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Ryan Ahmed
In this hands-on guided project, we will use the power of python to perform stock data visualization and stock return calculation. We will analyze the performance of following companies: Facebook, Netflix and Twitter over the past 7 years. Also, we will...
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In this hands-on guided project, we will use the power of python to perform stock data visualization and stock return calculation. We will analyze the performance of following companies: Facebook, Netflix and Twitter over the past 7 years. Also, we will analyze the stocks daily returns and study the correlations between various stocks in a portfolio. This project is crucial for investors who want to properly manage their portfolios, visualize datasets, find useful patterns, and gain valuable insights such as stock daily returns and risks. This project could be practically used for analyzing company stocks, indices and currencies. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Develops portfolio management skills and risk assessment, which are essential for investors
Covers a variety of stocks in a range of industries, providing a broad perspective on stock market analysis
Taught by Ryan Ahmed, an experienced instructor in stock market analysis
Provides hands-on exercises using Python, a widely-used programming language in finance
Suitable for students with a basic understanding of Python and finance concepts

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Reviews summary

Python financial analysis

The course is a good introduction to Python for financial analysis, but it can be improved by providing more up-to-date content and by having the instructor answer questions in the forum.
The course is easy to understand and follow along with.
"Pretty easy. Get you to practice with Pandas dataframe and plot."
The course content is not up-to-date.
The instructor does not answer questions in the forum.
"The instructor doesn't answer questions in the forum."

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 A Beginners Guide to Python for Financial Analysis with these activities:
Review Python programming basics
Refreshing your Python programming skills will ensure that you have a solid foundation for the course materials.
Browse courses on Python
Show steps
  • Review online tutorials or textbooks on Python basics.
  • Complete practice problems to test your understanding.
Complete online tutorials on Python data visualization
Completing tutorials will give you hands-on experience with the tools and techniques used in the course.
Browse courses on Python
Show steps
  • Find tutorials from reputable sources such as Coursera or edX.
  • Follow the tutorials step-by-step to create your own visualizations.
  • Experiment with different visualization techniques to find the ones that work best for your data.
Join a study group or online forum
Participating in a study group or online forum will allow you to connect with other students, share knowledge, and get support.
Browse courses on Collaboration
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  • Find a study group or online forum that aligns with your goals.
  • Participate actively in discussions and ask questions.
  • Help other students by answering their questions and sharing your insights.
Four other activities
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Show all seven activities
Solve practice problems on stock return calculation
Solving practice problems will help you master the formulas and techniques used to calculate stock returns.
Show steps
  • Find practice problems from textbooks, online resources, or your instructor.
  • Attempt to solve the problems on your own.
  • Check your solutions against the provided answers or ask your instructor for feedback.
Create a comprehensive study guide
Creating a comprehensive study guide will help you organize and retain the information covered in the course.
Browse courses on Study Skills
Show steps
  • Gather notes, handouts, and other course materials.
  • Organize the materials into a logical structure.
  • Summarize the key concepts and formulas.
  • Review the study guide regularly to reinforce your learning.
Create a portfolio tracker
Creating a portfolio tracker will allow you to apply the skills you learn in the course to a real-world scenario.
Browse courses on Portfolio Management
Show steps
  • Gather data on your investments, including stock prices, dividends, and fees.
  • Choose or develop a tool for tracking your portfolio.
  • Regularly update your portfolio tracker with the latest data.
  • Analyze your portfolio performance and make adjustments as needed.
Develop a presentation on stock market analysis
Creating a presentation will allow you to synthesize your knowledge and communicate it effectively.
Browse courses on Presentation Skills
Show steps
  • Choose a topic for your presentation.
  • Research and gather data on your topic.
  • Create slides that visually represent your findings.
  • Practice your presentation and deliver it to an audience.

Career center

Learners who complete A Beginners Guide to Python for Financial Analysis will develop knowledge and skills that may be useful to these careers:
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Python skills from this course are essential for the work they do. With Python, they can automate tasks, build models, and perform complex calculations. This course's focus on using Python for financial analysis makes it a perfect fit for those aspiring to be Quantitative Analysts.
Financial Analyst
Financial Analysts study historical prices, earning reports, and economic data to make predictions about future stock prices. This course provides a strong foundation in Python essential for this work. With Python, they analyze large datasets, create visualizations, build models, and more. This course's focus on stock data visualization and stock return calculation directly aligns with the responsibilities of a Financial Analyst.
Investment Manager
Investment Managers make investment decisions on behalf of their clients. They use a variety of tools and techniques to analyze investment opportunities. This course provides a strong foundation in Python, which Investment Managers can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Investment Managers.
Financial Modeler
Financial Modelers build financial models to help companies make investment decisions. They use a variety of tools and techniques to create models that can be used to forecast financial performance. This course provides a strong foundation in Python, which Financial Modelers can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Financial Modelers.
Risk Manager
Risk Managers assess and manage financial risks. They use a variety of tools and techniques to identify and mitigate risks. This course provides a strong foundation in Python, which Risk Managers can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Risk Managers.
Data Analyst
Data Analysts clean, process, and analyze data to extract insights and inform decision-making. Python is a widely used tool for Data Analysts, and this course provides a strong foundation in its use. The course's focus on stock data visualization and stock return calculation makes it particularly relevant for Data Analysts working in the financial sector.
Portfolio Manager
Portfolio Managers manage investment portfolios for their clients. They use a variety of tools and techniques to analyze investment opportunities and make investment decisions. This course provides a strong foundation in Python, which Portfolio Managers can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Portfolio Managers.
Hedge Fund Analyst
Hedge Fund Analysts evaluate investment opportunities for hedge funds. They use a variety of tools and techniques to analyze investment opportunities and make investment recommendations. This course provides a strong foundation in Python, which Hedge Fund Analysts can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Hedge Fund Analysts.
Investment Banker
Investment Bankers help companies raise capital and make investment decisions. They use a variety of tools and techniques to analyze investment opportunities and make investment recommendations. This course provides a strong foundation in Python, which Investment Bankers can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Investment Bankers.
Private Equity Analyst
Private Equity Analysts evaluate investment opportunities for private equity firms. They use a variety of tools and techniques to analyze investment opportunities and make investment recommendations. This course provides a strong foundation in Python, which Private Equity Analysts can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Private Equity Analysts.
Actuary
Actuaries use mathematical and statistical models to assess and manage financial risks. They work in a variety of industries, including insurance, finance, and healthcare. This course provides a strong foundation in Python, which Actuaries can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Actuaries.
Data Scientist
Data Scientists use data and analysis to solve business problems. They use a variety of tools and techniques to collect, analyze, and interpret data. This course provides a strong foundation in Python, which Data Scientists can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation may be useful for Data Scientists working in the financial sector.
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. They use a variety of tools and techniques to collect, analyze, and interpret data. This course provides a strong foundation in Python, which Business Analysts can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation may be useful for Business Analysts working in the financial sector.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use a variety of tools and techniques to create software that is efficient, reliable, and secure. This course provides a strong foundation in Python, which Software Engineers can use to develop software for a variety of applications. The course's focus on stock data visualization and stock return calculation may be useful for Software Engineers working in the financial sector.
Financial Planner
Financial Planners help individuals and families plan for their financial future. They use a variety of tools and techniques to analyze financial needs and make investment recommendations. This course provides a strong foundation in Python, which Financial Planners can use to automate tasks, build models, and analyze data. The course's focus on stock data visualization and stock return calculation makes it a perfect fit for those aspiring to be Financial Planners.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in A Beginners Guide to Python for Financial Analysis.
Provides a comprehensive overview of Python for financial analysis, covering topics such as data acquisition, data cleaning, and data visualization. It would be helpful as a reference for the course's Python-related sections.
Provides a comprehensive overview of financial data and risk management, covering topics such as data collection, data analysis, and risk modeling. It would be helpful as a reference for the course's financial data and risk management sections.
Provides a comprehensive overview of financial risk, covering topics such as risk measurement, risk management, and risk regulation. It would be helpful as a reference for the course's financial risk management sections.
Provides a comprehensive overview of Python for data analysis, covering topics such as data manipulation, data visualization, and data mining. It would be helpful as a reference for the course's Python-related sections.
Provides a comprehensive overview of financial risk management, covering topics such as risk measurement, risk management, and risk regulation. It would be helpful as a reference for the course's financial risk management sections.
Provides a gentle introduction to quantitative finance, covering topics such as risk management, asset pricing, and portfolio optimization. It would be helpful as background reading for the course's financial analysis sections.
Provides an overview of data science for business, covering topics such as data collection, data analysis, and data visualization. It would be helpful as background reading for the course's financial analysis sections.
Provides a comprehensive overview of risk management and financial institutions, covering topics such as credit risk, market risk, and operational risk. It would be helpful as background reading for the course's financial analysis sections.
Provides an overview of machine learning techniques for financial analysis, covering topics such as supervised learning, unsupervised learning, and time series analysis. It would be helpful as additional reading for the course's more advanced sections.

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