Python Analytics Automation Quant. Python and NoSQL (MongolDB) for Quant Finance
We currently are using NoSQL (MongolDB) but considering to use Spark also.
This course help in Mastering Python and MongoDB for computational finance and financial data science.
Most of the times as an analyst you need to pull big unstructured and do some computation on Data.
You have a choice of using R and Python after you have pulled data.
Playing with missing data is the most important things and for that I will show five important commands.
The more your reduce data before pulling the easier it would be do the computation.
Charts and pictures in python are sometimes not so intuitive.
Utility functions for data cleaning, charting, looping, error handling will be explained.
Duration 5 hours
Introduction to SQL (revisiting SQL commands used in Financial Data) and comparing commands like groupby and left join.
Joins become very important.
Python and R – Python is considered faster than R while doing the big data aggregation using map reduce is even better in mongo DB.
Big data tools do faster aggregation than anything outside.
NoSQL concepts and MongoDB
Aggregate, pipeline and other commands.
Pipleline and Map Reduce are used to aggregate the data over bringing the data into python.
Using Libraries in Python such as SciPy, Numpy, graphs and pandas.
Panda Joins and problems with joins
Getting deeper into the data
Project of pulling large amounts of data and processing it
Topics covered include: advanced concepts and approaches with NumPy and Pandas, time series management with Pandas as well as basic and
advanced operations, performant IO operations with Numpy and Pandas as well as basic and advanced visualization techniques.
Python for Finance (O’Reilly 2014)
Derivatives Analytics with Python (Wiley 2015)
Python for Finance: Analyze Big Financial Data