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种子名称： GetFreeCourses.Co-Udemy-Complete 2-in-1 Python for Business and Finance Bootcamp
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文件数目： 345个文件
文件大小： 10.92 GB
收录时间： 2021-8-12 17:33
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最近下载： 2024-11-23 04:33

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GetFreeCourses.Co-Udemy-Complete 2-in-1 Python for Business and Finance Bootcamp.torrent

01 Getting Started/001 Tips How to get the most out of this Course (don t skip).mp437.57MB

01 Getting Started/003 How to download and install Anaconda for Python coding.mp471MB

01 Getting Started/004 Jupyter Notebooks - let s get started.mp450.91MB

01 Getting Started/005 How to work with Jupyter Notebooks.mp453.47MB

02 ---- PART 1 PYTHON BASICS TIME VALUE OF MONEY AND CAPITAL BUDGETING ----/006 Overview Download of Course Materials for Part 1.mp430.37MB

02 ---- PART 1 PYTHON BASICS TIME VALUE OF MONEY AND CAPITAL BUDGETING ----/007 Coding Projects Part 1 - Overview.mp415.36MB

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

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

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

03 How to use Python as a Calculator for basic Time Value of Money Problems/011 Interest Rates and Returns (Theory).mp414.19MB

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

03 How to use Python as a Calculator for basic Time Value of Money Problems/013 Introduction to Variables.mp418.13MB

03 How to use Python as a Calculator for basic Time Value of Money Problems/014 Variables and Memory (Theory).mp45.48MB

03 How to use Python as a Calculator for basic Time Value of Money Problems/015 Excursus How to add inline comments.mp411.25MB

03 How to use Python as a Calculator for basic Time Value of Money Problems/016 More on Variables and Memory.mp422.22MB

03 How to use Python as a Calculator for basic Time Value of Money Problems/017 Variables - Dos Don ts and Conventions.mp417.06MB

03 How to use Python as a Calculator for basic Time Value of Money Problems/018 The print() Function.mp417.41MB

03 How to use Python as a Calculator for basic Time Value of Money Problems/019 Coding Exercise 1.mp446.86MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/020 TVM Problems with many Cashflows.mp410.49MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/021 Intro to Python Lists.mp47.76MB

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

04 How to use Lists and For Loops for TVM Problems with many Cashflows/023 Indexing Lists.mp413.86MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/024 For Loops - Iterating over Lists.mp429.91MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/025 The range Object - another Iterable.mp417.09MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/026 Calculate FV and PV for many Cashflows.mp433.53MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/027 The Net Present Value - NPV (Theory).mp433.29MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/028 Calculate an Investment Project s NPV.mp414.34MB

04 How to use Lists and For Loops for TVM Problems with many Cashflows/029 Coding Exercise 2.mp437.98MB

05 100 Python Objects Data Types Operators Functional Programming/030 Data Types in Action.mp424.34MB

05 100 Python Objects Data Types Operators Functional Programming/031 The Data Type Hierarchy (Theory).mp410.78MB

05 100 Python Objects Data Types Operators Functional Programming/032 Excursus Dynamic Typing in Python.mp45.19MB

05 100 Python Objects Data Types Operators Functional Programming/033 Build-in Functions.mp425.37MB

05 100 Python Objects Data Types Operators Functional Programming/034 Integers.mp410.98MB

05 100 Python Objects Data Types Operators Functional Programming/035 Floats.mp424.33MB

05 100 Python Objects Data Types Operators Functional Programming/036 How to round Floats (and Integers) with round().mp420.91MB

05 100 Python Objects Data Types Operators Functional Programming/037 More on Lists.mp424.6MB

05 100 Python Objects Data Types Operators Functional Programming/038 Lists and Element-wise Operations.mp417.59MB

05 100 Python Objects Data Types Operators Functional Programming/039 Slicing Lists.mp420.12MB

05 100 Python Objects Data Types Operators Functional Programming/041 Changing Elements in Lists.mp410.11MB

05 100 Python Objects Data Types Operators Functional Programming/042 Sorting and Reversing Lists.mp413.19MB

05 100 Python Objects Data Types Operators Functional Programming/043 Adding and removing Elements fromto Lists.mp438.54MB

05 100 Python Objects Data Types Operators Functional Programming/044 Mutable vs. immutable Objects (Part 1).mp434.49MB

05 100 Python Objects Data Types Operators Functional Programming/045 Mutable vs. immutable Objects (Part 2).mp421.85MB

05 100 Python Objects Data Types Operators Functional Programming/046 Coding Exercise 3.mp453.78MB

05 100 Python Objects Data Types Operators Functional Programming/047 Tuples.mp429.78MB

05 100 Python Objects Data Types Operators Functional Programming/048 Dictionaries.mp431.01MB

05 100 Python Objects Data Types Operators Functional Programming/049 Intro to Strings.mp440.84MB

05 100 Python Objects Data Types Operators Functional Programming/050 String Replacement.mp417.3MB

05 100 Python Objects Data Types Operators Functional Programming/051 Booleans.mp48.87MB

05 100 Python Objects Data Types Operators Functional Programming/052 Operators (Theory).mp411.71MB

05 100 Python Objects Data Types Operators Functional Programming/053 Comparison Logical and Membership Operators in Action.mp435.52MB

05 100 Python Objects Data Types Operators Functional Programming/054 Coding Exercise 4.mp442.3MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/055 Conditional Statements.mp438.63MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/056 Keywords pass continue and break.mp439.4MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/057 Calculate a Project s Payback Period.mp421.87MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/058 While Loops.mp435.63MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/059 The Internal Rate of Return - IRR (Theory).mp423.91MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/060 Solving for a Project s IRR.mp460.77MB

06 How to solve for IRR YTM with While Loops and Conditional Statements/061 Bonds and the Yield to Maturity - YTM (Theory).mp436.99MB

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

06 How to solve for IRR YTM with While Loops and Conditional Statements/063 Coding Exercise 5.mp455.81MB

07 How to create great graphs with Matplotlib - Plotting NPV and IRR/064 Intro.mp414.48MB

07 How to create great graphs with Matplotlib - Plotting NPV and IRR/065 Line Plots.mp423.34MB

07 How to create great graphs with Matplotlib - Plotting NPV and IRR/066 Scatter Plots.mp47.22MB

07 How to create great graphs with Matplotlib - Plotting NPV and IRR/067 Customizing Plots (Part 1).mp424.42MB

07 How to create great graphs with Matplotlib - Plotting NPV and IRR/068 Customizing Plots (Part 2).mp480.42MB

07 How to create great graphs with Matplotlib - Plotting NPV and IRR/069 Plotting NPV IRR.mp440.68MB

08 The Numpy Package Working with numbers made easy/071 Modules Packages and Libraries - No need to reinvent the Wheel.mp432.03MB

08 The Numpy Package Working with numbers made easy/072 Numpy Arrays.mp435.72MB

08 The Numpy Package Working with numbers made easy/073 Indexing and Slicing Numpy Arrays.mp413.67MB

08 The Numpy Package Working with numbers made easy/074 Vectorized Operations with Numpy Arrays.mp418.73MB

08 The Numpy Package Working with numbers made easy/075 Changing Elements in Numpy Arrays Mutability.mp424.52MB

08 The Numpy Package Working with numbers made easy/076 View vs. copy - potential Pitfalls when slicing Numpy Arrays.mp419.27MB

08 The Numpy Package Working with numbers made easy/077 Numpy Array Methods and Attributes.mp421.97MB

08 The Numpy Package Working with numbers made easy/078 Numpy Universal Functions.mp417.77MB

08 The Numpy Package Working with numbers made easy/079 Boolean Arrays and Conditional Filtering.mp418.14MB

08 The Numpy Package Working with numbers made easy/080 Advanced Filtering Bitwise Operators.mp428.31MB

08 The Numpy Package Working with numbers made easy/081 Determining a Project s Payback Period with np.where().mp422.53MB

08 The Numpy Package Working with numbers made easy/082 Creating Numpy Arrays from Scratch.mp437.86MB

08 The Numpy Package Working with numbers made easy/083 Coding Exercise 7.mp473.13MB

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

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/085 Evaluating Annuities with np.fv() - Funding Phase.mp432.8MB

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/086 Evaluating Annuities with np.fv() - Payout Phase.mp424.4MB

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

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

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

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

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

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/092 Retirement Plan Sensitivity Analysis.mp430.76MB

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/093 Mortgage Loan Analysis - Debt Sizing.mp439.07MB

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

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

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/096 Capital Budgeting - Mutually exclusive Projects (Part 1).mp423.06MB

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/097 Capital Budgeting - Mutually exclusive Projects (Part 2).mp440.99MB

09 How to solve complex TVM and Capital Budgeting problems with Python and Numpy/098 Capital Budgeting - Mutually exclusive Projects (Part 3).mp418.67MB

10 --- PART 2 STATISTICS AND HYPOTHESIS TESTING WITH PYTHON NUMPY AND SCIPY ---/100 Statistics - Overview Terms and Vocabulary.mp495.73MB

10 --- PART 2 STATISTICS AND HYPOTHESIS TESTING WITH PYTHON NUMPY AND SCIPY ---/101 Coding Projects Part 2 - Overview.mp421.07MB

10 --- PART 2 STATISTICS AND HYPOTHESIS TESTING WITH PYTHON NUMPY AND SCIPY ---/102 Download of Part 2 Course Materials.mp430.19MB

11 How to perform Descriptive Statistics on Populations and Samples/103 Population vs. Sample.mp443.91MB

11 How to perform Descriptive Statistics on Populations and Samples/104 Visualizing Frequency Distributions with plt.hist().mp422.65MB

11 How to perform Descriptive Statistics on Populations and Samples/105 Relative and Cumulative Frequencies with plt.hist().mp436.43MB

11 How to perform Descriptive Statistics on Populations and Samples/106 Measures of Central Tendency (Theory).mp420.74MB

11 How to perform Descriptive Statistics on Populations and Samples/107 Coding Measures of Central Tendency - Mean and Median.mp422.33MB

11 How to perform Descriptive Statistics on Populations and Samples/108 Coding Measures of Central Tendency - Geometric Mean.mp416.56MB

11 How to perform Descriptive Statistics on Populations and Samples/109 Excursus Why Log Returns are useful.mp412.4MB

11 How to perform Descriptive Statistics on Populations and Samples/110 Variability around the Central Tendency Dispersion (Theory).mp427.68MB

11 How to perform Descriptive Statistics on Populations and Samples/111 Minimum Maximum and Range with PythonNumpy.mp412.3MB

11 How to perform Descriptive Statistics on Populations and Samples/112 Percentiles with PythonNumpy.mp417.57MB

11 How to perform Descriptive Statistics on Populations and Samples/113 Variance and Standard Deviation with PythonNumpy.mp416.35MB

11 How to perform Descriptive Statistics on Populations and Samples/114 Skew and Kurtosis (Theory).mp418.03MB

11 How to perform Descriptive Statistics on Populations and Samples/115 How to calculate Skew and Kurtosis with scipy.stats.mp427.45MB

12 Common Probability Distributions and how to construct Confidence Intervals/117 How to generate Random Numbers with Numpy.mp425.19MB

12 Common Probability Distributions and how to construct Confidence Intervals/118 Reproducibility with np.random.seed().mp417.25MB

12 Common Probability Distributions and how to construct Confidence Intervals/119 Probability Distributions - Overview.mp435.68MB

12 Common Probability Distributions and how to construct Confidence Intervals/120 Discrete Uniform Distributions.mp428.2MB

12 Common Probability Distributions and how to construct Confidence Intervals/121 Continuous Uniform Distributions.mp420.11MB

12 Common Probability Distributions and how to construct Confidence Intervals/122 The Normal Distribution (Theory).mp418.43MB

12 Common Probability Distributions and how to construct Confidence Intervals/123 Creating a normally distributed Random Variable.mp424.11MB

12 Common Probability Distributions and how to construct Confidence Intervals/124 Normal Distribution - Probability Density Function (pdf) with scipy.stats.mp426.92MB

12 Common Probability Distributions and how to construct Confidence Intervals/125 Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats.mp415.38MB

12 Common Probability Distributions and how to construct Confidence Intervals/126 The Standard Normal Distribution and Z-Values.mp438.63MB

12 Common Probability Distributions and how to construct Confidence Intervals/127 Properties of the Standard Normal Distribution (Theory).mp414.84MB

12 Common Probability Distributions and how to construct Confidence Intervals/128 Probabilities and Z-Values with scipy.stats.mp459.27MB

12 Common Probability Distributions and how to construct Confidence Intervals/129 Confidence Intervals with scipy.stats.mp448.12MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/131 Sample Statistic Sampling Error and Sampling Distribution (Theory).mp433.91MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/132 Sampling with np.random.choice().mp420.64MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/133 Sampling Distribution.mp421.58MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/134 Standard Error.mp410.65MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/135 Central Limit Theorem (Coding Part 1).mp426.3MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/136 Central Limit Theorem (Coding Part 2).mp430.34MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/137 Central Limit Theorem (Theory).mp416.98MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/138 Point Estimates vs. Confidence Interval Estimates (known Population Variance).mp423.42MB

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

13 How to estimate Population parameters with Samples - Sampling and Estimation/140 Unknown Population Variance - the Standard Case (Example 1).mp426.34MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/141 Unknown Population Variance - the Standard Case (Example 2).mp417.8MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/142 Student s t-Distribution vs. Normal Distribution with scipy.stats.mp429.63MB

13 How to estimate Population parameters with Samples - Sampling and Estimation/143 Bootstrapping with Python an alternative method without Statistics.mp428.05MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/145 Hypothesis Testing (Theory).mp450.95MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/146 Two-tailed Z-Test with known Population Variance.mp452.74MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/147 What is the p-value (Theory).mp413.03MB

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

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/149 One-tailed Z-Test with known Population Variance.mp431.2MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/150 Two-tailed t-Test (unknown Population Variance).mp438.81MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/151 One-tailed t-Test (unknown Population Variance).mp415.56MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/152 Hypothesis Testing with Bootstrapping.mp432.46MB

14 How to perform Hypothesis Tests Z-Tests t-Tests Bootstrapping more/153 Testing for Normality of Financial Returns with scipy.stats.mp449.49MB

15 -- PART 3 ADVANCED PYTHON MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) ---/155 Overview Download of Course Materials for Part 3.mp411.28MB

15 -- PART 3 ADVANCED PYTHON MONTE CARLO SIMULATIONS AND VALUE AT RISK (VAR) ---/156 Coding Projects Part 3 - Overview.mp418.91MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/157 How to work with nested Lists.mp418.25MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/158 2-dimensional Numpy Arrays.mp416.13MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/159 How to slice 2-dim Numpy Arrays (Part 1).mp428.92MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/160 How to slice 2-dim Numpy Arrays (Part 2).mp48.76MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/161 Recap Changing Elements in a Numpy Array slice.mp416.51MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/162 How to perform row-wise and column-wise Operations.mp422.48MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/163 Reshaping and Transposing 2-dim Numpy Arrays.mp424.65MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/164 Creating 2-dim Numpy Arrays from Scratch.mp416.88MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/165 Arithmetic Vectorized Operations with 2-dim Numpy Arrays.mp427.5MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/166 The keepdims parameter.mp420.76MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/167 Adding Removing Elements.mp416.49MB

16 n-dimensional Numpy Arrays How to work with numerical Tabular Data/168 Merging and Concatenating Numpy Arrays.mp418.72MB

17 How to create your own user-defined Functions/170 Defining your first user-defined Function.mp427.36MB

17 How to create your own user-defined Functions/171 What s the difference between Positional Arguments vs. Keyword Arguments.mp436.35MB

17 How to create your own user-defined Functions/172 How to work with Default Arguments.mp428.47MB

17 How to create your own user-defined Functions/173 The Default Argument None.mp426.8MB

17 How to create your own user-defined Functions/174 How to unpack Iterables.mp418.62MB

17 How to create your own user-defined Functions/175 Sequences as arguments and args.mp426.26MB

17 How to create your own user-defined Functions/176 How to return many results.mp413.44MB

17 How to create your own user-defined Functions/177 Scope - easily explained.mp435.27MB

17 How to create your own user-defined Functions/178 How to create Nested Functions.mp430.12MB

17 How to create your own user-defined Functions/179 Putting it all together - Case Study.mp469.22MB

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/181 What is the Value-at-Risk (VaR) (Theory).mp420.39MB

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/182 Analyzing the Data past Performance.mp425.46MB

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

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

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

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

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

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

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

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/190 Conditional Value-at-Risk (CVaR).mp419.47MB

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/191 Dynamic path-dependent Simulations (Part 1).mp441.54MB

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/192 Dynamic path-dependent Simulations (Part 2).mp467.44MB

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/193 Dynamic path-dependent Simulations (Part 3).mp417.74MB

18 Monte Carlo Simulations and Value-at-Risk (VAR) with Python and Numpy/194 Dynamic path-dependent Simulations (Part 4).mp465.49MB

19 --- PART 4 MANAGING (FINANCIAL) DATA WITH PANDAS BEYOND EXCEL ---/196 Introduction.mp47.12MB

19 --- PART 4 MANAGING (FINANCIAL) DATA WITH PANDAS BEYOND EXCEL ---/197 Download of Part 4 Course Materials.mp468.4MB

19 --- PART 4 MANAGING (FINANCIAL) DATA WITH PANDAS BEYOND EXCEL ---/198 Tabular Data and Pandas DataFrames.mp423.02MB

20 Pandas Basics - Starting from Zero/199 First Steps (Inspection of Data Part 1).mp453.76MB

20 Pandas Basics - Starting from Zero/200 First Steps (Inspection of Data Part 2).mp442.83MB

20 Pandas Basics - Starting from Zero/201 Built-in Functions Attributes and Methods.mp443.9MB

20 Pandas Basics - Starting from Zero/203 Explore your own Dataset Coding Exercise 1 (Solution).mp434.75MB

20 Pandas Basics - Starting from Zero/204 Selecting Columns.mp433.01MB

20 Pandas Basics - Starting from Zero/205 Selecting Rows with Square Brackets (not advisable).mp418.75MB

20 Pandas Basics - Starting from Zero/206 Selecting Rows with iloc (position-based indexing).mp445.9MB

20 Pandas Basics - Starting from Zero/207 Slicing Rows and Columns with iloc (position-based indexing).mp420.57MB

20 Pandas Basics - Starting from Zero/209 Selecting Rows with loc (label-based indexing).mp425.26MB

20 Pandas Basics - Starting from Zero/210 Slicing Rows and Columns with loc (label-based indexing).mp480.34MB

20 Pandas Basics - Starting from Zero/212 Summary and Outlook.mp454.3MB

20 Pandas Basics - Starting from Zero/214 Coding Exercise 2 (Solution).mp443.05MB

21 Pandas Intermediate/216 First Steps with Pandas Series.mp430.45MB

21 Pandas Intermediate/217 Analyzing Numerical Series with unique() nunique() and value_counts().mp460.66MB

21 Pandas Intermediate/219 EXCURSUS Updating Pandas Anaconda.mp458.41MB

21 Pandas Intermediate/220 Analyzing non-numerical Series with unique() nunique() value_counts().mp436.16MB

21 Pandas Intermediate/221 The copy() method.mp420.75MB

21 Pandas Intermediate/222 Sorting of Series and Introduction to the inplace - parameter.mp433.42MB

21 Pandas Intermediate/224 Coding Exercise 3 (Solution).mp431.06MB

21 Pandas Intermediate/225 First Steps with Pandas Index Objects.mp437.15MB

21 Pandas Intermediate/226 Changing Row Index with set_index() and reset_index().mp463.04MB

21 Pandas Intermediate/227 Changing Column Labels.mp417.95MB

21 Pandas Intermediate/228 Renaming Index Column Labels with rename().mp427.99MB

21 Pandas Intermediate/230 Coding Exercise 4 (Solution).mp423.47MB

21 Pandas Intermediate/231 Sorting DataFrames with sort_index() and sort_values().mp444.25MB

21 Pandas Intermediate/232 nunique() and nlargest() nsmallest() with DataFrames.mp426.7MB

21 Pandas Intermediate/233 Filtering DataFrames (one Condition).mp444.83MB

21 Pandas Intermediate/234 Filtering DataFrames by many Conditions (AND).mp421.24MB

21 Pandas Intermediate/235 Filtering DataFrames by many Conditions (OR).mp425.81MB

21 Pandas Intermediate/236 Advanced Filtering with between() isin() and ~.mp454.42MB

21 Pandas Intermediate/237 any() and all().mp414.26MB

21 Pandas Intermediate/239 Coding Exercise 5 (Solution).mp462.92MB

21 Pandas Intermediate/240 Intro to NA Values missing Values.mp438.18MB

21 Pandas Intermediate/241 Handling NA Values missing Values.mp456.25MB

21 Pandas Intermediate/242 Exporting DataFrames to csv.mp410.58MB

21 Pandas Intermediate/243 Summary Statistics and Accumulations.mp446.94MB

21 Pandas Intermediate/244 The agg() method.mp418.85MB

21 Pandas Intermediate/246 Coding Exercise 6 (Solution).mp472.82MB

22 Data Visualization with Pandas Matplotlib and Seaborn/248 Visualization with Matplotlib (Intro).mp459.11MB

22 Data Visualization with Pandas Matplotlib and Seaborn/249 Customization of Plots.mp484.13MB

22 Data Visualization with Pandas Matplotlib and Seaborn/250 Histogramms (Part 1).mp420.48MB

22 Data Visualization with Pandas Matplotlib and Seaborn/251 Histogramms (Part 2).mp428.86MB

22 Data Visualization with Pandas Matplotlib and Seaborn/252 Scatterplots.mp429.45MB

22 Data Visualization with Pandas Matplotlib and Seaborn/253 First Steps with Seaborn.mp418.24MB

22 Data Visualization with Pandas Matplotlib and Seaborn/254 Categorical Seaborn Plots.mp470.73MB

22 Data Visualization with Pandas Matplotlib and Seaborn/255 Seaborn Regression Plots.mp466.51MB

22 Data Visualization with Pandas Matplotlib and Seaborn/256 Seaborn Heatmaps.mp435.71MB

22 Data Visualization with Pandas Matplotlib and Seaborn/258 Coding Exercise 7 (Solution).mp454.06MB

23 Pandas Advanced/260 Removing Columns.mp429.61MB

23 Pandas Advanced/261 Removing Rows.mp441.36MB

23 Pandas Advanced/262 Adding new Columns to a DataFrame.mp415.05MB

23 Pandas Advanced/263 Arithmetic Operations (Part 1).mp452.78MB

23 Pandas Advanced/264 Arithmetic Operations (Part 2).mp448.71MB

23 Pandas Advanced/265 Creating DataFrames from Scratch with pd.DataFrame().mp443.39MB

23 Pandas Advanced/266 Adding new Rows (Hands-on).mp416.99MB

23 Pandas Advanced/267 Adding new Rows to a DataFrame.mp476.19MB

23 Pandas Advanced/268 Manipulating Elements in a DataFrame.mp425.93MB

23 Pandas Advanced/270 Coding Exercise 8 (Solution).mp447.87MB

23 Pandas Advanced/271 Introduction to GroupBy Operations.mp48.09MB

23 Pandas Advanced/272 Understanding the GroupBy Object.mp439.41MB

23 Pandas Advanced/273 Splitting with many Keys.mp442.23MB

23 Pandas Advanced/274 split-apply-combine.mp440.1MB

23 Pandas Advanced/275 split-apply-combine applied.mp459.11MB

23 Pandas Advanced/276 Hierarchical Indexing with Groupby.mp427MB

23 Pandas Advanced/277 stack() and unstack().mp466.07MB

23 Pandas Advanced/279 Coding Exercise 9 (Solution).mp437.74MB

24 Managing Time Series and Financial Data with Pandas/280 Importing Time Series Data from csv-files.mp434.41MB

24 Managing Time Series and Financial Data with Pandas/281 Converting strings to datetime objects with pd.to_datetime().mp448.85MB

24 Managing Time Series and Financial Data with Pandas/282 Initial Analysis Visualization of Time Series.mp428.93MB

24 Managing Time Series and Financial Data with Pandas/283 Indexing and Slicing Time Series.mp440.63MB

24 Managing Time Series and Financial Data with Pandas/284 Creating a customized DatetimeIndex with pd.date_range().mp493.69MB

24 Managing Time Series and Financial Data with Pandas/285 More on pd.date_range().mp49.77MB

24 Managing Time Series and Financial Data with Pandas/287 Coding Exercise 10 (Solution).mp438.42MB

24 Managing Time Series and Financial Data with Pandas/288 Downsampling Time Series with resample() (Part 1).mp472.16MB

24 Managing Time Series and Financial Data with Pandas/289 Downsampling Time Series with resample (Part 2).mp441.45MB

24 Managing Time Series and Financial Data with Pandas/290 The PeriodIndex object.mp433.44MB

24 Managing Time Series and Financial Data with Pandas/291 Advanced Indexing with reindex().mp442.2MB

24 Managing Time Series and Financial Data with Pandas/293 Coding Exercise 11 (Solution).mp438.21MB

24 Managing Time Series and Financial Data with Pandas/294 Getting Ready (Installing required library).mp415.32MB

24 Managing Time Series and Financial Data with Pandas/295 Importing Stock Price Data from Yahoo Finance (it still works).mp458.48MB

24 Managing Time Series and Financial Data with Pandas/296 Initial Inspection and Visualization.mp436.35MB

24 Managing Time Series and Financial Data with Pandas/297 Normalizing Time Series to a Base Value (100).mp437.35MB

24 Managing Time Series and Financial Data with Pandas/298 The shift() method.mp429.49MB

24 Managing Time Series and Financial Data with Pandas/299 The methods diff() and pct_change().mp432.72MB

24 Managing Time Series and Financial Data with Pandas/300 Measuring Stock Performance with MEAN Returns and STD of Returns.mp434.87MB

24 Managing Time Series and Financial Data with Pandas/301 Financial Time Series - Return and Risk.mp444.9MB

24 Managing Time Series and Financial Data with Pandas/302 Financial Time Series - Covariance and Correlation.mp421.04MB

24 Managing Time Series and Financial Data with Pandas/303 Importing Financial Data from Excel.mp477.87MB

24 Managing Time Series and Financial Data with Pandas/304 Merging Aligning Financial Time Series (hands-on).mp425.9MB

24 Managing Time Series and Financial Data with Pandas/306 Coding Exercise 12 (Solution).mp447.76MB

25 Creating analyzing and optimizing Financial Portfolios with Python/307 Intro.mp417.24MB

25 Creating analyzing and optimizing Financial Portfolios with Python/308 Getting the Data.mp412.24MB

25 Creating analyzing and optimizing Financial Portfolios with Python/309 Creating the equally-weighted Portfolio.mp445.78MB

25 Creating analyzing and optimizing Financial Portfolios with Python/310 Creating many random Portfolios with Python.mp472.48MB

25 Creating analyzing and optimizing Financial Portfolios with Python/311 What is the Sharpe Ratio and a Risk Free Asset.mp416.87MB

25 Creating analyzing and optimizing Financial Portfolios with Python/312 Portfolio Analysis and the Sharpe Ratio with Python.mp444.86MB

25 Creating analyzing and optimizing Financial Portfolios with Python/313 Finding the Optimal Portfolio.mp439.05MB

25 Creating analyzing and optimizing Financial Portfolios with Python/314 Sharpe Ratio - visualized and explained.mp423.23MB

25 Creating analyzing and optimizing Financial Portfolios with Python/316 Coding Exercise 13 (Solution).mp477.7MB

25 Creating analyzing and optimizing Financial Portfolios with Python/317 Intro CAPM.mp47.99MB

25 Creating analyzing and optimizing Financial Portfolios with Python/318 Capital Market Line (CML) Two-Fund-Theorem.mp415.81MB

25 Creating analyzing and optimizing Financial Portfolios with Python/319 The Portfolio Diversification Effect.mp470.99MB

25 Creating analyzing and optimizing Financial Portfolios with Python/320 Systematic vs. unsystematic Risk.mp459.51MB

25 Creating analyzing and optimizing Financial Portfolios with Python/321 Capital Asset Pricing Model (CAPM) Security Market Line (SLM).mp439.19MB

25 Creating analyzing and optimizing Financial Portfolios with Python/322 Beta and Alpha.mp433.6MB

25 Creating analyzing and optimizing Financial Portfolios with Python/323 Redefining the Market Portfolio.mp436.67MB

25 Creating analyzing and optimizing Financial Portfolios with Python/324 Cyclical vs. non-cyclical Stocks - another Intuition on Beta.mp432.71MB

25 Creating analyzing and optimizing Financial Portfolios with Python/326 Coding Exercise 14 (Solution).mp459.21MB

26 --- PART 5 REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) ---/327 Introduction to Regression Analysis.mp450.63MB

26 --- PART 5 REGRESSION ANALYSIS (A MUST-HAVE FOR MACHINE LEARNING) ---/328 Coding Projects Part 5 - Overview.mp416.49MB

27 Correlation and Regression/330 Cleaning and preparing the Data - Movies Database (Part 1).mp447.03MB

27 Correlation and Regression/331 Cleaning and preparing the Data - Movies Database (Part 2).mp431.12MB

27 Correlation and Regression/332 Covariance and Correlation Coefficient (Theory).mp427.58MB

27 Correlation and Regression/333 How to calculate Covariance and Correlation in Python.mp423.98MB

27 Correlation and Regression/334 Correlation and Scatterplots visual Interpretation.mp420MB

27 Correlation and Regression/335 Creating a Confidence Interval for the Correlation Coefficient (Bootstrapping).mp438.94MB

27 Correlation and Regression/336 Testing for Correlation (t-Test).mp416.57MB

27 Correlation and Regression/337 What is Linear Regression (Theory).mp411.64MB

27 Correlation and Regression/338 A simple Linear Regression Model with numpy Scipy.mp439.72MB

27 Correlation and Regression/339 How to interpret Intercept and Slope Coefficient.mp412.33MB

27 Correlation and Regression/340 Case Study (Part 1) The Market Model (Single Factor Model).mp426.32MB

27 Correlation and Regression/341 Case Study (Part 2) The Market Model (Single Factor Model).mp410.31MB

28 OLS Regression ANOVA and Hypothesis Testing/343 OLS (Ordinary Least Squares) Regression (Theory).mp47.26MB

28 OLS Regression ANOVA and Hypothesis Testing/344 OLS Regression with statsmodels - Intro.mp448.78MB

28 OLS Regression ANOVA and Hypothesis Testing/345 OLS Regression - ANOVA (Theory).mp436.35MB

28 OLS Regression ANOVA and Hypothesis Testing/346 OLS Regression with Statsmodels - ANOVA.mp417.82MB

28 OLS Regression ANOVA and Hypothesis Testing/347 Coefficient of Determination (R squared).mp46.88MB

28 OLS Regression ANOVA and Hypothesis Testing/348 OLS Regression with statsmodels and DataFrames.mp422.04MB

28 OLS Regression ANOVA and Hypothesis Testing/349 Confidence Intervals for Regression Coefficients - Bootstrapping.mp453.48MB

28 OLS Regression ANOVA and Hypothesis Testing/350 Hypothesis Testing of Regression Coefficients (Theory).mp415.51MB

28 OLS Regression ANOVA and Hypothesis Testing/351 Hypothesis Testing of Regression Coefficients with statsmodels.mp417.77MB

28 OLS Regression ANOVA and Hypothesis Testing/352 Regression Analysis with statsmodels - the Summary Table.mp420.94MB

28 OLS Regression ANOVA and Hypothesis Testing/353 Case Study (Part 3) The Market Model (Single Factor Model).mp428.93MB

29 Multiple Regression Models/355 Multiple Regression (Theory).mp425.94MB

29 Multiple Regression Models/356 Movies Dataset - Preparing the Data.mp449.6MB

29 Multiple Regression Models/357 Multiple Regression Analysis with statsmodels.mp431.27MB

29 Multiple Regression Models/358 Coefficient of Determination (Adjusted R squared).mp415.02MB

29 Multiple Regression Models/359 Regression Coefficients Hypothesis Testing Model Specification.mp453.61MB

29 Multiple Regression Models/360 How to test the Significance of the Model as a whole (F-Test).mp420.33MB

29 Multiple Regression Models/361 Creating and working with Dummy Variables (Part 1).mp454.41MB

29 Multiple Regression Models/362 Creating and working with Dummy Variables (Part 2).mp443.97MB

30 Case Study Multi-Factor Models (Fama-French)/364 Fama-French An Introduction.mp452.56MB

30 Case Study Multi-Factor Models (Fama-French)/365 Single-Factor Models with the Fama-French Market Portfolio (Part 1).mp463.57MB

30 Case Study Multi-Factor Models (Fama-French)/366 Single-Factor Models with the Fama-French Market Portfolio (Part 2).mp439.08MB

30 Case Study Multi-Factor Models (Fama-French)/367 The Factors Size Value.mp449.08MB

30 Case Study Multi-Factor Models (Fama-French)/368 How to create a Fama-French Three-Factor Model.mp455.42MB

30 Case Study Multi-Factor Models (Fama-French)/369 The Factors Profitability and Investment.mp421.81MB

30 Case Study Multi-Factor Models (Fama-French)/370 How to create a Fama-French Five-Factor Model.mp446.58MB

31 Issues in Linear Regression Analysis and Logistic Regression/372 Linear Regression - not that easy.mp424.27MB

31 Issues in Linear Regression Analysis and Logistic Regression/373 Detecting and Handling Outliers (Part 1).mp467.97MB

31 Issues in Linear Regression Analysis and Logistic Regression/374 Detecting and Handling Outliers (Part 2).mp421.54MB

31 Issues in Linear Regression Analysis and Logistic Regression/375 Non-Linear Relationships - Feature Transformation.mp422.64MB

31 Issues in Linear Regression Analysis and Logistic Regression/376 Detecting and Handling Multicollinearity.mp448.52MB

31 Issues in Linear Regression Analysis and Logistic Regression/377 Detecting and Correcting Heteroskedasticity.mp461.3MB

31 Issues in Linear Regression Analysis and Logistic Regression/378 Detecting and Handling Serial Correlation (Autocorrelation).mp479.35MB

31 Issues in Linear Regression Analysis and Logistic Regression/379 Logistic Regression (Theory).mp415.05MB

31 Issues in Linear Regression Analysis and Logistic Regression/380 Logistic Regression with statsmodels (Part 1).mp431.14MB

31 Issues in Linear Regression Analysis and Logistic Regression/381 Logistic Regression with statsmodels (Part 2).mp440.77MB