Python for Excel Use xlwings for Data Science and Finance
What you’ll learn
Automate Excel with clean and powerful Python Code
Learn and master the xlwings library from 0 to 100
Use Excel as Graphical User Interface (GUI) and run your Python code with Excel
Create powerful Dashboard Apps with Excel (frontend) and Python (backend)
Use powerful Data Visualization Tools (Matplotlib, Seaborn) in Excel
Learn Python from scratch with a taylor-made Crash Course (For Python beginners)
Write UDFs (user defined functions) and use Numpy, Pandas and Machine Learning Libraries directly in Excel
Write Excel tools with Python instead of VBA and call your code directly from within Excel
Use xlwings to automate Excel reports with Python
Prototype Web apps
Write and use Dynamic Arrays with xlwings
Run your financial model 10,000 times & more with a Python Monte Carlo Simulation
Load (financial) data from Web APIs directly into Excel
Run Python Scripts from within Excel with Run main and RunPython
Replace VBA macros with clean and powerful Python code
Requirements
A desktop computer (Windows or Mac) capable of storing and running Python/Anaconda. The course will walk you through installing the necessary free software.
An internet connection capable of streaming HD Videos.
A working installation of Microsoft Excel.
Mac users are welcome. Please note that 10%-15% of the course content (UDFs) work on Windows only!
Willingness to code and work with Python.
Description
Excel vs. Python – what is the best tool for Data Science, Business and Finance?The answer is: Use Excel and Python together and integrate both tools with xlwings. Get the best of two worlds!With xlwings, you can use Python Data Science libraries like Numpy, Pandas, Scipy, Matplotlib, Seaborn and Scikit-learn directly in Excel! You can run Python code in Excel and boost your Excel projects! More and more Professionals and Developers useExcel as Frontend Python as analytical Backend. This course is the perfect choice for Experienced Python Coders: Use Excel as Graphical User Interphase (GUI) | Run your Python scripts with Excel | Present your results with Excel Dashboards Excel Users and complete Python Beginners: Boost your Excel projects with clean and powerful Python code! Mixed Groups: Non-Coders can run and use Python code simply by clicking on buttons in Excel. Why take this course?You will learn and master the xlwings library from scratchFor Excel Users and complete Python Beginners: This course includes a Python Crash Course that is tailor-made for you!It´s the most comprehensive and practical (hands-on) xlwings course on the webIt covers three comprehensive real-world projects.Project 1: You will learn how to boost your financial model in Excel by adding a Python Monte Carlo Simulation – Run your Excel calculation 10,000 times with different sets of inputs and analyze the results!Project 2: You will learn how to create Bloomberg-like Stock Dashboard Apps with Excel (Graphical User Interface) and Python (analytical Backend).Project 3: You will learn how to use Pandas methods and functions on your datasets directly in Excel.Why use Excel?There is no better Graphical User Interface (GUI) and Reporting tool than Excel. Excel is widely spread (750 million users) standardizedintuitive to usemost users are well-trained it requires low/zero set upit requires low/zero maintenance and it´s still the best choice for financial models & spreadsheet calculationsWhy use Python?With hundreds of powerful Libraries, Python is the first choice for Data Science, Machine Learning and advanced analytics in Business and Finance. The Python Ecosystem is way more powerful and versatile than VBA. And it´s cleaner and easier to learn and apply!Why learn and master xlwings?xlwings is the perfect tool to integrate Excel and Python! xlwings allows you toAutomate Excel from Python e.g. to produce reports or to interact with Jupyter Notebooks.Write macros in Python that you can run from buttons in Excel, e.g. to load in data from a database or an external API.Write UDFs (user-defined functions) and leverage the power from NumPy, Pandas and machine learning libraries.Leverage Python’s scientific stack for interactive data analysis using Jupyter Notebooks, NumPy, Pandas, scikit-learn, etc.Use xlwings to automate Excel reports with Python.Write Excel tools with Python instead of VBA and call your code directly from within Excel, e.g. via a button on the sheet.This also works great for prototyping web apps.Write (array) UDFs in a breeze by taking advantage of all the functionality already available in libraries like NumPy and Pandas.Dynamic array formulas are supported.As always, there is no risk for you as I offer a 30-Days-Money-Back-Guarantee. Looking forward to seeing you in the course!(Mac users are welcome! However, please note that 10%-15% of the course content (UDFs) is currently not available on Mac!)
Overview
Section 1: Getting Started
Lecture 1 Introduction (don´t skip!)
Lecture 2 Course Overview (don´t skip!)
Lecture 3 Tips: How to get the most out of this Course (don´t skip!)
Lecture 4 FAQ / Your Questions answered
Lecture 5 How to download and install Anaconda for Python coding
Lecture 6 Jupyter Notebooks – let´s get started
Lecture 7 How to work with Jupyter Notebooks
Section 2: First Steps with xlwings (Reading and Writing Elements)
Lecture 8 Introduction and Downloads
Lecture 9 How to install xlwings
Lecture 10 How to use xlwings as a Data Viewer
Lecture 11 Data Viewer – Update
Lecture 12 How to connect to an Excel Workbook
Lecture 13 How to read and write single Values
Lecture 14 How to assign a name
Lecture 15 How to write Excel Functions with Python
Lecture 16 Range Shortcuts
Lecture 17 Case Study – Bringing it all together
Lecture 18 Homework
Section 3: Reading and writing many Values
Lecture 19 Section Downloads
Lecture 20 One-dimensional Data Structures
Lecture 21 How to write Values vertically
Lecture 22 Rows and Columns (1dim vs. 2dim)
Lecture 23 How to read two-dimensional Data Structures
Lecture 24 Advanced Reading with expand
Lecture 25 How to write two-dimensional Data Structures
Lecture 26 Range Indexing and Slicing
Lecture 27 Efficiency
Lecture 28 Homework
Section 4: Project 1: Monte Carlo Simulations in Excel with Python (Part 1)
Lecture 29 Introduction
Lecture 30 Section Downloads
Lecture 31 The Excel Model explained (Part 1)
Lecture 32 The Excel Model explained (Part 2)
Lecture 33 Running a simple Monte Carlo Simulation
Lecture 34 A more advanced and realistic Monte Carlo Simulation
Lecture 35 Final Considerations
Section 5: Running Python Scripts in Excel – RunPython
Lecture 36 Introduction and Downloads
Lecture 37 Installing the xlwings add-in and other preparations
Lecture 38 Running Python Scripts with “Run main”
Lecture 39 Troubleshooting (Part 1)
Lecture 40 All you need to know about VBA Macros
Lecture 41 Running Python Scripts with “RunPython”
Lecture 42 Troubleshooting (Part 2)
Lecture 43 Run main vs RunPython
Lecture 44 Excursus: Converting Jupyter Notebooks to .py
Lecture 45 Homework
Section 6: Project 1: Monte Carlo Simulations in Excel with Python (Part 2)
Lecture 46 Introduction and Downloads
Lecture 47 Monte Carlo Simulation with RunPython (Part 1)
Lecture 48 Monte Carlo Simulation with RunPython (Part 2)
Section 7: Using Matplotlib and Seaborn in Excel with xlwings
Lecture 49 Introduction and Downloads
Lecture 50 How to write a Matplotlib Plot into Excel
Lecture 51 How to update the Plot
Lecture 52 How to change Size and Position (Part 1)
Lecture 53 How to change Size and Position (Part 2)
Lecture 54 How write a Seaborn Plot into Excel
Lecture 55 How to create Excel Charts with Python
Lecture 56 Homework: Adding a Plot to the Monte Carlo Simulation (Project 1)
Section 8: Project 2: Build Dashboard Apps with Excel (GUI) and Python (analytical backend)
Lecture 57 Introduction and Downloads
Lecture 58 IMPORTANT NOTICE (Update January 23)
Lecture 59 Stock Performance Analysis during COVID-19 with Python & Pandas (Part 1)
Lecture 60 Stock Performance Analysis during COVID-19 with Python & Pandas (Part 2)
Lecture 61 Stock Performance Analysis during COVID-19 with Python & Pandas (Part 3)
Lecture 62 Building a Stock Performance Dashboard App (Part 1)
Lecture 63 Building a Stock Performance Dashboard App (Part 2)
Lecture 64 Improving the Source Code and Errors
Section 9: Reading and Writing Data Structures (Numpy, Pandas) & Converters
Lecture 65 Section Downloads
Lecture 66 (Default) Converters
Lecture 67 The Numpy Converter
Lecture 68 The Dictionary Converter
Lecture 69 The DataFrame Converter (Part 1)
Lecture 70 The DataFrame Converter (Part 2)
Lecture 71 Data Science Application: Inspecting and Manipulating DataFrames in Excel
Lecture 72 The Pandas Series Converter
Lecture 73 Excursus: How to load Data from Excel into Pandas with pd.read_excel()
Lecture 74 Excursus: Advanced import with pd.read_excel()
Lecture 75 Excursus: How to load Financial Data / Time Series with pd.read_excel()
Section 10: User-defined Functions (UDF) and Dynamic Arrays with xlwings (Windows only)
Lecture 76 Introduction and Downloads
Lecture 77 Preparations and your first UDF
Lecture 78 How to change the Name and Location of the Python Module
Lecture 79 Troubleshooting (UDF)
Lecture 80 UDFs – Behind the Scenes
Lecture 81 More complex UDFs and the @xw.arg Decorator
Lecture 82 How to create Numpy UDFs
Lecture 83 UDFs and Array Formulas
Lecture 84 How to create Dynamic Arrays with xlwings UDFs
Lecture 85 How to create Pandas UDFs
Lecture 86 How to add Docstrings
Lecture 87 Homework
Section 11: Project 3: Use Pandas UDFs in Excel for Data Science and Finance (Windows only)
Lecture 88 Introduction and Downloads
Lecture 89 IMPORTANT NOTICE (Update January 23)
Lecture 90 How to load Financial Data from the Web into Excel with the DataReader UDF
Lecture 91 How to resample Time Series in Excel with the resample UDF
Lecture 92 How to calculate Financial Returns with a Pandas/Numpy UDF
Lecture 93 How to get Summary Statistics of a Dataset with the describe UDF
Lecture 94 How to create a Dataset´s Correlation Matrix with the corr UDF
Lecture 95 Taken all together – the Super UDF
Lecture 96 How to perform inner/outer/left/right joins with the merge UDF
Section 12: APPENDIX 1: Python Crash Course for Excel Users
Lecture 97 Introduction and Overview
Lecture 98 Section Downloads
Lecture 99 Intro to the Time Value of Money (TVM) Concept (Theory)
Lecture 100 Calculate Future Values (FV) with Python / Compounding
Lecture 101 Calculate Present Values (FV) with Python / Discounting
Lecture 102 Interest Rates and Returns (Theory)
Lecture 103 Calculate Interest Rates and Returns with Python
Lecture 104 Introduction to Variables
Lecture 105 Excursus: How to add inline comments
Lecture 106 Variables and Memory (Theory)
Lecture 107 More on Variables and Memory
Lecture 108 Variables – Dos, Don´ts and Conventions
Lecture 109 The print() Function
Lecture 110 Coding Exercise 1
Lecture 111 TVM Problems with many Cashflows
Lecture 112 Intro to Python Lists
Lecture 113 Zero-based Indexing and negative Indexing in Python (Theory)
Lecture 114 Indexing Lists
Lecture 115 For Loops – Iterating over Lists
Lecture 116 The range Object – another Iterable
Lecture 117 Calculate FV and PV for many Cashflows
Lecture 118 The Net Present Value – NPV (Theory)
Lecture 119 Calculate an Investment Project´s NPV
Lecture 120 Coding Exercise 2
Lecture 121 Data Types in Action
Lecture 122 The Data Type Hierarchy (Theory)
Lecture 123 Excursus: Dynamic Typing in Python
Lecture 124 Build-in Functions
Lecture 125 Integers
Lecture 126 Floats
Lecture 127 How to round Floats (and Integers) with round()
Lecture 128 More on Lists
Lecture 129 Lists and Element-wise Operations
Lecture 130 Slicing Lists
Lecture 131 Slicing Cheat Sheet
Lecture 132 Changing Elements in Lists
Lecture 133 Sorting and Reversing Lists
Lecture 134 Adding and removing Elements from/to Lists
Lecture 135 Mutable vs. immutable Objects (Part 1)
Lecture 136 Mutable vs. immutable Objects (Part 2)
Lecture 137 Coding Exercise 3
Lecture 138 Tuples
Lecture 139 Dictionaries
Lecture 140 Intro to Strings
Lecture 141 String Replacement
Lecture 142 Booleans
Lecture 143 Operators (Theory)
Lecture 144 Comparison, Logical and Membership Operators in Action
Lecture 145 Coding Exercise 4
Lecture 146 Conditional Statements
Lecture 147 Keywords pass, continue and break
Lecture 148 Calculate a Project´s Payback Period
Lecture 149 Defining your first user-defined Function
Lecture 150 What´s the difference between Positional Arguments vs. Keyword Arguments?
Lecture 151 How to work with Default Arguments
Lecture 152 Coding Exercise 5
Section 13: APPENDIX 2: Matplotlib, Numpy, Pandas and Seaborn Crash Course
Lecture 153 Downloads for this Section
Lecture 154 Matplotlib Introduction
Lecture 155 Line Plots
Lecture 156 Scatter Plots
Lecture 157 Customizing Plots (Part 1)
Lecture 158 Customizing Plots (Part 2)
Lecture 159 Coding Exercise 6
Lecture 160 Modules, Packages and Libraries – No need to reinvent the Wheel
Lecture 161 Numpy Arrays
Lecture 162 Indexing and Slicing Numpy Arrays
Lecture 163 Vectorized Operations with Numpy Arrays
Lecture 164 Changing Elements in Numpy Arrays & Mutability
Lecture 165 View vs. copy – potential Pitfalls when slicing Numpy Arrays
Lecture 166 Numpy Array Methods and Attributes
Lecture 167 Numpy Universal Functions
Lecture 168 Boolean Arrays and Conditional Filtering
Lecture 169 Coding Exercise 7
Lecture 170 How to work with nested Lists
Lecture 171 2-dimensional Numpy Arrays
Lecture 172 How to slice 2-dim Numpy Arrays (Part 1)
Lecture 173 How to slice 2-dim Numpy Arrays (Part 2)
Lecture 174 Recap: Changing Elements in a Numpy Array / slice
Lecture 175 How to perform row-wise and column-wise Operations
Lecture 176 Coding Exercise 8
Lecture 177 Intro to Tabular Data / Pandas
Lecture 178 Create your very first Pandas DataFrame (from csv)
Lecture 179 Pandas Display Options and the methods head() & tail()
Lecture 180 First Data Inspection
Lecture 181 Coding Exercise 9
Lecture 182 Selecting Columns
Lecture 183 Selecting one Column with the “dot notation”
Lecture 184 Zero-based Indexing and Negative Indexing
Lecture 185 Selecting Rows with iloc (position-based indexing)
Lecture 186 Slicing Rows and Columns with iloc (position-based indexing)
Lecture 187 Position-based Indexing Cheat Sheets
Lecture 188 Selecting Rows with loc (label-based indexing)
Lecture 189 Slicing Rows and Columns with loc (label-based indexing)
Lecture 190 Label-based Indexing Cheat Sheets
Lecture 191 Summary, Best Practices and Outlook
Lecture 192 Coding Exercise 10
Lecture 193 First Steps with Pandas Series
Lecture 194 First Steps with Pandas Index Objects
Lecture 195 Importing Time Series Data from csv-files
Lecture 196 Initial Analysis / Visualization of Time Series
Lecture 197 Seaborn Introduction
Section 14: What´s next? (outlook and additional resources)
Lecture 198 Bonus Lecture
Data Scientist and Finance Professionals seeking to use Excel as Frontend and Python as analytical Backend in their Projects.,Excel professionals seeking to write Excel tools with clean Python code instead of VBA/Marcos.,Python Beginners are welcome as the course includes a Python Crash Course designed for Excel Professionals.,Python Developers seeking to work with Excel as GUI (Graphical User Interface).
Course Information:
Udemy | English | 16h 15m | 5.52 GB
Created by: Alexander Hagmann
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