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[FreeCourseSite.com] Udemy - Manage Finance Data with Python & Pandas Unique Masterclass

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FreeCourseSite com Udemy - Manage Finance Data with Python & Pandas Unique Masterclass

文件列表
  1. 1. Getting Started/1. Course Overview and how to maximize your learning success.mp470.95MB
  2. 1. Getting Started/2. Tips How to get the most out of this Course.mp443.64MB
  3. 1. Getting Started/3. Did you know that....mp426.85MB
  4. 1. Getting Started/5. Installation of Anaconda.mp486.21MB
  5. 1. Getting Started/6. Opening a Jupyter Notebook.mp465.09MB
  6. 1. Getting Started/7. How to use Jupyter Notebooks.mp466.29MB
  7. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/1. Intro.mp432.15MB
  8. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/10. The S&P 500 Return Triangle (Part 1).mp456.32MB
  9. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/11. The S&P 500 Return Triangle (Part 2).mp460.35MB
  10. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/13. The S&P 500 Dollar Triangle.mp432.94MB
  11. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/14. The S&P 500 Weather Radar.mp441.9MB
  12. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/15. Exponentially-weighted Moving Averages (EWMA).mp423.78MB
  13. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/16. Expanding Windows.mp425.12MB
  14. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/17. Rolling Correlation.mp432.24MB
  15. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/18. rollling() with fixed-sized time offsets.mp433.02MB
  16. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/19. Merging Aligning Financial Time Series (hands-on).mp430.69MB
  17. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/2. Importing Financial Data from Excel.mp480.67MB
  18. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/20. Coding Exercise 13 (intro).mp420.62MB
  19. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/21. Coding Exercise 13 (Solution).mp496.57MB
  20. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/3. Simple Moving Averages (SMA) with rolling().mp440.98MB
  21. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/4. Momentum Trading Strategies with SMAs.mp433.01MB
  22. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/6. S&P 500 Performance Reporting - rolling risk and return.mp454.06MB
  23. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/7. S&P 500 Investment Horizon and Performance.mp451MB
  24. 10. Financial Data - Advanced Techniques (Rolling Statistics & Reporting)/8. Simple Returns vs. Log Returns.mp432.65MB
  25. 11. Create and Analyze Financial Indexes/1. Financial Indexes - an Overview.mp464.35MB
  26. 11. Create and Analyze Financial Indexes/10. Market Value-Weighted Index - Theory.mp442.75MB
  27. 11. Create and Analyze Financial Indexes/12. Creating a Market Value-Weighted Stock Index with Python (Part 1).mp449.8MB
  28. 11. Create and Analyze Financial Indexes/13. Creating a Market Value-Weighted Stock Index with Python (Part 2).mp444.26MB
  29. 11. Create and Analyze Financial Indexes/14. Comparison of weighting methods.mp432.48MB
  30. 11. Create and Analyze Financial Indexes/15. Price Index vs. PerformanceTotal Return Index.mp439.53MB
  31. 11. Create and Analyze Financial Indexes/16. Coding Exercise 14 (intro).mp411.36MB
  32. 11. Create and Analyze Financial Indexes/17. Coding Exercise 14 (Solution).mp469.87MB
  33. 11. Create and Analyze Financial Indexes/3. Getting the Data.mp428.05MB
  34. 11. Create and Analyze Financial Indexes/4. Price-Weighted Index - Theory.mp434.95MB
  35. 11. Create and Analyze Financial Indexes/6. Creating a Price-Weighted Stock Index with Python.mp450.23MB
  36. 11. Create and Analyze Financial Indexes/7. Equal-Weighted Index - Theory.mp425.39MB
  37. 11. Create and Analyze Financial Indexes/9. Creating an Equal-Weighted Stock Index with Python.mp445.48MB
  38. 12. Create, Analyze and Optimize Financial Portfolios/1. Intro.mp422.09MB
  39. 12. Create, Analyze and Optimize Financial Portfolios/11. Coding Exercise 15 (Intro).mp422.5MB
  40. 12. Create, Analyze and Optimize Financial Portfolios/12. Coding Exercise 15 (Solution).mp488.5MB
  41. 12. Create, Analyze and Optimize Financial Portfolios/2. Getting the Data.mp412.25MB
  42. 12. Create, Analyze and Optimize Financial Portfolios/3. Creating the equally-weighted Portfolio.mp454.3MB
  43. 12. Create, Analyze and Optimize Financial Portfolios/4. Creating many random Portfolios with Python.mp472.47MB
  44. 12. Create, Analyze and Optimize Financial Portfolios/5. What is the Sharpe Ratio and a Risk Free Asset.mp416.88MB
  45. 12. Create, Analyze and Optimize Financial Portfolios/6. Portfolio Analysis and the Sharpe Ratio with Python.mp444.85MB
  46. 12. Create, Analyze and Optimize Financial Portfolios/8. Finding the Optimal Portfolio.mp447.74MB
  47. 12. Create, Analyze and Optimize Financial Portfolios/9. Sharpe Ratio - visualized and explained.mp423.23MB
  48. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/1. Intro.mp410MB
  49. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/10. Redefining the Market Portfolio.mp445.82MB
  50. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/11. Cyclical vs. non-cyclical Stocks - another Intuition on Beta.mp440.55MB
  51. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/13. Coding Exercise 16 (Intro).mp418.78MB
  52. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/14. Coding Exercise 16 (Solution).mp468.45MB
  53. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/2. Capital Market Line (CML) & Two-Fund-Theorem.mp415.81MB
  54. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/4. The Portfolio Diversification Effect.mp486.5MB
  55. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/5. Systematic vs. unsystematic Risk.mp473.1MB
  56. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/7. Capital Asset Pricing Model (CAPM) & Security Market Line (SLM).mp448.36MB
  57. 13. Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)/9. Beta and Alpha.mp442.67MB
  58. 14. Forward-looking Mean-Variance Optimization & Asset Allocation/1. Intro.mp420.47MB
  59. 14. Forward-looking Mean-Variance Optimization & Asset Allocation/2. Mean-Variance Optimization (MVO).mp461.73MB
  60. 14. Forward-looking Mean-Variance Optimization & Asset Allocation/3. It´s not that simple - Part 1 (Investments 101 vs. Real World).mp450.48MB
  61. 14. Forward-looking Mean-Variance Optimization & Asset Allocation/4. Changing Expected Returns.mp442.91MB
  62. 14. Forward-looking Mean-Variance Optimization & Asset Allocation/5. It´s not that simple - Part 2 (Investments 101 vs. Real World).mp460.07MB
  63. 15. Interactive Financial Charts with Plotly and Cufflinks/1. Intro.mp416.54MB
  64. 15. Interactive Financial Charts with Plotly and Cufflinks/2. Getting Ready (Installing required libraries).mp418.82MB
  65. 15. Interactive Financial Charts with Plotly and Cufflinks/3. Creating Offline Graphs in Jupyter Notebooks.mp455.39MB
  66. 15. Interactive Financial Charts with Plotly and Cufflinks/4. Interactive Price Charts with Plotly.mp438.69MB
  67. 15. Interactive Financial Charts with Plotly and Cufflinks/5. Customizing Plotly Charts.mp446.88MB
  68. 15. Interactive Financial Charts with Plotly and Cufflinks/6. Interactive Histograms with Plotly.mp430.59MB
  69. 15. Interactive Financial Charts with Plotly and Cufflinks/7. Candle-Stick and OHLC Charts with Plotly.mp426.5MB
  70. 15. Interactive Financial Charts with Plotly and Cufflinks/8. SMA and Bollinger Bands with Plotly.mp442.6MB
  71. 15. Interactive Financial Charts with Plotly and Cufflinks/9. More Technical Indicators with Plotly (Volume, MACD, DMI).mp411.12MB
  72. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/1. Financial Analyst Challenge (Intro).mp49.97MB
  73. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/2. Financial Analyst Challenge (Instruction & Hints).mp431.59MB
  74. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/3. Financial Analyst Challenge (Solution Part 1).mp418.28MB
  75. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/4. Financial Analyst Challenge (Solution Part 2).mp456.51MB
  76. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/5. Financial Analyst Challenge (Solution Part 3).mp447.71MB
  77. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/6. Financial Analyst Challenge (Solution Part 4).mp438.8MB
  78. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/7. Financial Analyst Challenge (Solution Part 5).mp435.47MB
  79. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/8. Financial Analyst Challenge (Solution Part 6).mp452.32MB
  80. 16. ----- PART 3 THE FINANCIAL ANALYST CHALLENGE (FINAL PROJECT) ----/9. Financial Analyst Challenge (Solution Part 7).mp458.54MB
  81. 17. ---------- PART 4 ADVANCED TOPICS ----------------/1. Helpful DatetimeIndex Attributes and Methods.mp444.28MB
  82. 17. ---------- PART 4 ADVANCED TOPICS ----------------/10. The Timedelta Object.mp442.81MB
  83. 17. ---------- PART 4 ADVANCED TOPICS ----------------/2. Filling NA Values with bfill, ffill and interpolation.mp478.47MB
  84. 17. ---------- PART 4 ADVANCED TOPICS ----------------/3. resample() and agg().mp446.64MB
  85. 17. ---------- PART 4 ADVANCED TOPICS ----------------/4. resample() and OHLC().mp48.79MB
  86. 17. ---------- PART 4 ADVANCED TOPICS ----------------/5. Upsampling with resample().mp449.64MB
  87. 17. ---------- PART 4 ADVANCED TOPICS ----------------/6. Timezones and Converting (Part 1).mp429.3MB
  88. 17. ---------- PART 4 ADVANCED TOPICS ----------------/7. Timezones and Converting (Part 2).mp436.92MB
  89. 17. ---------- PART 4 ADVANCED TOPICS ----------------/8. Shifting Dates with pd.DateOffset().mp431.37MB
  90. 17. ---------- PART 4 ADVANCED TOPICS ----------------/9. Advanced Date Shifting.mp426.61MB
  91. 19. Appendix 1 Python Crash Course (optional)/1. Intro.mp45.95MB
  92. 19. Appendix 1 Python Crash Course (optional)/10. Conditional Statements (if, elif, else, while).mp486.02MB
  93. 19. Appendix 1 Python Crash Course (optional)/11. For Loops.mp458.37MB
  94. 19. Appendix 1 Python Crash Course (optional)/12. Key words break, pass, continue.mp436.73MB
  95. 19. Appendix 1 Python Crash Course (optional)/13. Generating Random Numbers.mp438.12MB
  96. 19. Appendix 1 Python Crash Course (optional)/14. User Defined Functions (Part 1).mp464.38MB
  97. 19. Appendix 1 Python Crash Course (optional)/15. User Defined Functions (Part 2).mp457.43MB
  98. 19. Appendix 1 Python Crash Course (optional)/16. User Defined Functions (Part 3).mp452.16MB
  99. 19. Appendix 1 Python Crash Course (optional)/17. Visualization with Matplotlib.mp4124.27MB
  100. 19. Appendix 1 Python Crash Course (optional)/19. Quiz Solution.mp438.25MB
  101. 19. Appendix 1 Python Crash Course (optional)/2. First Steps.mp434.23MB
  102. 19. Appendix 1 Python Crash Course (optional)/3. Variables.mp431.45MB
  103. 19. Appendix 1 Python Crash Course (optional)/4. Data Types Integers & Floats.mp449.51MB
  104. 19. Appendix 1 Python Crash Course (optional)/5. Data Types Strings.mp477.74MB
  105. 19. Appendix 1 Python Crash Course (optional)/6. Data Types Lists (Part 1).mp462.73MB
  106. 19. Appendix 1 Python Crash Course (optional)/7. Data Types Lists (Part 2).mp4134.38MB
  107. 19. Appendix 1 Python Crash Course (optional)/8. Data Types Tuples.mp441.81MB
  108. 19. Appendix 1 Python Crash Course (optional)/9. Operators & Booleans.mp459.49MB
  109. 20. Appendix 2 Numpy Crash Course (optional)/1. Introduction to Numpy Arrays.mp441.11MB
  110. 20. Appendix 2 Numpy Crash Course (optional)/10. Summary Statistics.mp444.82MB
  111. 20. Appendix 2 Numpy Crash Course (optional)/11. Visualization and (Linear) Regression.mp484.47MB
  112. 20. Appendix 2 Numpy Crash Course (optional)/13. Numpy Quiz Solution.mp445.45MB
  113. 20. Appendix 2 Numpy Crash Course (optional)/2. Numpy Arrays Vectorization.mp464.73MB
  114. 20. Appendix 2 Numpy Crash Course (optional)/3. Numpy Arrays Indexing and Slicing.mp453.46MB
  115. 20. Appendix 2 Numpy Crash Course (optional)/4. Numpy Arrays Shape and Dimensions.mp435.52MB
  116. 20. Appendix 2 Numpy Crash Course (optional)/5. Numpy Arrays Indexing and Slicing of multi-dimensional Arrays.mp473.61MB
  117. 20. Appendix 2 Numpy Crash Course (optional)/6. Numpy Arrays Boolean Indexing.mp444.19MB
  118. 20. Appendix 2 Numpy Crash Course (optional)/7. Generating Random Numbers.mp467.51MB
  119. 20. Appendix 2 Numpy Crash Course (optional)/8. Performance Issues.mp449.87MB
  120. 20. Appendix 2 Numpy Crash Course (optional)/9. Case Study Numpy vs. Python Standard Library.mp445.62MB
  121. 3. Pandas Basics/1. Intro to Tabular Data Pandas.mp427.55MB
  122. 3. Pandas Basics/10. Explore your own Dataset Coding Exercise 1 (Solution).mp439.47MB
  123. 3. Pandas Basics/11. Selecting Columns.mp438.56MB
  124. 3. Pandas Basics/12. Selecting Rows with Square Brackets (not advisable).mp422.05MB
  125. 3. Pandas Basics/13. Selecting Rows with iloc (position-based indexing).mp453.96MB
  126. 3. Pandas Basics/14. Slicing Rows and Columns with iloc (position-based indexing).mp425.92MB
  127. 3. Pandas Basics/16. Selecting Rows with loc (label-based indexing).mp430.36MB
  128. 3. Pandas Basics/17. Slicing Rows and Columns with loc (label-based indexing).mp491.41MB
  129. 3. Pandas Basics/19. Summary and Outlook.mp462.13MB
  130. 3. Pandas Basics/21. Coding Exercise 2 (Intro).mp48.64MB
  131. 3. Pandas Basics/22. Coding Exercise 2 (Solution).mp450.56MB
  132. 3. Pandas Basics/3. First Steps (Inspection of Data, Part 1).mp445.2MB
  133. 3. Pandas Basics/4. First Steps (Inspection of Data, Part 2).mp456.78MB
  134. 3. Pandas Basics/5. Coding Exercise 0 Coding the Video Lectures.mp4113.37MB
  135. 3. Pandas Basics/6. Built-in Functions, Attributes and Methods.mp450.67MB
  136. 3. Pandas Basics/7. Make it easy TAB Completion and Tooltip.mp454.2MB
  137. 3. Pandas Basics/9. Explore your own Dataset Coding Exercise 1 (Intro).mp460.54MB
  138. 4. Pandas Intermediate Topics/10. Coding Exercise 3 (Intro).mp410.52MB
  139. 4. Pandas Intermediate Topics/11. Coding Exercise 3 (Solution).mp435.9MB
  140. 4. Pandas Intermediate Topics/12. First Steps with Pandas Index Objects.mp443.04MB
  141. 4. Pandas Intermediate Topics/13. Changing Row Index with set_index() and reset_index().mp475.07MB
  142. 4. Pandas Intermediate Topics/14. Changing Column Labels.mp421.18MB
  143. 4. Pandas Intermediate Topics/15. Renaming Index & Column Labels with rename().mp433.54MB
  144. 4. Pandas Intermediate Topics/17. Coding Exercise 4 (Intro).mp47.67MB
  145. 4. Pandas Intermediate Topics/18. Coding Exercise 4 (Solution).mp427.34MB
  146. 4. Pandas Intermediate Topics/19. Sorting DataFrames with sort_index() and sort_values().mp454.38MB
  147. 4. Pandas Intermediate Topics/2. First Steps with Pandas Series.mp435.96MB
  148. 4. Pandas Intermediate Topics/20. nunique() and nlargest() nsmallest() with DataFrames.mp432.62MB
  149. 4. Pandas Intermediate Topics/21. Filtering DataFrames (one Condition).mp452.91MB
  150. 4. Pandas Intermediate Topics/22. Filtering DataFrames by many Conditions (AND).mp425.9MB
  151. 4. Pandas Intermediate Topics/23. Filtering DataFrames by many Conditions (OR).mp430.79MB
  152. 4. Pandas Intermediate Topics/24. Advanced Filtering with between(), isin() and ~.mp465.45MB
  153. 4. Pandas Intermediate Topics/25. any() and all().mp417.51MB
  154. 4. Pandas Intermediate Topics/27. Coding Exercise 5 (Intro).mp411MB
  155. 4. Pandas Intermediate Topics/28. Coding Exercise 5 (Solution).mp471.87MB
  156. 4. Pandas Intermediate Topics/29. Intro to NA Values missing Values.mp445.65MB
  157. 4. Pandas Intermediate Topics/3. Analyzing Numerical Series with unique(), nunique() and value_counts().mp472.67MB
  158. 4. Pandas Intermediate Topics/30. Handling NA Values missing Values.mp468.61MB
  159. 4. Pandas Intermediate Topics/31. Exporting DataFrames to csv.mp413.25MB
  160. 4. Pandas Intermediate Topics/32. Summary Statistics and Accumulations.mp457.58MB
  161. 4. Pandas Intermediate Topics/33. The agg() method.mp422.81MB
  162. 4. Pandas Intermediate Topics/34. Coding Exercise 6 (Intro).mp415.33MB
  163. 4. Pandas Intermediate Topics/35. Coding Exercise 6 (Solution).mp483.91MB
  164. 4. Pandas Intermediate Topics/5. EXCURSUS Updating Pandas Anaconda.mp477.1MB
  165. 4. Pandas Intermediate Topics/6. Analyzing non-numerical Series with unique(), nunique(), value_counts().mp442.89MB
  166. 4. Pandas Intermediate Topics/7. The copy() method.mp424.78MB
  167. 4. Pandas Intermediate Topics/8. Sorting of Series and Introduction to the inplace - parameter.mp441.42MB
  168. 5. Data Visualization with Matplotlib and Seaborn/10. Seaborn Heatmaps.mp442.75MB
  169. 5. Data Visualization with Matplotlib and Seaborn/11. Coding Exercise 7 (Intro).mp410.69MB
  170. 5. Data Visualization with Matplotlib and Seaborn/12. Coding Exercise 7 (Solution).mp462.15MB
  171. 5. Data Visualization with Matplotlib and Seaborn/2. Visualization with Matplotlib (Intro).mp470.28MB
  172. 5. Data Visualization with Matplotlib and Seaborn/3. Customization of Plots.mp4103.1MB
  173. 5. Data Visualization with Matplotlib and Seaborn/4. Histogramms (Part 1).mp424.6MB
  174. 5. Data Visualization with Matplotlib and Seaborn/5. Histogramms (Part 2).mp434.14MB
  175. 5. Data Visualization with Matplotlib and Seaborn/6. Scatterplots.mp436.12MB
  176. 5. Data Visualization with Matplotlib and Seaborn/7. First Steps with Seaborn.mp422.1MB
  177. 5. Data Visualization with Matplotlib and Seaborn/8. Categorical Seaborn Plots.mp485.2MB
  178. 5. Data Visualization with Matplotlib and Seaborn/9. Seaborn Regression Plots.mp479.41MB
  179. 6. Pandas Advanced Topics/10. Coding Exercise 8 (Intro).mp49.04MB
  180. 6. Pandas Advanced Topics/11. Coding Exercise 8 (Solution).mp454.6MB
  181. 6. Pandas Advanced Topics/12. Introduction to GroupBy Operations.mp410.06MB
  182. 6. Pandas Advanced Topics/13. Understanding the GroupBy Object.mp446.25MB
  183. 6. Pandas Advanced Topics/14. Splitting with many Keys.mp449.71MB
  184. 6. Pandas Advanced Topics/15. split-apply-combine.mp447.06MB
  185. 6. Pandas Advanced Topics/16. split-apply-combine applied.mp470.71MB
  186. 6. Pandas Advanced Topics/17. Hierarchical Indexing with Groupby.mp432.86MB
  187. 6. Pandas Advanced Topics/18. stack() and unstack().mp478.77MB
  188. 6. Pandas Advanced Topics/2. Removing Columns.mp436.06MB
  189. 6. Pandas Advanced Topics/20. Coding Exercise 9 (Intro).mp47.84MB
  190. 6. Pandas Advanced Topics/21. Coding Exercise 9 (Solution).mp444.46MB
  191. 6. Pandas Advanced Topics/3. Removing Rows.mp449.66MB
  192. 6. Pandas Advanced Topics/4. Adding new Columns to a DataFrame.mp417.89MB
  193. 6. Pandas Advanced Topics/5. Arithmetic Operations (Part 1).mp463.51MB
  194. 6. Pandas Advanced Topics/6. Arithmetic Operations (Part 2).mp458.44MB
  195. 6. Pandas Advanced Topics/8. Adding new Rows to a DataFrame.mp488.04MB
  196. 6. Pandas Advanced Topics/9. Manipulating Elements in a DataFrame.mp431.28MB
  197. 8. Time Series Data in Pandas Introduction/1. Importing Time Series Data from csv-files.mp441.75MB
  198. 8. Time Series Data in Pandas Introduction/10. Downsampling Time Series with resample (Part 2).mp449.13MB
  199. 8. Time Series Data in Pandas Introduction/11. The PeriodIndex object.mp438.78MB
  200. 8. Time Series Data in Pandas Introduction/12. Advanced Indexing with reindex().mp450.48MB
  201. 8. Time Series Data in Pandas Introduction/13. Coding Exercise 11 (intro).mp48.76MB
  202. 8. Time Series Data in Pandas Introduction/14. Coding Exercise 11 (Solution).mp444.08MB
  203. 8. Time Series Data in Pandas Introduction/2. Converting strings to datetime objects with pd.to_datetime().mp457.97MB
  204. 8. Time Series Data in Pandas Introduction/3. Initial Analysis Visualization of Time Series.mp435.02MB
  205. 8. Time Series Data in Pandas Introduction/4. Indexing and Slicing Time Series.mp448.16MB
  206. 8. Time Series Data in Pandas Introduction/5. Creating a customized DatetimeIndex with pd.date_range().mp4114.71MB
  207. 8. Time Series Data in Pandas Introduction/6. More on pd.date_range().mp412.36MB
  208. 8. Time Series Data in Pandas Introduction/7. Coding Exercise 10 (intro).mp49.94MB
  209. 8. Time Series Data in Pandas Introduction/8. Coding Exercise 10 (Solution).mp444.46MB
  210. 8. Time Series Data in Pandas Introduction/9. Downsampling Time Series with resample() (Part 1).mp485.52MB
  211. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/11. Financial Time Series - Covariance and Correlation.mp425.73MB
  212. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/12. Coding Exercise 12 (intro).mp417.53MB
  213. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/13. Coding Exercise 12 (Solution).mp455.95MB
  214. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/2. Getting Ready (Installing required library).mp421.77MB
  215. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/3. Importing Stock Price Data from Yahoo Finance (it still works!).mp471.86MB
  216. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/4. Initial Inspection and Visualization.mp442.33MB
  217. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/5. Normalizing Time Series to a Base Value (100).mp444.25MB
  218. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/6. The shift() method.mp435.76MB
  219. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/7. The methods diff() and pct_change().mp440.26MB
  220. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/8. Measuring Stock Performance with MEAN Returns and STD of Returns.mp443.93MB
  221. 9. Financial Data - Essential Workflows (Risk, Return & Correlation)/9. Financial Time Series - Return and Risk.mp453.66MB
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