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种子名称： GetFreeCourses.Co-Udemy-Python for Finance 2021 Financial Analysis for Investing
文件类型： 视频
文件数目： 184个文件
文件大小： 18.36 GB
收录时间： 2023-8-17 07:47
已经下载： 3次
资源热度： 85
最近下载： 2024-5-26 23:36

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GetFreeCourses.Co-Udemy-Python for Finance 2021 Financial Analysis for Investing.torrent

1. Introduction/1. One Question.mp443.21MB

1. Introduction/2. Get the most out of this course.mp475.17MB

10. Data Sources/1. Introduction.mp420.16MB

10. Data Sources/10. Exercises.mp457.07MB

10. Data Sources/11. Solutions.mp4166.28MB

10. Data Sources/12. What did we learn.mp425.04MB

10. Data Sources/2. What will we learn.mp465.9MB

10. Data Sources/3. Pandas Datareader - Remote Data Access for Pandas.mp432.91MB

10. Data Sources/4. Jupyter Notebook Pandas Datareader - Part I.mp4249.43MB

10. Data Sources/5. Jupyter Notebook Pandas Datareader - Part II.mp4121.68MB

10. Data Sources/6. The Yahoo! Finance API - read Financial Statements.mp453.56MB

10. Data Sources/7. Jupyter Notebook Yahoo! Finance - Financial Statements.mp4164.91MB

10. Data Sources/8. Web Scraping.mp458.62MB

10. Data Sources/9. Jupyter Notebook Web Scraping.mp4211.58MB

11. Time Series Data/1. Introduction.mp471.07MB

11. Time Series Data/2. Rate of Return, Percentage Change, and Normalization.mp4121.94MB

11. Time Series Data/3. Jupyter Notebook Rate of Return, Percentage Change, and Normalization.mp4136.02MB

11. Time Series Data/4. CAGR.mp441.45MB

11. Time Series Data/5. Jupyter Notebook CAGR.mp4103.37MB

11. Time Series Data/6. Jupyter Notebook Multiple Time Frames.mp4105.11MB

11. Time Series Data/7. Case Study DOW Theory.mp4323.58MB

11. Time Series Data/8. Jupyter Notebook Case Study DOW Theory.mp4193.72MB

11. Time Series Data/9. What did we learn.mp429.3MB

12. Technical Indicators/1. Introduction.mp453.82MB

12. Technical Indicators/10. Jupyter Notebook Exporting to Excel.mp4245.65MB

12. Technical Indicators/11. Jupyter Notebook Using our Excel Sheet.mp4143.37MB

12. Technical Indicators/12. Exercises.mp479.65MB

12. Technical Indicators/13. Solutions.mp4138.49MB

12. Technical Indicators/14. What did we learn.mp418.25MB

12. Technical Indicators/2. What is a Technical Indicator and Types of Indicators.mp4147.52MB

12. Technical Indicators/3. Indicator Moving Average.mp4103.25MB

12. Technical Indicators/4. Jupyter Notebook Simple Moving Average (MA).mp4184.47MB

12. Technical Indicators/5. Jupyter Notebook Exponential Moving Average (EMA).mp492.89MB

12. Technical Indicators/6. Indicator MACD.mp489.8MB

12. Technical Indicators/7. Jupyter Notebook MACD.mp4155.76MB

12. Technical Indicators/8. Indicator Stochastic Oscillator.mp478.8MB

12. Technical Indicators/9. Jupyter Notebook Stochastic Oscillator.mp4170.34MB

13. NumPy/1. Introduction.mp4121.09MB

13. NumPy/10. What did we learn.mp449.17MB

13. NumPy/2. Jupyter Notebook Introduction to NumPy.mp4154.96MB

13. NumPy/3. Jupyter Notebook Index, Slicing, and Views.mp4124.46MB

13. NumPy/4. Jupyter Notebook DataFrames and Series with NumPy.mp4172.53MB

13. NumPy/5. Jupyter Notebook Vectorization with NumPy.mp4139.81MB

13. NumPy/6. Jupyter Notebook Matplotlib and NumPy.mp4126.76MB

13. NumPy/7. Jupyter Notebook Dot product and Transpose.mp4156.52MB

13. NumPy/8. Exercises.mp485.88MB

13. NumPy/9. Solutions.mp4144.54MB

14. Correlation and Linear Regression/1. Introduction.mp430.77MB

14. Correlation and Linear Regression/10. Jupyter Notebook Beta Calculations.mp4109.41MB

14. Correlation and Linear Regression/11. CAPM.mp488.02MB

14. Correlation and Linear Regression/12. Jupyter Notebook CAPM Calculations.mp4105.22MB

14. Correlation and Linear Regression/13. Exercises.mp455.36MB

14. Correlation and Linear Regression/14. Solutions.mp4107.06MB

14. Correlation and Linear Regression/15. What did we learn.mp439.55MB

14. Correlation and Linear Regression/2. Adjusted Close.mp447.95MB

14. Correlation and Linear Regression/3. Volatility of a Stock.mp4106.65MB

14. Correlation and Linear Regression/4. Jupyter Notebook Volatility Calculations.mp4213.26MB

14. Correlation and Linear Regression/5. Correlation Between Securities.mp445.22MB

14. Correlation and Linear Regression/6. Jupyter Notebook Correlation Calculations.mp493.97MB

14. Correlation and Linear Regression/7. Linear Regression.mp472.35MB

14. Correlation and Linear Regression/8. Jupyter Notebook Linear Regression.mp4169.36MB

14. Correlation and Linear Regression/9. Beta.mp442.59MB

15. Working with Portfolios and Monte Carlo Simulations/1. Introduction.mp430.75MB

15. Working with Portfolios and Monte Carlo Simulations/10. Exercises.mp458.58MB

15. Working with Portfolios and Monte Carlo Simulations/11. Solutions.mp4155.06MB

15. Working with Portfolios and Monte Carlo Simulations/12. What did we learn.mp432.42MB

15. Working with Portfolios and Monte Carlo Simulations/2. Portfolios.mp428.6MB

15. Working with Portfolios and Monte Carlo Simulations/3. Jupyter Notebook Portfolio.mp4135.94MB

15. Working with Portfolios and Monte Carlo Simulations/4. Sharpe Ratio.mp454.2MB

15. Working with Portfolios and Monte Carlo Simulations/5. Jupyter Notebook Sharpe Ratio Calculations.mp4126.59MB

15. Working with Portfolios and Monte Carlo Simulations/6. Monte Carlo Simulations.mp478.1MB

15. Working with Portfolios and Monte Carlo Simulations/7. Jupyter Notebook Monte Carlo Simulations - Introduction.mp4154.87MB

15. Working with Portfolios and Monte Carlo Simulations/8. Jupyter Notebook Portfolios and Monte Carlo Simulations.mp4157.84MB

15. Working with Portfolios and Monte Carlo Simulations/9. Jupyter Notebook The Efficient Frontier.mp463.97MB

16. Finish Line/1. Introduction.mp419.97MB

16. Finish Line/2. 3 Books to Read.mp4231.77MB

16. Finish Line/3. Goodbye.mp444.94MB

2. Setup/1. Introduction.mp410.86MB

2. Setup/2. Download Anaconda (includes Python and Jupyter notebook).mp423.63MB

2. Setup/3. Resources and setup environment in Jupyter notebook.mp474.25MB

2. Setup/4. Prompt rating.mp412.18MB

3. Jupyter Notebook guide/1. Introduction.mp420.12MB

3. Jupyter Notebook guide/3. Jupyter Notebook The Dashboard.mp456.08MB

3. Jupyter Notebook guide/4. Jupyter Notebook Run and restart a Notebook.mp453.53MB

3. Jupyter Notebook guide/5. Jupyter Notebook Copy and reorganize code.mp427.42MB

3. Jupyter Notebook guide/6. Jupyter Notebook Comment and markdown.mp433.08MB

3. Jupyter Notebook guide/7. Jupyter Notebook Tab + Tab + Shift & Tab.mp482.17MB

3. Jupyter Notebook guide/8. What did we learn.mp419.77MB

4. Python Crash Course/1. Introduction.mp424.77MB

4. Python Crash Course/10. Other types.mp453.86MB

4. Python Crash Course/11. Functions.mp459.73MB

4. Python Crash Course/12. Lambda functions.mp4109.56MB

4. Python Crash Course/13. Exercises.mp472.71MB

4. Python Crash Course/14. Solutions.mp4164.74MB

4. Python Crash Course/15. New to Python We have all been there.mp472.23MB

4. Python Crash Course/16. What did we learn.mp427MB

4. Python Crash Course/2. Variables and types.mp4157.86MB

4. Python Crash Course/3. The print statement.mp439.35MB

4. Python Crash Course/4. Boolean expressions.mp482.59MB

4. Python Crash Course/5. If statements.mp474.06MB

4. Python Crash Course/6. Python lists.mp471.68MB

4. Python Crash Course/7. For-loops.mp462.36MB

4. Python Crash Course/8. While loops.mp435.63MB

4. Python Crash Course/9. Python Dictionaries (dict).mp453.99MB

5. Lemonade Stand/1. Introduction.mp428.94MB

5. Lemonade Stand/10. Dividend a story - an easy way to understand them.mp476.94MB

5. Lemonade Stand/11. Jupyter Notebook Dividend.mp4180.79MB

5. Lemonade Stand/12. What did we learn.mp455.7MB

5. Lemonade Stand/2. Intrinsic Value.mp477.57MB

5. Lemonade Stand/3. Introduction to the Lemonade Stand.mp4117.85MB

5. Lemonade Stand/4. The Lemonade Stand - the easy to understand example.mp499.67MB

5. Lemonade Stand/5. Jupyter Notebook The Lemonade Stand.mp4193.4MB

5. Lemonade Stand/6. Shares.mp4101.17MB

5. Lemonade Stand/7. Shares a story - Understand what they really are.mp4121.18MB

5. Lemonade Stand/8. Jupyter Notebook Shares.mp4158.17MB

5. Lemonade Stand/9. Dividend.mp489.41MB

6. Pandas/1. Introduction.mp4137.59MB

6. Pandas/10. Read and Write with Pandas - Part II.mp4156.61MB

6. Pandas/11. Read and Write with Pandas - Part III.mp4155.98MB

6. Pandas/12. Merge - Join - Concatenate - Part I.mp4112.59MB

6. Pandas/13. Merge - Join - Concatenate - Part II.mp468.43MB

6. Pandas/14. Transpose and clean data.mp4110.79MB

6. Pandas/15. Views.mp476.58MB

6. Pandas/16. Useful methods to know.mp4111.34MB

6. Pandas/17. Apply - an awesome method to master.mp477.88MB

6. Pandas/18. Exercises.mp474.33MB

6. Pandas/19. Solutions.mp4139.83MB

6. Pandas/2. Introduction to Pandas - a small demonstration.mp4156.46MB

6. Pandas/20. What did we learn.mp447.15MB

6. Pandas/3. Series.mp4159.69MB

6. Pandas/4. DataFrames - Part I.mp4167.25MB

6. Pandas/5. DataFrames - Part II.mp4107.46MB

6. Pandas/6. DataFrames - Part III.mp4117.88MB

6. Pandas/7. DataFrames - Part IV.mp498.67MB

6. Pandas/8. DataFrames - Part V.mp4104.4MB

6. Pandas/9. Read and Write with Pandas - Part I.mp4165.71MB

7. Intrinsic Value/1. Introduction.mp426.71MB

7. Intrinsic Value/10. Current ratio - Evaluation.mp474.59MB

7. Intrinsic Value/11. Jupyter Notebook - Current ratio.mp4148.28MB

7. Intrinsic Value/12. Stable and predictable.mp4160.68MB

7. Intrinsic Value/13. Return of Investment (ROI) - Evaluation.mp4105.9MB

7. Intrinsic Value/14. Jupyter Notebook Return of Investment.mp4135.97MB

7. Intrinsic Value/15. Revenue - Evaluation.mp494.66MB

7. Intrinsic Value/16. Jupyter Notebook Revenue.mp4230.69MB

7. Intrinsic Value/17. Earnings Per Share (EPS) - Evaluation.mp443.19MB

7. Intrinsic Value/18. Jupyter Notebook Earnings Per Share (EPS).mp4120.1MB

7. Intrinsic Value/19. Book Value - Evaluation.mp484.2MB

7. Intrinsic Value/2. Outcome of section.mp4141.02MB

7. Intrinsic Value/20. Jupyter Notebook Book Value.mp4160.54MB

7. Intrinsic Value/21. Free Cash Flow (FCF) - Evaluation.mp438.95MB

7. Intrinsic Value/22. Jupyter Notebook Free Cash Flow (FCF).mp466.14MB

7. Intrinsic Value/23. Combine All Data.mp469.22MB

7. Intrinsic Value/24. Jupyter Notebook Combine All Data.mp4141.16MB

7. Intrinsic Value/25. Calculate a Fair Price (Intrinsic Value).mp4139.51MB

7. Intrinsic Value/26. Price-to-Earnings (PE) ratio.mp455.15MB

7. Intrinsic Value/27. Jupyter Notebook Price-to-Earnings (PE) ratio.mp473.37MB

7. Intrinsic Value/28. Jupyter Notebook Calculate a Fair Price (Intrinsic Value).mp4129.82MB

7. Intrinsic Value/29. Compare it with Current Price.mp4127.87MB

7. Intrinsic Value/3. Understand Risk - Part I.mp493.63MB

7. Intrinsic Value/30. What did we learn.mp499.88MB

7. Intrinsic Value/4. Understand Risk - Part II.mp464.16MB

7. Intrinsic Value/5. Understand Rik - Part III.mp461.79MB

7. Intrinsic Value/6. Understand Risk - All put together.mp457.04MB

7. Intrinsic Value/7. Evaluate Leadership.mp4205.56MB

7. Intrinsic Value/8. Debt-to-Equity ration - Evaluation.mp493.65MB

7. Intrinsic Value/9. Jupyter Notebook Debt-to-Equity ratio.mp4278.31MB

8. Matplotlib/1. Introduction.mp420.95MB

8. Matplotlib/10. Solutions.mp4188.52MB

8. Matplotlib/11. What did we learn.mp431.51MB

8. Matplotlib/2. Overview of section.mp4104.6MB

8. Matplotlib/3. Jupyter Notebook Matplotlib basics.mp4120.74MB

8. Matplotlib/4. Jupyter Notebook Work with Axis.mp4115.37MB

8. Matplotlib/5. Jupyter Notebook Title and Labels.mp4104.81MB

8. Matplotlib/6. Jupyter Notebook Matplotlib and Pandas.mp496.98MB

8. Matplotlib/7. Jupyter Notebook Pandas and data structures.mp4146.08MB

8. Matplotlib/8. Jupyter Notebook Bar plots.mp4114.01MB

8. Matplotlib/9. Exercises.mp476.14MB

9. Visualization and Excel Export of Financial Data/1. Introduction.mp433.21MB

9. Visualization and Excel Export of Financial Data/2. Matplotlib - Part I.mp4200.63MB

9. Visualization and Excel Export of Financial Data/3. Matplotlib - Part II.mp4179.81MB

9. Visualization and Excel Export of Financial Data/4. Export to Excel - Part I.mp4156.78MB

9. Visualization and Excel Export of Financial Data/5. Export to Excel - Part II.mp4249.24MB

9. Visualization and Excel Export of Financial Data/6. Export to Excel - Part III.mp4126.31MB

9. Visualization and Excel Export of Financial Data/7. What did we learn.mp444.05MB