PacktPub | Python for Finance: Investment Fundamentals and Data Analytics [Video] [FCO]
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| 0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url | 409.6 B | ||
| 00001 What_Does_the_Course_Cover.mp4 | 46.5 MB | ||
| 00002 Python.mp4 | 12.9 MB | ||
| 00003 Why_Python.mp4 | 54.7 MB | ||
| 00004 Jupyter.mp4 | 11.8 MB | ||
| 00005 Setting_Up_the_Environment.mp4 | 17.2 MB | ||
| 00006 Programming_Explained_in_5_Minutes.mp4 | 57.7 MB | ||
| 00007 Why_Jupyter.mp4 | 38 MB | ||
| 00008 Installing_Anaconda.mp4 | 47.1 MB | ||
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| 00010 Jupyter_s_Interface_Prerequisites_for_Coding.mp4 | 42.3 MB | ||
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| 00013 Jupyter_-_Restarting_the_Kernel.mp4 | 14.6 MB | ||
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| 00015 Understanding_Numbers_and_Boolean_Values.mp4 | 23.9 MB | ||
| 00016 Strings.mp4 | 43.2 MB | ||
| 00017 The_Arithmetic_Operators_of_Python.mp4 | 27.3 MB | ||
| 00018 What_is_the_Double_Equality_Sign.mp4 | 9.8 MB | ||
| 00019 How_to_Reassign_Values.mp4 | 6.7 MB | ||
| 00020 How_to_Add_Comments.mp4 | 8.8 MB | ||
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| 00023 How_to_Structure_Your_Code_with_Indentation.mp4 | 10.1 MB | ||
| 00024 Python_Comparison_Operators.mp4 | 15 MB | ||
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| 00029 An_Additional_Explanation_of_Boolean_Values.mp4 | 16.2 MB | ||
| 00030 How_to_Define_a_Function_in_Python.mp4 | 11.7 MB | ||
| 00031 How_to_Create_a_Function_with_a_Parameter.mp4 | 32.4 MB | ||
| 00032 Another_Way_to_Define_a_Function.mp4 | 21 MB | ||
| 00033 How_to_Use_a_Function_within_a_Function.mp4 | 12.2 MB | ||
| 00034 Use_Conditional_Statements_and_Functions_Together.mp4 | 23.7 MB | ||
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| 00036 Built-In_Functions_in_Python_Worth_Knowing.mp4 | 31 MB | ||
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| 00041 Python_Dictionaries.mp4 | 33.7 MB | ||
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| 00043 Using_While_Loops_and_Incrementing.mp4 | 23 MB | ||
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| 00046 All_In_Conditional_Statements_Functions_and_Loops.mp4 | 15.2 MB | ||
| 00047 How_to_Iterate_over_Dictionaries.mp4 | 24.9 MB | ||
| 00048 Object-Oriented_Programming.mp4 | 44.1 MB | ||
| 00049 Modules_Packages_and_the_Python_Standard_Library.mp4 | 47.5 MB | ||
| 00050 Importing_Modules.mp4 | 24 MB | ||
| 00051 What_is_Software_Documentation.mp4 | 50.1 MB | ||
| 00052 The_Python_Documentation.mp4 | 83.2 MB | ||
| 00053 Must-Have_Packages_Fin_DSc.mp4 | 54.2 MB | ||
| 00054 Arrays.mp4 | 42.1 MB | ||
| 00055 Generating_Random_Numbers.mp4 | 22.9 MB | ||
| 00056 Using_Financial_Data_in_Python.mp4 | 22.9 MB | ||
| 00057 Importing_Data_Part_I.mp4 | 38.5 MB | ||
| 00058 Importing_Data_Part_II.mp4 | 103.8 MB | ||
| 00059 Importing_Data_Part_III.mp4 | 49.5 MB | ||
| 00060 Changing_the_Index_of_Your_Time-Series_Data.mp4 | 46.5 MB | ||
| 00061 Restarting_the_Jupyter_Kernel.mp4 | 37.3 MB | ||
| 00062 Considering_Both_Risk_and_Return.mp4 | 29.7 MB | ||
| 00063 What_are_We_Going_to_See_Next.mp4 | 23.7 MB | ||
| 00064 Calculating_a_Security_s_Rate_of_Return.mp4 | 44.6 MB | ||
| 00065 Simple_Returns_Part_I.mp4 | 66.5 MB | ||
| 00066 Simple_Returns_Part_II.mp4 | 33 MB | ||
| 00067 Log_Returns.mp4 | 40.4 MB | ||
| 00068 Portfolio_of_Securities_and_Calculating_Rate_of_Return.mp4 | 20.7 MB | ||
| 00069 Calculating_the_Rate_of_Return_of_a_Portfolio_of_Securities.mp4 | 96.2 MB | ||
| 00070 Popular_Stock_Indices.mp4 | 30.6 MB | ||
| 00071 Calculating_the_Return_of_Indices.mp4 | 60.2 MB | ||
| 00072 How_Do_We_Measure_a_Security_s_Risk.mp4 | 54.6 MB | ||
| 00073 Calculating_a_Security_s_Risk_in_Python.mp4 | 66.9 MB | ||
| 00074 The_Benefits_of_Portfolio_Diversification.mp4 | 31.6 MB | ||
| 00075 Calculating_the_Covariance_between_Securities.mp4 | 28.7 MB | ||
| 00076 Measuring_the_Correlation_between_Securities.mp4 | 30.5 MB | ||
| 00077 Calculating_Covariance_and_Correlation.mp4 | 51.5 MB | ||
| 00078 Considering_the_Risk_of_Multiple_Securities.mp4 | 42.5 MB | ||
| 00079 Calculating_Portfolio_Risk.mp4 | 27.5 MB | ||
| 00080 Understanding_Systematic_Versus_Idiosyncratic_Risk.mp4 | 24.7 MB | ||
| 00081 Calculating_Diversifiable_and_NonDiversifiable_Risk.mp4 | 46 MB | ||
| 00082 Simple_Regression_Analysis.mp4 | 30.1 MB | ||
| 00083 Running_a_Regression_in_Python.mp4 | 60.3 MB | ||
| 00084 How_to_Distinguish_Good_Regressions.mp4 | 44.7 MB | ||
| 00085 Computing_Alpha_Beta_and_R2_in_Python.mp4 | 65.1 MB | ||
| 00086 Markowitz_Portfolio_Theory.mp4 | 61.4 MB | ||
| 00087 Obtaining_the_Efficient_Frontier_Part_I.mp4 | 56.8 MB | ||
| 00088 Obtaining_the_Efficient_Frontier_Part_II.mp4 | 72 MB | ||
| 00089 Obtaining_the_Efficient_Frontier_Part_III.mp4 | 25 MB | ||
| 00090 The_Intuition_Behind_the_CAPM.mp4 | 40.8 MB | ||
| 00091 Understanding_and_Calculating_Beta.mp4 | 35.9 MB | ||
| 00092 Calculating_the_Beta_of_a_Stock.mp4 | 40.4 MB | ||
| 00093 The_CAPM_Formula.mp4 | 35.2 MB | ||
| 00094 Calculating_the_Expected_Return_of_a_Stock.mp4 | 23.2 MB | ||
| 00095 Introducing_the_Sharpe_Ratio.mp4 | 19.3 MB | ||
| 00096 Obtaining_the_Sharpe_Ratio_in_Python.mp4 | 12.2 MB | ||
| 00097 Measuring_Alpha.mp4 | 32.7 MB | ||
| 00098 Multivariate_Regression_Analysis.mp4 | 45.8 MB | ||
| 00099 Running_a_Multivariate_Regression_in_Python.mp4 | 93.2 MB | ||
| 00100 The_Essence_of_Monte_Carlo_Simulations.mp4 | 21.7 MB | ||
| 00101 Monte_Carlo_in_Corporate_Finance.mp4 | 23.3 MB | ||
| 00102 MC_Predicting_Gross_Profit_Part_I.mp4 | 76.7 MB | ||
| 00103 MC_Predicting_Gross_Profit_Part_II.mp4 | 29.4 MB | ||
| 00104 Forecasting_Stock_Prices_with_an_MC_Simulation.mp4 | 30.3 MB | ||
| 00105 MC_Forecasting_Stock_Prices_Part_I.mp4 | 36.5 MB | ||
| 00106 MC_Forecasting_Stock_Prices_Part_II.mp4 | 49.8 MB | ||
| 00107 MC_Forecasting_Stock_Prices_Part_III.mp4 | 44.8 MB | ||
| 00108 An_Introduction_to_Derivative_Contracts.mp4 | 54 MB | ||
| 00109 The_Black_Scholes_Formula.mp4 | 40.3 MB | ||
| 00110 MC_Black_Scholes_Merton_Updated.mp4 | 62.8 MB | ||
| 00111 MC_Euler_Discretization_Part_I.mp4 | 67.7 MB | ||
| 00112 MC_Euler_Discretization_Part_II.mp4 | 19.1 MB | ||
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Description
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By : 365 Careers Ltd.
Updated : August 2021
Course Source : https://www.packtpub.com/product/python-for-finance-investment-fundamentals-and-data-analytics-video/9781789618976
Video Details
ISBN 9781789618976
Course Length 7 hours 9 minutes
About
Explore how Python can be applied in the world of finance and solve portfolio optimization problems.
Description
This course will take you on a journey where you will learn how to code in Python. You will learn how to use Python in a real working environment and explore how Python can be applied in the world of finance to solve portfolio optimization problems.
The first part of the course is ideal for beginners and people who want to brush up on their Python skills. Once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. The finance block of this course will teach you in-demand, real-world skills employers are looking for. This explains topics such as how to work with Python’s conditional statements, functions, sequences, and loops, build investment portfolios, and more.
The code bundle for this video course is available at https://github.com/PacktPublishing/Python-for-Finance-Investment-Fundamentals-and-Data-Analytics
About This Video
• Learn Python programming from scratch
• Learn to conduct real-world financial analysis in Python
• A practical tutorial designed for programming beginners and aspiring data scientists
Author
365 Careers’ courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.
If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start.
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