on financial modeling

Show steps

Exploring additional resources will expand your knowledge and understanding of financial modeling techniques.

Browse courses on Financial Modeling

Show steps

Search for online courses, articles, or books on financial modeling.
Attend webinars or workshops on financial modeling.

Contribute to open-source projects in financial data analysis

Show steps

Contributing to open-source projects will enhance your skills and knowledge in financial data analysis.

Browse courses on Open Source

Show steps

Identify open-source projects in the financial data analysis domain.
Review the codebase and identify areas where you can contribute.
Submit your contributions and engage with the project community.

Build a simple financial trading model

Show steps

Building a simple trading model will reinforce your understanding of the concepts covered in the course.

Show steps

Identify a suitable dataset and features for your model.
Choose and implement a machine learning algorithm for your model.
Evaluate the performance of your model using appropriate metrics.

Career center

Learners who complete Python and Statistics for Financial Analysis will develop knowledge and skills that may be useful to these careers:

Financial Analyst

Financial Analysts use statistical instruments to advise individuals and organizations on investment decisions. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build trading models and evaluate their performance using different investment indicators, both of which are tasks within the 'Financial Analyst' job function.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use statistical instruments to evaluate risk in investments. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build trading models and evaluate their performance using different investment indicators, both of which are tasks within the 'Quantitative Analyst' job function.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts use statistical instruments to solve business problems. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Data Analyst' job function.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers use statistical instruments to build and maintain machine learning models. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Machine Learning Engineer' job function.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use statistical instruments to solve business problems. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Data Scientist' job function.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians apply statistical techniques to collect, analyze, interpret, and present data. This course will help you to develop the skills you need to be successful in this role, including how to use Python to import, manipulate, and visualize data, as well as how to apply statistical concepts to real-world problems.

See salaries and explore the career path for Statistician

Financial Risk Manager

Financial Risk Managers assess and manage the financial risks faced by organizations. Those individuals who take this course will learn essential Python coding and statistical concepts which they can apply to this role. Specifically, they will learn how to build trading models and evaluate their performance using different investment indicators.

See salaries and explore the career path for Financial Risk Manager

Investment Analyst

Investment Analysts evaluate and research investments. Those individuals who take this course will learn essential Python coding and statistical concepts which they can apply to this role. Specifically, they will learn how to build trading models and evaluate their performance using different investment indicators.

See salaries and explore the career path for Investment Analyst

Actuary

Actuaries use statistical instruments to assess and manage financial risks faced by individuals and organizations. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Actuary' job function.

See salaries and explore the career path for Actuary

Software Engineer

Software Engineers design, develop, and maintain software systems. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, similar to how they would have to in the 'Software Engineer' job function.

See salaries and explore the career path for Software Engineer

Market Researcher

Market Researchers use statistical instruments to collect and analyze data about markets and consumers. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Market Researcher' job function.

See salaries and explore the career path for Market Researcher

Business Analyst

Business Analysts use statistical instruments to solve business problems. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Business Analyst' job function.

See salaries and explore the career path for Business Analyst

Economist

Economists use statistical instruments to analyze economic data. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Economist' job function.

See salaries and explore the career path for Economist

Data Engineer

Data Engineers design and implement data pipelines and systems. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Data Engineer' job function.

See salaries and explore the career path for Data Engineer

Financial Planner

Financial Planners use statistical instruments to help individuals and organizations plan for their financial futures. Those individuals who take this course will learn essential Python coding and statistical concepts that will help them analyze complex datasets, similar to how they would have to on the job. They will learn how to build models and evaluate their performance using different indicators, both of which are tasks within the 'Financial Planner' job function.

See salaries and explore the career path for Financial Planner