Why do you need to take this Python for Finance Course? The Finance and Investment Industry is experiencing rapid change due to the rapid advancement of processing power, connectivity, and machine learning tools. Not only this, Finance and Investment are increasingly becoming a data-driven business, shifting from a math/formula-based business.
How can you keep up?
You need to leave Excel behind to manage your Financial Data. Python and Pandas offer the opportunity for deep dives into Machine Learning, as well as becoming more efficient at work when you are dealing with Financial Data.
Learning Pandas from scratch is nearly as easy as learning Excel. Pandas is like Excel for Python. Due to its many features, Pandas appears to be more complicated than other tools. As you practice, you will become familiar with pandas and will be able to use them for managing financial data, for meeting the same goal Jobaaj Learnings introduced Advanced Python for Finance Course.
Significant Highlights of The Course
- Deep Dive in Pandas, Financial Data, and Financial Concepts
- Online video lectures
- Quizzes for Practice
- Taught by qualified Python professional
- Projects at the end of the course
Why learn Pandas?
The workflows you usually carry out with Excel can be more efficiently done with Pandas. There may be dozens of lines of coding running automatically behind the scenes of Pandas, which is a high-level coding library. Pandas operations are typically wiped out in one line of code! However, you must learn and master Pandas in an order that enables you to understand more about what’s going on and realize the pitfalls (Don’ts) and remain compliant (DOs). Python/Panda coding is one of the most in-demand skills in Finance.
The difficulty level can be selected from the Python courses we offer:-
For getting started with Python you can choose our Python for Finance Level – 1 Course, there is no prior knowledge needed. In Python for Finance Level – 2 you’ll be learning to Import Financial Data from Free Web Sources, CSV, and Excel Files. Calculate Returns, Risks, and Correlations of Stocks, Indexes, and Portfolios. Calculate Simple Returns, Log Returns, and Annualized Returns and Risks. Learn how to construct your financial index (price, equal, or value-based). Optimize your stock portfolio Calculate Sharpe Ratio, Systematic Risk, Unsystematic Risk, Beta, and Alpha for stocks, indexes, and portfolios. Understanding Modern Portfolio Theory and Risk Diversification, thus allowing for Capital Asset Pricing Model (CAPM). Insight into Mean-Variance Optimization (MVO) and how it is used in Real World (and why it is not used in many cases). Create customized charting with technical indicators (SMA, Candle Stick, Bollinger Bands, etc.) for the financial performance (e.g. Rolling Statistics, Simple Moving Averages, etc.)
What else do we offer?
- Practical-oriented course
There is a strong practical focus to the course lessons, and students work only with the existing programs to demonstrate the concepts. Every module ends with a Quiz containing practical questions related to the course, and students are encouraged to answer the questions before moving forward.
- Quizzes for practice
A quiz is also included at the end of every section of this course and the module end questions. We have also provided worksheets, assignments, and projects for practice.
- Instructor support for questions
Connect with Instructor via WhatsApp/Mail. Jobaaj understands that students will have questions about the course. It is also necessary for a healthy learning process, so we encourage students to ask their questions in the videos discussion forum. Every question will be answered as soon as possible.
Who can take this Course?
- For Investment & Finance Professionals who are interested in transitioning from Excel to Python to boost their careers and work efficiency.
- Finance Students and Researchers who learned to handle large datasets and reached the limits of Excel.
- Data Scientists who want to improve their data handling/manipulation skills (in particular for statistical data).
- Anyone interested in data science (financial).
- Anybody who is interested in how Financial Performance is measured and how indices (stock) and portfolios are created, analyzed, visualized, and optimized. By using data examples instead of theories and formulas, it is easier to understand the concepts.
Numpy Crash Course
Why Numpy? Numpy Array VS Python List
How to Use Numpy
Basic Operations in Numpy
Numpy 2D Array
Numpy Arrays: Boolean Indexing
Generating Random Numbers
Statistics in Numpy
Introduction to Pandas
Intro to Pandas/Tabular Data
Inspection of Data
Built-in Functions, Attributes and Methods
Where we can get DATA From
Selecting Rows with Square Brackets (not advisable)
Selecting Rows with iloc (position-based indexing)
Pandas Cheat Sheet
Different ways of Importing Data Using Pandas
Using pd.read_csv to import csv files
Using pd.read_csv to import messy csv-files
Importing Data from Excel with pd.read_excel()
Importing messy Data from Excel with pd.read_excel()
Importing Data from the Web with pd.read_html()
Creating IPL Dataset using pd.read_html()
Cleaning Data Using Pandas
First Steps of Data Cleaning - Data Inspection
Different ways of formatting and Cleaning string Data
Typecasting - Changing datatypes using astype()
What are null, NA, NaN values?
How to detect the null values in large datasets?
Removing null values without affecting the Dataframe
How null values can be replaced?
Intro and detection of Duplicates
Handling the duplicate data in a Dataframe
What are outliers? How to find an Outlier?
Best ways of handling an Outlier?
Compressing data size using categorical dtype
Deep Dive in Pandas
Dropping Rows and Columns using Pandas
Adding new Columns using Square Brackets
Case study for Arithmetic Operations on NITI Ayog Data
Best possible ways to create a DataFrame
Adding new Rows (Custom Data)
Merging, Joining and Concatenating Dataframes
Time Series in Pandas
Getting started with the Time Series Dataset
Types of converting str to datetime object
Analysis and Visualization of Timeseries Data
Using Indexing and Slicing in Time Series Data
fixed frequency DatetimeIndex using pd.date_range()
Using resample() to Downsampling Time Series
Time Series Using yfinance and resample()
Importing Financial Data from Yahoo Finance
Getting Data of TESLA Stock by YFinance
Customising the Stock Data by YFinance
Stock Split and Dividends by YFinance
Exporting to CSV/ Excel File by YFinance
Importing multiples stocks and Financial Indexes Data by YFinance
Importing Currency Exchange & CryptoCurrency Data by YFinance
Importing ETFs and MF Data by YFinance
Stock Fundamentals, Meta Info and Performance Metrics by YFinance
Financials (Balancesheet, Cashflows, P&L) by YFinance
Put and Call Options by YFinance
Stream Real Time data from YFinance
Financial Data - Essential Workflows (Risk, Return & Correlation)
Getting the Data ready - Initial inspections
Time Series Normalization
Using shift, diff and pct_change method
Using Mean and STD to measure stock performance
Calculating Risk and Return of 7 Stocks
Calculating Stock Covariance and Correlation
Advanced Techniques (Rolling Statistics & Reporting) on Financial Data
S&P 500 Performance Reporting – rolling risk and return
S&P 500: Investment Horizon and Performance
Simple Returns vs. Log Returns
The S&P 500 Return Triangle
The S&P 500 Dollar Triangle
The S&P 500 “Weather Radar”
Exponentially-weighted Moving Averages (EWMA)
rollling() with fixed-sized time offsets
Merging / Aligning Financial Time Series (hands-on)
Fetching major Indexes Data
Calculating SMA of Nifty 50
Simple Moving Average with Momentum Trading
Creating and Analysing Financial Indexes
Getting the Data
Price-Weighted Index – Theory
Creating a Price-Weighted Stock Index with Python
Market Value-Weighted Index – Theory
Creating a Market Value-Weighted Stock Index with Python
Comparison of weighting methods
Price Index vs. Performance/Total Return Index
Create, Analyze and Optimize Financial Portfolios
Getting the Data
Creating many random Portfolios with Python
What is the Sharpe Ratio and a Risk Free Asset?
Finding the Optimal Portfolio
Sharpe Ratio – visualized and explained
Modern Portfolio Theory & Asset Pricing (CAPM, Beta, Alpha, SLM & Risk divers.)
Capital Market Line (CML) & Two-Fund-Theorem
The Portfolio Diversification Effect
Systematic vs. unsystematic Risk
video coming soon
video coming soon
Forward-looking Mean-Variance Optimization & Asset Allocation
Mean-Variance Optimization (MVO)
It´s not that simple – Part 1 (Investments 101 vs. Real World)
Changing Expected Returns
It´s not that simple – Part 2 (Investments 101 vs. Real World)
Cyclical vs. non-cyclical Stocks – another Intuition on Beta
Interactive Financial Charts with Plotly and Cufflinks
Interactive Price Charts with Plotly
Customizing Plotly Charts
Interactive Histograms with Plotly
Candle-Stick and OHLC Charts with Plotly
SMA and Bollinger Bands with Plotly