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    Complete 2-in-1 Python for Business and Finance Bootcamp

    BY
    Udemy

    Enrich your mastery of Python and learn full Python Data Science Stack using the real examples from the finance and business realm through this course by Udemy.

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    Mode

    Online

    Fees

    ₹ 599 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    Complete 2-in-1 Python for Business and Finance Bootcamp course is a comprehensive online programme targeted for the Business and Finance Professionals, Python Developers, and Computer Scientists alike to explore the possible leverage of Python in the business field. The curriculum offers exhaustive content that provides the learners with very fundamental knowledge of coding to the more advanced theories.

    Created by Alexander Hagmann, a Data Scientist, Finance Professional, and Entrepreneur, the Complete 2-in-1 Python for Business and Finance Bootcamp online course combines the elements of Data Science and Machine Learning with the traditional financial theories. The online programme is structured in a way that imparts both theoretical and practical knowledge of coding to the participants giving them more insight. The learners will be able to understand the coding through the real examples taken from the marketing, manufacturing, and finance fields. 

    Complete 2-in-1 Python for Business and Finance Bootcamp certification, provided by Udemy, brings about an opportunity for the learner to have mastery in Python and expertise in Business & Finance, Statistics, and Regression. This course could be pursued by anyone who has a laptop or PC with internet access and does not require prior knowledge of Python Coding, Finance, Statistics, and  Data Science and learners can kickstart the course with the introductory content on the fundamentals.  

    The highlights

    • Online course 
    • 30-Day Money-Back Guarantee
    • Downloadable Jupyter Notebooks
    • Downloadable resources
    • Full lifetime access
    • Access on mobile and TV
    • Certificate of completion
    •  English videos with subtitle

    Program offerings

    • 37.5 hours on-demand video
    • 40 articles
    • 65 downloadable resources
    • Full lifetime access
    • Access on mobile and tv
    • Certificate of completion
    • English videos with subtitle
    • Coding exercise
    • Downloadable jupyter notebooks

    Course and certificate fees

    Fees information
    ₹ 599  ₹3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    Who it is for

    Researcher

    What you will learn

    Data science knowledge Statistical skills Knowledge of numpy Knowledge of python Financial knowledge

    At the end of the Complete 2-in-1 Python for Business and Finance Bootcamp online certification, the learners will be able to explore Python coding from the very fundamental concepts in a Business, Finance & Data Science context and learn Statistics, Regression, and the ways of using Numpy and Scipy for numerical, financial and scientific computing. Plus, the participants will obtain the skill set to use stats for Statistics and Hypothesis Testing and other aspects of Python useful for business and finance.

    The syllabus

    Getting Started

    • Tips: How to get the most out of this Course (don't skip!)
    • FAQ / Your Questions answered
    • How to download and install Anaconda for Python coding
    • Jupyter Notebooks - let´s get started
    • How to work with Jupyter Notebooks

    ___Part 1 : Python Basics, Time Value of Money and Capital Budgeting ___

    • Overview & Download of Course Materials for Part 1
    • Coding Projects Part 1 - Overview

    How to use Python as a Calculator for basic Time Value of Money Problems

    • Intro to the Time Value of Money (TVM) Concept (Theory)
    • Calculate Future Value (FV) with Python/ Compounding
    • Calculate Present Values (FV) with Python / Discounting
    • Interest Rates and Returns (Theory)
    • Calculate Interest Rates and Returns with Python
    • Introduction to Variables
    • Excursus: How to add inline comments
    • Variables and Memory (Theory)
    • More on Variables and Memory
    • Variables - Dos, Don´ts, and Conventions
    • The print() Function
    • Coding Exercise 1

    How to use Lists and For Loops for TVM Problems with many Cashflows

    • TVM Problems with many Cashflows
    • Intro to Python Lists
    • Zero-based Indexing and negative Indexing in Python (Theory)
    • Indexing Lists
    • For Loops - Iterating over Lists
    • The range Object - another Iterable
    • Calculate FV and PV for many Cashflows
    • The Net Present Value - NPV (Theory)
    • Calculate an investment Project's NPV
    • Coding Exercise 2

    100% Python: Objects, Data Types, Operators & Functional Programming

    • Data Types in Action
    • The Data Type Hierarchy (Theory)
    • Excursus: Dynamic Typing in Python
    • Built-in Functions
    • Integers
    • Floats
    • How to round Floats (and Integers) with round()
    • More on Lists
    • Lists and Element-wise Operations
    • Slicing Lists
    • Slicing Cheat Sheet
    • Changing Elements in Lists
    • Sorting and Reversing Lists
    • Adding and Removing Elements from/to Lists
    • Mutable vs. immutable Objects (Part 1)
    • Mutable vs. immutable Objects (Part 2)
    • Coding Exercise 3
    • Tuples
    • Dictionaries
    • Intro to Strings
    • String Replacement
    • Booleans
    • Operators (Theory)
    • Comparison, Logical and Membership Operators in Action
    • Coding Exercise 4

    How yo slove for IRR & YTM with While Loops and Conditional Statements

    • Conditional Statements
    • Keywords pass, continue and break
    • Calculate a Project´s Payback Period
    • While Loops
    • The Internal Rate of Return - IRR (Theory)
    • Solving for a Project´s IRR
    • Bonds and the Yield to Maturity - YTM (Theory)
    • Solving for a Bond´s Yield to Maturity (YTM)
    • Coding Exercise 5

    How too create great graphs with Matplotlib - Plotting NPV and IRR

    • Intro
    • Line Plots
    • Scatter Plots
    • Customizing Plots (Part 1)
    • Customizing Plots (Part 2)
    • Plotting NPV & IRR
    • Coding Exercise 6

    The Numpy Pckage: Working with numbers made easy!

    • Modules, Packages, and Libraries - No need to reinvent the Wheel
    • Numpy Arrays
    • Indexing and Slicing Numpy Arrays
    • Vectorized Operations with Numpy Arrays
    • Changing Elements in Numpy Arrays & Mutability
    • View vs. copy - potential Pitfalls when slicing Numpy Arrays
    • Numpy Array Methods and Attributes
    • Numpy Universal Functions
    • Boolean Arrays and Conditional Filtering
    • Advanced Filtering & Bitwise Operators
    • Determining a Project´s Payback Period with np. where()
    • Creating Numpy Arrays from Scratch
    • Coding Exercise 7

    How to solve complex complex TVM and Capital Budgeting problems with Python and Numpy

    • Evaluating Investments with npf.npv() and npf.irr()
    • Evaluating Annuities with npf.fv.() - Funding Phase
    • Evaluating Annuities with npf.fv() - Payout Phase
    • How to solve for annuity payments with npf.pmt()
    • How to solve for the number of periodic payments with npf.nper()
    • How to calculate the required Contract Value with npf.pv()
    • Frequency of compounding and the effective annual interest rate
    • How to evaluate a Retirement Plan A-Z
    • Retirement Plan: Sensitivity Analysis
    • Mortgage Loan Analysis - Debt Sizing
    • Mortgage Loan Analysis - Interest Payments and Amortization Schedule
    • Calculate PV of equal installments with npf.pv() - Valuation of Bonds
    • Capital Budgeting - Mutually exclusive Projects (Part 1)
    • Capital Budgeting - Mutually exclusive Projects (Part 2)
    • Capital Budgeting - Mutually exclusive Projects (Part 3)
    • Coding Exercise 8

    ___Part 2: Statistics and Hypothesis Testing with Python, Numpy and Scipy___

    • Statistics  - Overview, Terms, and Vocabulary
    • Coding Projects Part 2 - Overview
    • A download of Part 2 Course Materials

    How to perform Descriptive Statistics on Populations and Samples

    • Population vs. Sample
    • Visualizing Frequency Distributions with plt.hist()
    • Relative and Cumulative Frequencies with plt.hist()
    • Measures of Central Tendency (Theory)
    • Coding Measures of Central Tendency - Mean and Median
    • Coding Measures of Central Tendency - Geometric Mean
    • Excursus: Why Log Returns are useful
    • Variability around the Central Tendency / Dispersion (Theory)
    • Minimum, Maximum, and Range with Python/Numpy
    • Variance and Standard Deviation with Python/Numpy
    • Percentiles with Python/Numpy
    • Skew and Kurtosis (Theory)
    • How to calculate Skew and Kurtosis with scipy.stats
    • Coding Exercise 1

    Common probablity Distributions and how to constuct Confidence Intervals

    • How to generate Random Numbers with Numpy
    • Reproducibility with np. random.seed()
    • Probability Distributions - Overview
    • Discrete Uniform Distributions
    • Continuous Uniform Distributions
    • The Normal Distribution (Theory)
    • Creating a normally Distribution Random Variable
    • Normal Distribution - Probability Density Function (pdf) with scipy.stats
    • Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats
    • The Standard Normal Distribution and Z-Values
    • Properties of the Standard Normal Distribution (Theory)
    • Probabilities and Z-Values with scipy.stats
    • Confidence Intervals with scipy.stats
    • Coding Exercise 2

    How to estimate Population parameters with samples - Sampling and Estimation

    • Sample Statistic, Sampling Error, and Sampling Distribution (Theory)
    • Sampling with np.random.choice().
    • Sampling Distribution
    • Standard Error
    • Central Limit Theorem (Coding Part 1)
    • Central Limit Theorem (Coding Part 2)
    • Central Limit Theorem (Theory)
    • Point Estimates vs. Confidence Interval Estimates (known Population Variance)
    • The Student´s t-distribution: What is it and why/when do we use it?
    • Unknown Population Variance - the Standard Case (Example 1)
    • Unknown Population Variance - the Standard Case (Example 2)
    • Student´s t-Distribution vs. Normal Distribution with scipy.stats
    • Bootstrapping with Python: an alternative method without Statistics
    • Coding Exercise 3

    How to perfoem Hypothesis Test: Z-Test, t-Tests, Bootstrapping & more

    • Hypothesis Testing (Theory)
    • Two-tailed Z-Test with known Population Variance
    • What is the p-value? ( Theory)
    • Calculating and interpreting z-statistic and p-value with scipy.stats
    • One-tailed Z-Test with known Population Variance
    • Two-tailed t-Test (unknown Population Variance)
    • One-tailed t-Test (unknown Population Variance)
    • Hypothesis Testing with Bootstrapping
    • Testing for Normality of Financial Returns with scipy.stats
    • Coding Exercise 4

    ___Part 3: Advance Python, Monte Carlo Simulations and Value at Risk (VAR)___

    • *Update Notice (June 2021)*
    • Overview & Download of Course Materials for Part 3
    • Coding Projects Part 3 - Overview

    n-dimensional Numpy Arrays / How to work with numerical Tabular Data

    • How to work with nested Lists
    • 2-dimensional Numpy Arrays
    • How to slice 2-dim Numpy Arrays (Part 1)
    • How to slice 2-dim Numpy Arrays (Part 2)
    • Recap: Changing Elements in a Numpy Array/slice
    • How to perform row-wise and column-wise Operations
    • Reshaping and Transposing 2-dim Numpy Arrays
    • Creating 2-dim Numpy Arrays from Scratch
    • Arithmetic & Vectorized Operations with 2-dim Numpy Arrays
    • The keepdims parameter
    • Adding & Removing Elements
    • Merging and Concatenating Numpy Arrays
    • Coding Exercise 1

    How to creat your own user-defined Functions

    • Defining your first user-defined Function
    • What´s the difference between Positional Arguments vs. Keyword Arguments?
    • How to work with Default Arguments
    • The Default Argument None
    • How to unpack Iterables
    • Sequences as arguments and *args
    • 05:05
    • How to return many results
    • Scope - easily explained
    • How to create Nested Functions
    • Putting it all together - Case Study
    • Coding Exercise 2

    Monte Carlo Simulations and value-at-Risk (VAR) with Python and Numpy

    • What is the Value-at-Risk (VaR)? (Theory)
    • Analyzing the Data/past Performance
    • How to use the Parametric Method to calculate Value-at-Risk (VaR)
    • How to use the Historical Method to calculate Value-at-Risk (VaR)
    • Monte Carlo Simulations for Value-at-Risk - Parametric (Part 1)
    • Monte Carlo Simulations for Value-at-Risk - Parametric (Part 2)
    • Monte Carlo Simulations for Value-at-Risk - Parametric (Part 3)
    • Monte Carlo Simulations for Value-at-Risk - Bootstrapping (Part 1)
    • Monte Carlo Simulations for Value-at-Risk - Bootstrapping (Part 2)
    • Conditional Value-at-Risk (CVaR)
    • Dynamic & path-dependent Simulations (Part 1)
    • Dynamic & path-dependent Simulations (Part 2)
    • Dynamic & path-dependent Simulations (Part 3)
    • Dynamic & path-dependent Simulations (Part 4)
    • Coding Exercise 3

    ___Part 4: Managing (Financial) data with Pandas: Beyond Excel___

    • Introduction
    • The download of Part 4 Course Materials
    • Tabular Data and Pandas DataFrames

    Pandas Basic - Starting from Zero

    • First Steps (Inspection of Data, Part 1)
    • First Steps (Inspection of Data, Part 2)
    • Built-in Functions, Attributes, and Methods
    • Explore your own Dataset: Coding Exercise 1 (Intro)
    • Explore your own Dataset: Coding Exercise 1 (Solution)
    • Selecting Columns
    • Selecting Rows with Square Brackets (not advisable)
    • Selecting Rows with iloc (position-based indexing)
    • Slicing Rows and Columns with iloc (position-based indexing)
    • Position-based Indexing Cheat Sheets
    • Selecting Rows with loc (label-based indexing)
    • Slicing Rows and Columns with loc (label-based indexing)
    • Label-based Indexing Cheat Sheets
    • Summary and Outlook
    • Coding Exercise 2 (Intro)
    • Coding Exercise 2 (Solution)

    Pandas Intermediate

    • Intro
    • First Steps with Pandas Series
    • Analyzing Numerical Series with unique(), nunique() and value_counts()
    • UPDATE Pandas Version 0.24.0 (Jan 2019)
    • EXCURSUS: Updating Pandas / Anaconda
    • Analyzing non-numerical Series with unique(), nunique(), value_counts()
    • The copy() method
    • Sorting of Series and Introduction to the inplace - parameter
    • Coding Exercise 3 (Intro)
    • Coding Exercise 3 (Solution)
    • First Steps with Pandas Index Objects
    • Changing Row Index with set_index() and reset_index()
    • Changing Column Labels
    • Renaming Index & Column Labels with rename()
    • Coding Exercise 4 (Intro)
    • Coding Exercise 4 (Solution)
    • Sorting DataFrames with sort_index() and sort_values()
    • nunique() and nlargest() / nsmallest() with DataFrames
    • Filtering DataFrames (one Condition)
    • Filtering DataFrames by many Conditions (AND)
    • Filtering DataFrames by many Conditions (OR)
    • Advanced Filtering with between(), isin() and ~
    • any() and all()
    • Coding Exercise 5 (Intro)
    • Coding Exercise 5 (Solution)
    • Intro to NA Values / missing Values
    • Handling NA Values / missing Values
    • Exporting DataFrames to csv
    • Summary Statistics and Accumulations
    • The agg() method
    • Coding Exercise 6 (Intro)
    • Coding Exercise 6 (Solution)

    Data Visualization with Pandas, Matplotlib and Seaborn

    • Intro
    • Visualization with Matplotlib (Intro)
    • Customization of Plots
    • Histograms (Part 1)
    • Histograms (Part 2)
    • Scatterplots
    • First Steps with Seaborn
    • Categorical Seaborn Plots
    • Seaborn Regression Plots
    • Seaborn Heatmaps
    • Coding Exercise 7 (Intro)
    • Coding Exercise 7 (Solution)

    Pandas Advanced

    • Intro
    • Removing Columns
    • Removing Rows
    • Adding new Columns to a DataFrame
    • Arithmetic Operations (Part 1)
    • Arithmetic Operations (Part 2)
    • Creating DataFrames from Scratch with pd.DataFrame()
    • Adding new Rows (Hands-on)
    • Adding new Rows to a DataFrame
    • Manipulating Elements in a DataFrame
    • Coding Exercise 8 (Intro)
    • Coding Exercise 8 (Solution)
    • Introduction to GroupBy Operations
    • Understanding the GroupBy Object
    • Splitting with many Keys
    • split-apply-combine
    • split-apply-combine applied
    • Hierarchical Indexing with Groupby
    • stack() and unstack()
    • Coding Exercise 9 (Intro)
    • Coding Exercise 9 (Solution)

    Managing Time Series and Financial Data with Pandas

    • Importing Time Series Data from csv-files
    • Converting strings to DateTime objects with pd.to_datetime()
    • Initial Analysis / Visualization of Time Series
    • Indexing and Slicing Time Series
    • Creating a customized DatetimeIndex with pd.date_range()
    • More on pd.date_range()
    • Coding Exercise 10 (intro)
    • Coding Exercise 10 (Solution)
    • Downsampling Time Series with resample() (Part 1)
    • Downsampling Time Series with resample (Part 2)
    • The PeriodIndex object
    • Advanced Indexing with reindex()
    • Coding Exercise 11 (intro)
    • Coding Exercise 11 (Solution)
    • Getting Ready (Installing required library)
    • Importing Stock Price Data from Yahoo Finance (it still works!)
    • Initial Inspection and Visualization
    • Normalizing Time Series to a Base Value (100)
    • The shift() method
    • The methods diff() and pct_change()
    • Measuring Stock Performance with MEAN Returns and STD of Returns
    • Financial Time Series - Return and Risk
    • Financial Time Series - Covariance and Correlation
    • Importing Financial Data from Excel
    • Merging / Aligning Financial Time Series (hands-on)
    • Coding Exercise 12 (intro)
    • Coding Exercise 12 (Solution)

    Creating, analyzing and optimizing Financial Portfolios with Python

    • Intro
    • Getting the Data
    • Creating the equally-weighted Portfolio
    • Creating many random Portfolios with Python
    • What is the Sharpe Ratio and a Risk Free Asset?
    • Portfolio Analysis and the Sharpe Ratio with Python
    • Finding the Optimal Portfolio
    • Excursus: Portfolio Optimization with scipy
    • Sharpe Ratio - visualized and explained
    • Coding Exercise 13 (Intro)
    • Coding Exercise 13 (Solution)
    • Intro CAPM
    • Capital Market Line (CML) & Two-Fund-Theorem
    • The Portfolio Diversification Effect
    • Systematic vs. unsystematic Risk
    • Capital Asset Pricing Model (CAPM) & Security Market Line (SLM)
    • Beta and Alpha
    • Redefining the Market Portfolio
    • Cyclical vs. non-cyclical Stocks - another Intuition on Beta
    • Coding Exercise 14 (Intro)
    • Coding Exercise 14 (Solution)

    ___Part 5: Regression Analysis (A Must-Have for Machine Learning)___

    • Introduction to Regression Analysis
    • Coding Projects Part 5 - Overview
    • The download of Part 5 Course Materials

    Correlation and Regression

    • Cleaning and preparing the Data - Movies Database (Part 1)
    • Cleaning and preparing the Data - Movies Database (Part 2)
    • Covariance and Correlation Coefficient (Theory)
    • How to calculate Covariance and Correlation in Python
    • Correlation and Scatterplots – visual Interpretation
    • Creating a Confidence Interval for the Correlation Coefficient (Bootstrapping)
    • Testing for Correlation (t-Test)
    • What is Linear Regression? (Theory)
    • A simple Linear Regression Model with numpy & Scipy
    • How to interpret Intercept and Slope Coefficient
    • Case Study (Part 1): The Market Model (Single Factor Model)
    • Case Study (Part 2): The Market Model (Single Factor Model)
    • Coding Exercise 1

    OLS Regressio, ANOVA and Hypothesis Testing

    • OLS (Ordinary Least Squares) Regression (Theory)
    • OLS Regression with statsmodels - Intro
    • OLS Regression - ANOVA (Theory)
    • OLS Regression with Statsmodels - ANOVA
    • Coefficient of Determination (R squared)
    • OLS Regression with statsmodels and DataFrames
    • Confidence Intervals for Regression Coefficients - Bootstrapping
    • Hypothesis Testing of Regression Coefficients (Theory)
    • Hypothesis Testing of Regression Coefficients with statsmodels
    • Regression Analysis with statsmodels - the Summary Table
    • Case Study (Part 3): The Market Model (Single Factor Model)
    • Coding Exercise 2

    Multiple Regression Models

    • Multiple Regression (Theory)
    • Movies Dataset - Preparing the Data
    • Multiple Regression Analysis with statsmodels
    • Coefficient of Determination (Adjusted R squared)
    • Regression Coefficients, Hypothesis Testing & Model Specification
    • How to test the Significance of the Model as a whole (F-Test)
    • Creating and working with Dummy Variables (Part 1)
    • Creating and working with Dummy Variables (Part 2)
    • Coding Exercise 3

    Case Study: Multi-Factor Models (Fama-French)

    • Fama-French: An Introduction
    • Single-Factor Models with the Fama-French Market Portfolio (Part 1)
    • Single-Factor Models with the Fama-French Market Portfolio (Part 2)
    • The Factors Size & Value
    • How to create a Fama-French Three-Factor Model
    • The Factors Profitability and Investment
    • How to create a Fama-French Five-Factor Model
    • Coding Exercise 4

    Issues in Linear Regression Analysis and Logistic Regression

    • Linear Regression - not that easy!
    • Detecting and Handling Outliers (Part 1)
    • Detecting and Handling Outliers (Part 2)
    • Non-Linear Relationships - Feature Transformation
    • Detecting and Handling Multicollinearity
    • Detecting and Correcting Heteroskedasticity
    • Detecting and Handling Serial Correlation (Autocorrelation)
    • Logistic Regression (Theory)
    • Logistic Regression with statsmodels (Part 1)
    • Logistic Regression with statsmodels (Part 2)

    Bonus: Introduction to Object Oriented Programming (OOP)

    • Downloads for this Section
    • Introduction to OOP and examples for Classes
    • The FinancialInstrument Class live in action (Part 1)
    • The FinancialInstrument Class live in action (Part 2)
    • The special method __init__()
    • The method get_data()
    • The method log_returns()
    • String representation and the special method __repr__()
    • The methods plot_prices() and plot_returns()
    • Encapsulation and protected Attributes
    • The method set_ticker()
    • Adding more methods and performance metrics
    • Inheritance
    • Inheritance and the super() Function
    • Adding meaningful Docstrings
    • Creating and Importing Python Modules (.py)
    • Coding Exercise: Create your own Class

    What's Next ?

    • Get your special BONUS here!

    Instructors

    Mr Alexander Hagmann

    Mr Alexander Hagmann
    Data Scientist
    Udemy

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