GigaCourse Com Udemy - Complete 2-in-1 Python for Business and Finance Bootcamp

1. 01 Getting Started/001 Tips How to get the most out of this Course (don t skip).mp437.57MB
2. 01 Getting Started/003 How to download and install Anaconda for Python coding.mp471MB
3. 01 Getting Started/004 Jupyter Notebooks - let s get started.mp450.91MB
4. 01 Getting Started/005 How to work with Jupyter Notebooks.mp453.47MB
5. 02 ---- PART 1 PYTHON BASICS TIME VALUE OF MONEY AND CAPITAL BUDGETING ----/006 Overview Download of Course Materials for Part 1.mp430.37MB
6. 02 ---- PART 1 PYTHON BASICS TIME VALUE OF MONEY AND CAPITAL BUDGETING ----/007 Coding Projects Part 1 - Overview.mp415.36MB
7. 03 How to use Python as a Calculator for basic Time Value of Money Problems/008 Intro to the Time Value of Money (TVM) Concept (Theory).mp416.49MB
8. 03 How to use Python as a Calculator for basic Time Value of Money Problems/009 Calculate Future Values (FV) with Python Compounding.mp412.75MB
9. 03 How to use Python as a Calculator for basic Time Value of Money Problems/010 Calculate Present Values (FV) with Python Discounting.mp410.06MB
10. 03 How to use Python as a Calculator for basic Time Value of Money Problems/011 Interest Rates and Returns (Theory).mp414.19MB
11. 03 How to use Python as a Calculator for basic Time Value of Money Problems/012 Calculate Interest Rates and Returns with Python.mp419.27MB
12. 03 How to use Python as a Calculator for basic Time Value of Money Problems/013 Introduction to Variables.mp418.13MB
13. 03 How to use Python as a Calculator for basic Time Value of Money Problems/014 Variables and Memory (Theory).mp45.48MB
14. 03 How to use Python as a Calculator for basic Time Value of Money Problems/015 Excursus How to add inline comments.mp411.25MB
15. 03 How to use Python as a Calculator for basic Time Value of Money Problems/016 More on Variables and Memory.mp422.22MB
16. 03 How to use Python as a Calculator for basic Time Value of Money Problems/017 Variables - Dos Don ts and Conventions.mp417.06MB
17. 03 How to use Python as a Calculator for basic Time Value of Money Problems/018 The print() Function.mp417.41MB
18. 03 How to use Python as a Calculator for basic Time Value of Money Problems/019 Coding Exercise 1.mp446.86MB
19. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/020 TVM Problems with many Cashflows.mp410.49MB
20. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/021 Intro to Python Lists.mp47.76MB
21. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/022 Zero-based Indexing and negative Indexing in Python (Theory).mp47.44MB
22. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/023 Indexing Lists.mp413.86MB
23. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/024 For Loops - Iterating over Lists.mp429.91MB
24. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/025 The range Object - another Iterable.mp417.09MB
25. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/026 Calculate FV and PV for many Cashflows.mp433.53MB
26. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/027 The Net Present Value - NPV (Theory).mp433.29MB
27. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/028 Calculate an Investment Project s NPV.mp414.34MB
28. 04 How to use Lists and For Loops for TVM Problems with many Cashflows/029 Coding Exercise 2.mp437.98MB
29. 05 100 Python Objects Data Types Operators Functional Programming/030 Data Types in Action.mp424.34MB
30. 05 100 Python Objects Data Types Operators Functional Programming/031 The Data Type Hierarchy (Theory).mp410.78MB
31. 05 100 Python Objects Data Types Operators Functional Programming/032 Excursus Dynamic Typing in Python.mp45.19MB
32. 05 100 Python Objects Data Types Operators Functional Programming/033 Build-in Functions.mp425.37MB
33. 05 100 Python Objects Data Types Operators Functional Programming/034 Integers.mp410.98MB
34. 05 100 Python Objects Data Types Operators Functional Programming/035 Floats.mp424.33MB
35. 05 100 Python Objects Data Types Operators Functional Programming/036 How to round Floats (and Integers) with round().mp420.91MB
36. 05 100 Python Objects Data Types Operators Functional Programming/037 More on Lists.mp424.6MB
37. 05 100 Python Objects Data Types Operators Functional Programming/038 Lists and Element-wise Operations.mp417.59MB
38. 05 100 Python Objects Data Types Operators Functional Programming/039 Slicing Lists.mp420.12MB
39. 05 100 Python Objects Data Types Operators Functional Programming/041 Changing Elements in Lists.mp410.11MB
40. 05 100 Python Objects Data Types Operators Functional Programming/042 Sorting and Reversing Lists.mp413.19MB
41. 05 100 Python Objects Data Types Operators Functional Programming/043 Adding and removing Elements fromto Lists.mp438.54MB
42. 05 100 Python Objects Data Types Operators Functional Programming/044 Mutable vs. immutable Objects (Part 1).mp434.49MB
43. 05 100 Python Objects Data Types Operators Functional Programming/045 Mutable vs. immutable Objects (Part 2).mp421.85MB
44. 05 100 Python Objects Data Types Operators Functional Programming/046 Coding Exercise 3.mp453.78MB
45. 05 100 Python Objects Data Types Operators Functional Programming/047 Tuples.mp429.78MB
46. 05 100 Python Objects Data Types Operators Functional Programming/048 Dictionaries.mp431.01MB
47. 05 100 Python Objects Data Types Operators Functional Programming/049 Intro to Strings.mp440.84MB
48. 05 100 Python Objects Data Types Operators Functional Programming/050 String Replacement.mp417.3MB
49. 05 100 Python Objects Data Types Operators Functional Programming/051 Booleans.mp48.87MB
50. 05 100 Python Objects Data Types Operators Functional Programming/052 Operators (Theory).mp411.71MB
51. 05 100 Python Objects Data Types Operators Functional Programming/053 Comparison Logical and Membership Operators in Action.mp435.52MB
52. 05 100 Python Objects Data Types Operators Functional Programming/054 Coding Exercise 4.mp442.3MB
53. 06 How to solve for IRR YTM with While Loops and Conditional Statements/055 Conditional Statements.mp438.63MB
54. 06 How to solve for IRR YTM with While Loops and Conditional Statements/056 Keywords pass continue and break.mp439.4MB
55. 06 How to solve for IRR YTM with While Loops and Conditional Statements/057 Calculate a Project s Payback Period.mp421.87MB
56. 06 How to solve for IRR YTM with While Loops and Conditional Statements/058 While Loops.mp435.63MB
57. 06 How to solve for IRR YTM with While Loops and Conditional Statements/059 The Internal Rate of Return - IRR (Theory).mp423.91MB
58. 06 How to solve for IRR YTM with While Loops and Conditional Statements/060 Solving for a Project s IRR.mp460.77MB
59. 06 How to solve for IRR YTM with While Loops and Conditional Statements/061 Bonds and the Yield to Maturity - YTM (Theory).mp436.99MB
60. 06 How to solve for IRR YTM with While Loops and Conditional Statements/062 Solving for a Bond s Yield to Maturity (YTM).mp412.53MB
61. 06 How to solve for IRR YTM with While Loops and Conditional Statements/063 Coding Exercise 5.mp455.81MB
62. 07 How to create great graphs with Matplotlib - Plotting NPV and IRR/064 Intro.mp414.48MB
63. 07 How to create great graphs with Matplotlib - Plotting NPV and IRR/065 Line Plots.mp423.34MB
64. 07 How to create great graphs with Matplotlib - Plotting NPV and IRR/066 Scatter Plots.mp47.22MB
65. 07 How to create great graphs with Matplotlib - Plotting NPV and IRR/067 Customizing Plots (Part 1).mp424.42MB
66. 07 How to create great graphs with Matplotlib - Plotting NPV and IRR/068 Customizing Plots (Part 2).mp480.42MB
67. 07 How to create great graphs with Matplotlib - Plotting NPV and IRR/069 Plotting NPV IRR.mp440.68MB
68. 08 The Numpy Package Working with numbers made easy/071 Modules Packages and Libraries - No need to reinvent the Wheel.mp432.03MB
69. 08 The Numpy Package Working with numbers made easy/072 Numpy Arrays.mp435.72MB
70. 08 The Numpy Package Working with numbers made easy/073 Indexing and Slicing Numpy Arrays.mp413.67MB
71. 08 The Numpy Package Working with numbers made easy/074 Vectorized Operations with Numpy Arrays.mp418.73MB
72. 08 The Numpy Package Working with numbers made easy/075 Changing Elements in Numpy Arrays Mutability.mp424.52MB
73. 08 The Numpy Package Working with numbers made easy/076 View vs. copy - potential Pitfalls when slicing Numpy Arrays.mp419.27MB
74. 08 The Numpy Package Working with numbers made easy/077 Numpy Array Methods and Attributes.mp421.97MB
75. 08 The Numpy Package Working with numbers made easy/078 Numpy Universal Functions.mp417.77MB
76. 08 The Numpy Package Working with numbers made easy/079 Boolean Arrays and Conditional Filtering.mp418.14MB
77. 08 The Numpy Package Working with numbers made easy/080 Advanced Filtering Bitwise Operators.mp428.31MB
78. 08 The Numpy Package Working with numbers made easy/081 Determining a Project s Payback Period with np.where().mp422.53MB
79. 08 The Numpy Package Working with numbers made easy/082 Creating Numpy Arrays from Scratch.mp437.86MB
80. 08 The Numpy Package Working with numbers made easy/083 Coding Exercise 7.mp473.13MB
81. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/084 Evaluating Investments with np.npv() and np.irr().mp422.24MB
82. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/085 Evaluating Annuities with np.fv() - Funding Phase.mp432.8MB
83. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/086 Evaluating Annuities with np.fv() - Payout Phase.mp424.4MB
84. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/087 How to solve for annuity payments with np.pmt().mp415.78MB
85. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/088 How to solve for the number of periodic payments with np.nper().mp412.7MB
86. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/089 How to calculate the required Contract Value with np.pv().mp415.27MB
87. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/090 Frequency of compounding and the effective annual interest rate.mp421.81MB
88. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/091 How to evaluate a Retirement Plan A-Z.mp431.97MB
89. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/092 Retirement Plan Sensitivity Analysis.mp430.76MB
90. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/093 Mortgage Loan Analysis - Debt Sizing.mp439.07MB
91. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/094 Mortgage Loan Analysis - Interest Payments and Amortization Schedule.mp481.35MB
92. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/095 Calculate PV of equal installments with np.pv() - Valuation of Bonds.mp410.15MB
93. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/096 Capital Budgeting - Mutually exclusive Projects (Part 1).mp423.06MB
94. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/097 Capital Budgeting - Mutually exclusive Projects (Part 2).mp440.99MB
95. 09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/098 Capital Budgeting - Mutually exclusive Projects (Part 3).mp418.67MB
96. 10 --- PART 2 STATISTICS AND HYPOTHESIS TESTING WITH PYTHON NUMPY AND SCIPY ---/100 Statistics - Overview Terms and Vocabulary.mp495.73MB
97. 10 --- PART 2 STATISTICS AND HYPOTHESIS TESTING WITH PYTHON NUMPY AND SCIPY ---/101 Coding Projects Part 2 - Overview.mp421.07MB
98. 10 --- PART 2 STATISTICS AND HYPOTHESIS TESTING WITH PYTHON NUMPY AND SCIPY ---/102 Download of Part 2 Course Materials.mp430.19MB
99. 11 How to perform Descriptive Statistics on Populations and Samples/103 Population vs. Sample.mp443.91MB
100. 11 How to perform Descriptive Statistics on Populations and Samples/104 Visualizing Frequency Distributions with plt.hist().mp422.65MB
101. 11 How to perform Descriptive Statistics on Populations and Samples/105 Relative and Cumulative Frequencies with plt.hist().mp436.43MB
102. 11 How to perform Descriptive Statistics on Populations and Samples/106 Measures of Central Tendency (Theory).mp420.74MB
103. 11 How to perform Descriptive Statistics on Populations and Samples/107 Coding Measures of Central Tendency - Mean and Median.mp422.33MB
104. 11 How to perform Descriptive Statistics on Populations and Samples/108 Coding Measures of Central Tendency - Geometric Mean.mp416.56MB
105. 11 How to perform Descriptive Statistics on Populations and Samples/109 Excursus Why Log Returns are useful.mp412.4MB
106. 11 How to perform Descriptive Statistics on Populations and Samples/110 Variability around the Central Tendency Dispersion (Theory).mp427.68MB
107. 11 How to perform Descriptive Statistics on Populations and Samples/111 Minimum Maximum and Range with PythonNumpy.mp412.3MB
108. 11 How to perform Descriptive Statistics on Populations and Samples/112 Percentiles with PythonNumpy.mp417.57MB
109. 11 How to perform Descriptive Statistics on Populations and Samples/113 Variance and Standard Deviation with PythonNumpy.mp416.35MB
110. 11 How to perform Descriptive Statistics on Populations and Samples/114 Skew and Kurtosis (Theory).mp418.03MB
111. 11 How to perform Descriptive Statistics on Populations and Samples/115 How to calculate Skew and Kurtosis with scipy.stats.mp427.45MB
112. 12 Common Probability Distributions and how to construct Confidence Intervals/117 How to generate Random Numbers with Numpy.mp425.19MB
113. 12 Common Probability Distributions and how to construct Confidence Intervals/118 Reproducibility with np.random.seed().mp417.25MB
114. 12 Common Probability Distributions and how to construct Confidence Intervals/119 Probability Distributions - Overview.mp435.68MB
115. 12 Common Probability Distributions and how to construct Confidence Intervals/120 Discrete Uniform Distributions.mp428.2MB
116. 12 Common Probability Distributions and how to construct Confidence Intervals/121 Continuous Uniform Distributions.mp420.11MB
117. 12 Common Probability Distributions and how to construct Confidence Intervals/122 The Normal Distribution (Theory).mp418.43MB
118. 12 Common Probability Distributions and how to construct Confidence Intervals/123 Creating a normally distributed Random Variable.mp424.11MB
119. 12 Common Probability Distributions and how to construct Confidence Intervals/124 Normal Distribution - Probability Density Function (pdf) with scipy.stats.mp426.92MB
120. 12 Common Probability Distributions and how to construct Confidence Intervals/125 Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats.mp415.38MB
121. 12 Common Probability Distributions and how to construct Confidence Intervals/126 The Standard Normal Distribution and Z-Values.mp438.63MB
122. 12 Common Probability Distributions and how to construct Confidence Intervals/127 Properties of the Standard Normal Distribution (Theory).mp414.84MB
123. 12 Common Probability Distributions and how to construct Confidence Intervals/128 Probabilities and Z-Values with scipy.stats.mp459.27MB
124. 12 Common Probability Distributions and how to construct Confidence Intervals/129 Confidence Intervals with scipy.stats.mp448.12MB
125. 13 How to estimate Population parameters with Samples - Sampling and Estimation/131 Sample Statistic Sampling Error and Sampling Distribution (Theory).mp433.91MB
126. 13 How to estimate Population parameters with Samples - Sampling and Estimation/132 Sampling with np.random.choice().mp420.64MB
127. 13 How to estimate Population parameters with Samples - Sampling and Estimation/133 Sampling Distribution.mp421.58MB
128. 13 How to estimate Population parameters with Samples - Sampling and Estimation/134 Standard Error.mp410.65MB
129. 13 How to estimate Population parameters with Samples - Sampling and Estimation/135 Central Limit Theorem (Coding Part 1).mp426.3MB
130. 13 How to estimate Population parameters with Samples - Sampling and Estimation/136 Central Limit Theorem (Coding Part 2).mp430.34MB
131. 13 How to estimate Population parameters with Samples - Sampling and Estimation/137 Central Limit Theorem (Theory).mp416.98MB
132. 13 How to estimate Population parameters with Samples - Sampling and Estimation/138 Point Estimates vs. Confidence Interval Estimates (known Population Variance).mp423.42MB
133. 13 How to estimate Population parameters with Samples - Sampling and Estimation/139 The Student s t-distribution What is it and whywhen do we use it.mp420.14MB
134. 13 How to estimate Population parameters with Samples - Sampling and Estimation/140 Unknown Population Variance - the Standard Case (Example 1).mp426.34MB
135. 13 How to estimate Population parameters with Samples - Sampling and Estimation/141 Unknown Population Variance - the Standard Case (Example 2).mp417.8MB
136. 13 How to estimate Population parameters with Samples - Sampling and Estimation/142 Student s t-Distribution vs. Normal Distribution with scipy.stats.mp429.63MB
137. 13 How to estimate Population parameters with Samples - Sampling and Estimation/143 Bootstrapping with Python an alternative method without Statistics.mp428.05MB
138. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/145 Hypothesis Testing (Theory).mp450.95MB
139. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/146 Two-tailed Z-Test with known Population Variance.mp452.74MB
140. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/147 What is the p-value (Theory).mp413.03MB
141. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/148 Calculating and interpreting z-statistic and p-value with scipy.stats.mp422.19MB
142. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/149 One-tailed Z-Test with known Population Variance.mp431.2MB
143. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/150 Two-tailed t-Test (unknown Population Variance).mp438.81MB
144. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/151 One-tailed t-Test (unknown Population Variance).mp415.56MB
145. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/152 Hypothesis Testing with Bootstrapping.mp432.46MB
146. 14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/153 Testing for Normality of Financial Returns with scipy.stats.mp449.49MB
147. 15 -- PART 3 ADVANCED PYTHON MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) ---/155 Overview Download of Course Materials for Part 3.mp411.28MB
148. 15 -- PART 3 ADVANCED PYTHON MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) ---/156 Coding Projects Part 3 - Overview.mp418.91MB
149. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/157 How to work with nested Lists.mp418.25MB
150. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/158 2-dimensional Numpy Arrays.mp416.13MB
151. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/159 How to slice 2-dim Numpy Arrays (Part 1).mp428.92MB
152. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/160 How to slice 2-dim Numpy Arrays (Part 2).mp48.76MB
153. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/161 Recap Changing Elements in a Numpy Array slice.mp416.51MB
154. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/162 How to perform row-wise and column-wise Operations.mp422.48MB
155. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/163 Reshaping and Transposing 2-dim Numpy Arrays.mp424.65MB
156. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/164 Creating 2-dim Numpy Arrays from Scratch.mp416.88MB
157. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/165 Arithmetic Vectorized Operations with 2-dim Numpy Arrays.mp427.5MB
158. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/166 The keepdims parameter.mp420.76MB
159. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/167 Adding Removing Elements.mp416.49MB
160. 16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/168 Merging and Concatenating Numpy Arrays.mp418.72MB
161. 17 How to create your own user-defined Functions/170 Defining your first user-defined Function.mp427.36MB
162. 17 How to create your own user-defined Functions/171 What s the difference between Positional Arguments vs. Keyword Arguments.mp436.35MB
163. 17 How to create your own user-defined Functions/172 How to work with Default Arguments.mp428.47MB
164. 17 How to create your own user-defined Functions/173 The Default Argument None.mp426.8MB
165. 17 How to create your own user-defined Functions/174 How to unpack Iterables.mp418.62MB
166. 17 How to create your own user-defined Functions/175 Sequences as arguments and args.mp426.26MB
167. 17 How to create your own user-defined Functions/176 How to return many results.mp413.44MB
168. 17 How to create your own user-defined Functions/177 Scope - easily explained.mp435.27MB
169. 17 How to create your own user-defined Functions/178 How to create Nested Functions.mp430.12MB
170. 17 How to create your own user-defined Functions/179 Putting it all together - Case Study.mp469.22MB
171. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/181 What is the Value-at-Risk (VaR) (Theory).mp420.39MB
172. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/182 Analyzing the Data past Performance.mp425.46MB
173. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/183 How to use the Parametric Method to calculate Value-at-Risk (VaR).mp423.88MB
174. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/184 How to use the Historical Method to calculate Value-at-Risk (VaR).mp413.6MB
175. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/185 Monte Carlo Simulations for Value-at-Risk - Parametric (Part 1).mp429.44MB
176. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/186 Monte Carlo Simulations for Value-at-Risk - Parametric (Part 2).mp443.53MB
177. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/187 Monte Carlo Simulations for Value-at-Risk - Parametric (Part 3).mp451.01MB
178. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/188 Monte Carlo Simulations for Value-at-Risk - Bootstrapping (Part 1).mp441.98MB
179. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/189 Monte Carlo Simulations for Value-at-Risk - Bootstrapping (Part 2).mp436.11MB
180. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/190 Conditional Value-at-Risk (CVaR).mp419.47MB
181. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/191 Dynamic path-dependent Simulations (Part 1).mp441.54MB
182. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/192 Dynamic path-dependent Simulations (Part 2).mp467.44MB
183. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/193 Dynamic path-dependent Simulations (Part 3).mp417.74MB
184. 18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/194 Dynamic path-dependent Simulations (Part 4).mp465.49MB
185. 19 --- PART 4 MANAGING (FINANCIAL) DATA WITH PANDAS BEYOND EXCEL ---/196 Introduction.mp47.12MB
186. 19 --- PART 4 MANAGING (FINANCIAL) DATA WITH PANDAS BEYOND EXCEL ---/197 Download of Part 4 Course Materials.mp468.4MB
187. 19 --- PART 4 MANAGING (FINANCIAL) DATA WITH PANDAS BEYOND EXCEL ---/198 Tabular Data and Pandas DataFrames.mp423.02MB
188. 20 Pandas Basics - Starting from Zero/199 First Steps (Inspection of Data Part 1).mp453.76MB
189. 20 Pandas Basics - Starting from Zero/200 First Steps (Inspection of Data Part 2).mp442.83MB
190. 20 Pandas Basics - Starting from Zero/201 Built-in Functions Attributes and Methods.mp443.9MB
191. 20 Pandas Basics - Starting from Zero/203 Explore your own Dataset Coding Exercise 1 (Solution).mp434.75MB
192. 20 Pandas Basics - Starting from Zero/204 Selecting Columns.mp433.01MB
193. 20 Pandas Basics - Starting from Zero/205 Selecting Rows with Square Brackets (not advisable).mp418.75MB
194. 20 Pandas Basics - Starting from Zero/206 Selecting Rows with iloc (position-based indexing).mp445.9MB
195. 20 Pandas Basics - Starting from Zero/207 Slicing Rows and Columns with iloc (position-based indexing).mp420.57MB
196. 20 Pandas Basics - Starting from Zero/209 Selecting Rows with loc (label-based indexing).mp425.26MB
197. 20 Pandas Basics - Starting from Zero/210 Slicing Rows and Columns with loc (label-based indexing).mp480.34MB
198. 20 Pandas Basics - Starting from Zero/212 Summary and Outlook.mp454.3MB
199. 20 Pandas Basics - Starting from Zero/214 Coding Exercise 2 (Solution).mp443.05MB
200. 21 Pandas Intermediate/216 First Steps with Pandas Series.mp430.45MB
201. 21 Pandas Intermediate/217 Analyzing Numerical Series with unique() nunique() and value_counts().mp460.66MB
202. 21 Pandas Intermediate/219 EXCURSUS Updating Pandas Anaconda.mp458.41MB
203. 21 Pandas Intermediate/220 Analyzing non-numerical Series with unique() nunique() value_counts().mp436.16MB
204. 21 Pandas Intermediate/221 The copy() method.mp420.75MB
205. 21 Pandas Intermediate/222 Sorting of Series and Introduction to the inplace - parameter.mp433.42MB
206. 21 Pandas Intermediate/224 Coding Exercise 3 (Solution).mp431.06MB
207. 21 Pandas Intermediate/225 First Steps with Pandas Index Objects.mp437.15MB
208. 21 Pandas Intermediate/226 Changing Row Index with set_index() and reset_index().mp463.04MB
209. 21 Pandas Intermediate/227 Changing Column Labels.mp417.95MB
210. 21 Pandas Intermediate/228 Renaming Index Column Labels with rename().mp427.99MB
211. 21 Pandas Intermediate/230 Coding Exercise 4 (Solution).mp423.47MB
212. 21 Pandas Intermediate/231 Sorting DataFrames with sort_index() and sort_values().mp444.25MB
213. 21 Pandas Intermediate/232 nunique() and nlargest() nsmallest() with DataFrames.mp426.7MB
214. 21 Pandas Intermediate/233 Filtering DataFrames (one Condition).mp444.83MB
215. 21 Pandas Intermediate/234 Filtering DataFrames by many Conditions (AND).mp421.24MB
216. 21 Pandas Intermediate/235 Filtering DataFrames by many Conditions (OR).mp425.81MB
217. 21 Pandas Intermediate/236 Advanced Filtering with between() isin() and ~.mp454.42MB
218. 21 Pandas Intermediate/237 any() and all().mp414.26MB
219. 21 Pandas Intermediate/239 Coding Exercise 5 (Solution).mp462.92MB
220. 21 Pandas Intermediate/240 Intro to NA Values missing Values.mp438.18MB
221. 21 Pandas Intermediate/241 Handling NA Values missing Values.mp456.25MB
222. 21 Pandas Intermediate/242 Exporting DataFrames to csv.mp410.58MB
223. 21 Pandas Intermediate/243 Summary Statistics and Accumulations.mp446.94MB
224. 21 Pandas Intermediate/244 The agg() method.mp418.85MB
225. 21 Pandas Intermediate/246 Coding Exercise 6 (Solution).mp472.82MB
226. 22 Data Visualization with Pandas Matplotlib and Seaborn/248 Visualization with Matplotlib (Intro).mp459.11MB
227. 22 Data Visualization with Pandas Matplotlib and Seaborn/249 Customization of Plots.mp484.13MB
228. 22 Data Visualization with Pandas Matplotlib and Seaborn/250 Histogramms (Part 1).mp420.48MB
229. 22 Data Visualization with Pandas Matplotlib and Seaborn/251 Histogramms (Part 2).mp428.86MB
230. 22 Data Visualization with Pandas Matplotlib and Seaborn/252 Scatterplots.mp429.45MB
231. 22 Data Visualization with Pandas Matplotlib and Seaborn/253 First Steps with Seaborn.mp418.24MB
232. 22 Data Visualization with Pandas Matplotlib and Seaborn/254 Categorical Seaborn Plots.mp470.73MB
233. 22 Data Visualization with Pandas Matplotlib and Seaborn/255 Seaborn Regression Plots.mp466.51MB
234. 22 Data Visualization with Pandas Matplotlib and Seaborn/256 Seaborn Heatmaps.mp435.71MB
235. 22 Data Visualization with Pandas Matplotlib and Seaborn/258 Coding Exercise 7 (Solution).mp454.06MB
236. 23 Pandas Advanced/260 Removing Columns.mp429.61MB
237. 23 Pandas Advanced/261 Removing Rows.mp441.36MB
238. 23 Pandas Advanced/262 Adding new Columns to a DataFrame.mp415.05MB
239. 23 Pandas Advanced/263 Arithmetic Operations (Part 1).mp452.78MB
240. 23 Pandas Advanced/264 Arithmetic Operations (Part 2).mp448.71MB
241. 23 Pandas Advanced/265 Creating DataFrames from Scratch with pd.DataFrame().mp443.39MB
242. 23 Pandas Advanced/266 Adding new Rows (Hands-on).mp416.99MB
243. 23 Pandas Advanced/267 Adding new Rows to a DataFrame.mp476.19MB
244. 23 Pandas Advanced/268 Manipulating Elements in a DataFrame.mp425.93MB
245. 23 Pandas Advanced/270 Coding Exercise 8 (Solution).mp447.87MB
246. 23 Pandas Advanced/271 Introduction to GroupBy Operations.mp48.09MB
247. 23 Pandas Advanced/272 Understanding the GroupBy Object.mp439.41MB
248. 23 Pandas Advanced/273 Splitting with many Keys.mp442.23MB
249. 23 Pandas Advanced/274 split-apply-combine.mp440.1MB
250. 23 Pandas Advanced/275 split-apply-combine applied.mp459.11MB
251. 23 Pandas Advanced/276 Hierarchical Indexing with Groupby.mp427MB
252. 23 Pandas Advanced/277 stack() and unstack().mp466.07MB
253. 23 Pandas Advanced/279 Coding Exercise 9 (Solution).mp437.74MB
254. 24 Managing Time Series and Financial Data with Pandas/280 Importing Time Series Data from csv-files.mp434.41MB
255. 24 Managing Time Series and Financial Data with Pandas/281 Converting strings to datetime objects with pd.to_datetime().mp448.85MB
256. 24 Managing Time Series and Financial Data with Pandas/282 Initial Analysis Visualization of Time Series.mp428.93MB
257. 24 Managing Time Series and Financial Data with Pandas/283 Indexing and Slicing Time Series.mp440.63MB
258. 24 Managing Time Series and Financial Data with Pandas/284 Creating a customized DatetimeIndex with pd.date_range().mp493.69MB
259. 24 Managing Time Series and Financial Data with Pandas/285 More on pd.date_range().mp49.77MB
260. 24 Managing Time Series and Financial Data with Pandas/287 Coding Exercise 10 (Solution).mp438.42MB
261. 24 Managing Time Series and Financial Data with Pandas/288 Downsampling Time Series with resample() (Part 1).mp472.16MB
262. 24 Managing Time Series and Financial Data with Pandas/289 Downsampling Time Series with resample (Part 2).mp441.45MB
263. 24 Managing Time Series and Financial Data with Pandas/290 The PeriodIndex object.mp433.44MB
264. 24 Managing Time Series and Financial Data with Pandas/291 Advanced Indexing with reindex().mp442.2MB
265. 24 Managing Time Series and Financial Data with Pandas/293 Coding Exercise 11 (Solution).mp438.21MB
266. 24 Managing Time Series and Financial Data with Pandas/294 Getting Ready (Installing required library).mp415.32MB
267. 24 Managing Time Series and Financial Data with Pandas/295 Importing Stock Price Data from Yahoo Finance (it still works).mp458.48MB
268. 24 Managing Time Series and Financial Data with Pandas/296 Initial Inspection and Visualization.mp436.35MB
269. 24 Managing Time Series and Financial Data with Pandas/297 Normalizing Time Series to a Base Value (100).mp437.35MB
270. 24 Managing Time Series and Financial Data with Pandas/298 The shift() method.mp429.49MB
271. 24 Managing Time Series and Financial Data with Pandas/299 The methods diff() and pct_change().mp432.72MB
272. 24 Managing Time Series and Financial Data with Pandas/300 Measuring Stock Performance with MEAN Returns and STD of Returns.mp434.87MB
273. 24 Managing Time Series and Financial Data with Pandas/301 Financial Time Series - Return and Risk.mp444.9MB
274. 24 Managing Time Series and Financial Data with Pandas/302 Financial Time Series - Covariance and Correlation.mp421.04MB
275. 24 Managing Time Series and Financial Data with Pandas/303 Importing Financial Data from Excel.mp477.87MB
276. 24 Managing Time Series and Financial Data with Pandas/304 Merging Aligning Financial Time Series (hands-on).mp425.9MB
277. 24 Managing Time Series and Financial Data with Pandas/306 Coding Exercise 12 (Solution).mp447.76MB
278. 25 Creating analyzing and optimizing Financial Portfolios with Python/307 Intro.mp417.24MB
279. 25 Creating analyzing and optimizing Financial Portfolios with Python/308 Getting the Data.mp412.24MB
280. 25 Creating analyzing and optimizing Financial Portfolios with Python/309 Creating the equally-weighted Portfolio.mp445.78MB
281. 25 Creating analyzing and optimizing Financial Portfolios with Python/310 Creating many random Portfolios with Python.mp472.48MB
282. 25 Creating analyzing and optimizing Financial Portfolios with Python/311 What is the Sharpe Ratio and a Risk Free Asset.mp416.87MB
283. 25 Creating analyzing and optimizing Financial Portfolios with Python/312 Portfolio Analysis and the Sharpe Ratio with Python.mp444.86MB
284. 25 Creating analyzing and optimizing Financial Portfolios with Python/313 Finding the Optimal Portfolio.mp439.05MB
285. 25 Creating analyzing and optimizing Financial Portfolios with Python/314 Sharpe Ratio - visualized and explained.mp423.23MB
286. 25 Creating analyzing and optimizing Financial Portfolios with Python/316 Coding Exercise 13 (Solution).mp477.7MB
287. 25 Creating analyzing and optimizing Financial Portfolios with Python/317 Intro CAPM.mp47.99MB
288. 25 Creating analyzing and optimizing Financial Portfolios with Python/318 Capital Market Line (CML) Two-Fund-Theorem.mp415.81MB
289. 25 Creating analyzing and optimizing Financial Portfolios with Python/319 The Portfolio Diversification Effect.mp470.99MB
290. 25 Creating analyzing and optimizing Financial Portfolios with Python/320 Systematic vs. unsystematic Risk.mp459.51MB
291. 25 Creating analyzing and optimizing Financial Portfolios with Python/321 Capital Asset Pricing Model (CAPM) Security Market Line (SLM).mp439.19MB
292. 25 Creating analyzing and optimizing Financial Portfolios with Python/322 Beta and Alpha.mp433.6MB
293. 25 Creating analyzing and optimizing Financial Portfolios with Python/323 Redefining the Market Portfolio.mp436.67MB
294. 25 Creating analyzing and optimizing Financial Portfolios with Python/324 Cyclical vs. non-cyclical Stocks - another Intuition on Beta.mp432.71MB
295. 25 Creating analyzing and optimizing Financial Portfolios with Python/326 Coding Exercise 14 (Solution).mp459.21MB
296. 26 --- PART 5 REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) ---/327 Introduction to Regression Analysis.mp450.63MB
297. 26 --- PART 5 REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) ---/328 Coding Projects Part 5 - Overview.mp416.49MB
298. 27 Correlation and Regression/330 Cleaning and preparing the Data - Movies Database (Part 1).mp447.03MB
299. 27 Correlation and Regression/331 Cleaning and preparing the Data - Movies Database (Part 2).mp431.12MB
300. 27 Correlation and Regression/332 Covariance and Correlation Coefficient (Theory).mp427.58MB
301. 27 Correlation and Regression/333 How to calculate Covariance and Correlation in Python.mp423.98MB
302. 27 Correlation and Regression/334 Correlation and Scatterplots visual Interpretation.mp420MB
303. 27 Correlation and Regression/335 Creating a Confidence Interval for the Correlation Coefficient (Bootstrapping).mp438.94MB
304. 27 Correlation and Regression/336 Testing for Correlation (t-Test).mp416.57MB
305. 27 Correlation and Regression/337 What is Linear Regression (Theory).mp411.64MB
306. 27 Correlation and Regression/338 A simple Linear Regression Model with numpy Scipy.mp439.72MB
307. 27 Correlation and Regression/339 How to interpret Intercept and Slope Coefficient.mp412.33MB
308. 27 Correlation and Regression/340 Case Study (Part 1) The Market Model (Single Factor Model).mp426.32MB
309. 27 Correlation and Regression/341 Case Study (Part 2) The Market Model (Single Factor Model).mp410.31MB
310. 28 OLS Regression ANOVA and Hypothesis Testing/343 OLS (Ordinary Least Squares) Regression (Theory).mp47.26MB
311. 28 OLS Regression ANOVA and Hypothesis Testing/344 OLS Regression with statsmodels - Intro.mp448.78MB
312. 28 OLS Regression ANOVA and Hypothesis Testing/345 OLS Regression - ANOVA (Theory).mp436.35MB
313. 28 OLS Regression ANOVA and Hypothesis Testing/346 OLS Regression with Statsmodels - ANOVA.mp417.82MB
314. 28 OLS Regression ANOVA and Hypothesis Testing/347 Coefficient of Determination (R squared).mp46.88MB
315. 28 OLS Regression ANOVA and Hypothesis Testing/348 OLS Regression with statsmodels and DataFrames.mp422.04MB
316. 28 OLS Regression ANOVA and Hypothesis Testing/349 Confidence Intervals for Regression Coefficients - Bootstrapping.mp453.48MB
317. 28 OLS Regression ANOVA and Hypothesis Testing/350 Hypothesis Testing of Regression Coefficients (Theory).mp415.51MB
318. 28 OLS Regression ANOVA and Hypothesis Testing/351 Hypothesis Testing of Regression Coefficients with statsmodels.mp417.77MB
319. 28 OLS Regression ANOVA and Hypothesis Testing/352 Regression Analysis with statsmodels - the Summary Table.mp420.94MB
320. 28 OLS Regression ANOVA and Hypothesis Testing/353 Case Study (Part 3) The Market Model (Single Factor Model).mp428.93MB
321. 29 Multiple Regression Models/355 Multiple Regression (Theory).mp425.94MB
322. 29 Multiple Regression Models/356 Movies Dataset - Preparing the Data.mp449.6MB
323. 29 Multiple Regression Models/357 Multiple Regression Analysis with statsmodels.mp431.27MB
324. 29 Multiple Regression Models/358 Coefficient of Determination (Adjusted R squared).mp415.02MB
325. 29 Multiple Regression Models/359 Regression Coefficients Hypothesis Testing Model Specification.mp453.61MB
326. 29 Multiple Regression Models/360 How to test the Significance of the Model as a whole (F-Test).mp420.33MB
327. 29 Multiple Regression Models/361 Creating and working with Dummy Variables (Part 1).mp454.41MB
328. 29 Multiple Regression Models/362 Creating and working with Dummy Variables (Part 2).mp443.97MB
329. 30 Case Study Multi-Factor Models (Fama-French)/364 Fama-French An Introduction.mp452.56MB
330. 30 Case Study Multi-Factor Models (Fama-French)/365 Single-Factor Models with the Fama-French Market Portfolio (Part 1).mp463.57MB
331. 30 Case Study Multi-Factor Models (Fama-French)/366 Single-Factor Models with the Fama-French Market Portfolio (Part 2).mp439.08MB
332. 30 Case Study Multi-Factor Models (Fama-French)/367 The Factors Size Value.mp449.08MB
333. 30 Case Study Multi-Factor Models (Fama-French)/368 How to create a Fama-French Three-Factor Model.mp455.42MB
334. 30 Case Study Multi-Factor Models (Fama-French)/369 The Factors Profitability and Investment.mp421.81MB
335. 30 Case Study Multi-Factor Models (Fama-French)/370 How to create a Fama-French Five-Factor Model.mp446.58MB
336. 31 Issues in Linear Regression Analysis and Logistic Regression/372 Linear Regression - not that easy.mp424.27MB
337. 31 Issues in Linear Regression Analysis and Logistic Regression/373 Detecting and Handling Outliers (Part 1).mp467.97MB
338. 31 Issues in Linear Regression Analysis and Logistic Regression/374 Detecting and Handling Outliers (Part 2).mp421.54MB
339. 31 Issues in Linear Regression Analysis and Logistic Regression/375 Non-Linear Relationships - Feature Transformation.mp422.64MB
340. 31 Issues in Linear Regression Analysis and Logistic Regression/376 Detecting and Handling Multicollinearity.mp448.52MB
341. 31 Issues in Linear Regression Analysis and Logistic Regression/377 Detecting and Correcting Heteroskedasticity.mp461.3MB
342. 31 Issues in Linear Regression Analysis and Logistic Regression/378 Detecting and Handling Serial Correlation (Autocorrelation).mp479.35MB
343. 31 Issues in Linear Regression Analysis and Logistic Regression/379 Logistic Regression (Theory).mp415.05MB
344. 31 Issues in Linear Regression Analysis and Logistic Regression/380 Logistic Regression with statsmodels (Part 1).mp431.14MB
345. 31 Issues in Linear Regression Analysis and Logistic Regression/381 Logistic Regression with statsmodels (Part 2).mp440.77MB