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.
What will i learn?
- Python for Finance
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Why Numpy? Numpy Array VS Python List
00:09:00
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How to Use Numpy
00:14:00
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Basic Operations in Numpy
00:15:00
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Numpy 2D Array
00:08:00
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Numpy Arrays: Boolean Indexing
00:13:00
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Generating Random Numbers
00:18:00
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Performance Issues
00:14:00
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Statistics in Numpy
00:09:00
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Intro to Pandas/Tabular Data
00:12:00
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Inspection of Data
00:13:00
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Built-in Functions, Attributes and Methods
00:11:00
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Where we can get DATA From
00:08:00
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Selecting Columns
00:17:00
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Selecting Rows with Square Brackets (not advisable)
00:09:00
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Selecting Rows with iloc (position-based indexing)
00:20:00
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Pandas Cheat Sheet
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Using pd.read_csv to import csv files
00:15:34
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Using pd.read_csv to import messy csv-files
00:10:00
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Importing Data from Excel with pd.read_excel()
00:05:30
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Importing messy Data from Excel with pd.read_excel()
00:08:00
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Importing Data from the Web with pd.read_html()
00:08:00
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Creating IPL Dataset using pd.read_html()
00:19:00
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First Steps of Data Cleaning - Data Inspection
00:19:00
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Different ways of formatting and Cleaning string Data
00:15:10
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Typecasting - Changing datatypes using astype()
00:10:12
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What are null, NA, NaN values?
00:06:25
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How to detect the null values in large datasets?
00:11:35
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Removing null values without affecting the Dataframe
00:14:30
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How null values can be replaced?
00:07:50
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Intro and detection of Duplicates
00:06:05
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Handling the duplicate data in a Dataframe
00:08:46
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What are outliers? How to find an Outlier?
00:10:43
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Best ways of handling an Outlier?
00:09:04
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Compressing data size using categorical dtype
00:08:10
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Dropping Rows and Columns using Pandas
00:25:26
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Adding new Columns using Square Brackets
00:11:00
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Case study for Arithmetic Operations on NITI Ayog Data
00:35:12
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Best possible ways to create a DataFrame
00:18:11
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Adding new Rows (Custom Data)
00:15:24
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Merging, Joining and Concatenating Dataframes
00:13:32
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Manipulating Elements in a DataFrame
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Introduction to GroupBy Operations
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Splitting with many Keys
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split-apply-combine
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split-apply-combine applied
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Hierarchical Indexing with Groupby
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stack() and unstack()
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Getting started with the Time Series Dataset
00:07:29
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Types of converting str to datetime object
00:09:59
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Analysis and Visualization of Timeseries Data
00:09:17
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Using Indexing and Slicing in Time Series Data
00:11:36
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fixed frequency DatetimeIndex using pd.date_range()
00:22:00
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Using resample() to Downsampling Time Series
00:12:00
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Time Series Using yfinance and resample()
00:07:00
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Getting Data of TESLA Stock by YFinance
00:09:29
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Customising the Stock Data by YFinance
00:14:24
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Stock Split and Dividends by YFinance
00:08:09
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Exporting to CSV/ Excel File by YFinance
00:04:57
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Importing multiples stocks and Financial Indexes Data by YFinance
00:10:56
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Importing Currency Exchange & CryptoCurrency Data by YFinance
00:07:03
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Importing ETFs and MF Data by YFinance
00:08:16
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Stock Fundamentals, Meta Info and Performance Metrics by YFinance
00:12:05
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Financials (Balancesheet, Cashflows, P&L) by YFinance
00:07:55
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Put and Call Options by YFinance
00:04:02
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Stream Real Time data from YFinance
00:08:28
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Getting the Data ready - Initial inspections
00:08:28
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Time Series Normalization
00:13:03
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Using shift, diff and pct_change method
00:13:06
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Measuring Stock Performance with MEAN Returns and STD of Returns
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Financial Time Series – Return and Risk
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Financial Time Series – Covariance and Correlation
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Importing Financial Data from Excel
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Simple Moving Averages (SMA) with rolling()
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Momentum Trading Strategies with SMAs
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S&P 500 Performance Reporting – rolling risk and return
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S&P 500: Investment Horizon and Performance
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Simple Returns vs. Log Returns
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The S&P 500 Return Triangle
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The S&P 500 Dollar Triangle
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The S&P 500 “Weather Radar”
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Exponentially-weighted Moving Averages (EWMA)
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Expanding Windows
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Rolling Correlation
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rollling() with fixed-sized time offsets
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Merging / Aligning Financial Time Series (hands-on)
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Financial Indexes
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Getting the Data
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Price-Weighted Index – Theory
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Creating a Price-Weighted Stock Index with Python
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Equal-Weighted Index
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Market Value-Weighted Index – Theory
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Creating a Market Value-Weighted Stock Index with Python
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Comparison of weighting methods
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Price Index vs. Performance/Total Return Index
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Getting the Data
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Creating many random Portfolios with Python
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What is the Sharpe Ratio and a Risk Free Asset?
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Finding the Optimal Portfolio
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Sharpe Ratio – visualized and explained
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Capital Market Line (CML) & Two-Fund-Theorem
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The Portfolio Diversification Effect
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Systematic vs. unsystematic Risk
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video coming soon
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video coming soon
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Mean-Variance Optimization (MVO)
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It´s not that simple – Part 1 (Investments 101 vs. Real World)
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Changing Expected Returns
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It´s not that simple – Part 2 (Investments 101 vs. Real World)
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Cyclical vs. non-cyclical Stocks – another Intuition on Beta
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Creating Graphs
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Interactive Price Charts with Plotly
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Customizing Plotly Charts
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Interactive Histograms with Plotly
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Candle-Stick and OHLC Charts with Plotly
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SMA and Bollinger Bands with Plotly
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Reviews
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Afreen KhanThe course is very good if you want to understand financial data with the use of pandas. The explanations are very clear. It is very easy to follow the course with kaggle notebook. I enjoyed it very much. -
Manish DixitThis is a helpful guide to analyzing financial data using Python. In the training, the trainer describes in detail how to calculate different metrics (return, risk, correlations, portfolio optimization, etc.), get data, and customize charts. Overall, this training course was excellent.
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Anurag SinghDespite all the information, which is a lot, he manages to deliver it in an excellent manner. He explains everything in great detail and one practically cannot get lost (even with all this information, which is a lot). There is a lot of information included in the course. I highly recommend it to anyone who wants to learn Pandas.
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Deepesh TyagiIt is by far the most comprehensive course on using Python/Pandas with financial data sets. I highly recommend it because everything is clear and concise, without getting bogged down in finance theory.
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