A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

Repository spanning resources to prepare for Data Scientist, Product Analytics roles at FAANG companies.

A simple SPA Portfolio template for developer/designers built with React. Use it to showcase your work, testimonials and other information to clients.

Fine tuning a LLaMA 2 model on Finance Alpaca using 4/8 bit quantization, easily feasible on Colab.

About Me

Computing Customer Lifetime value via Machine Learning approach

Predicting buy/sell of Reliance Stock using a Support Vector Machine on top of commonly used technical indicators.

The famous and simple Dual Moving Average crossover strategy implemented in Python, and then backtesting it on 2 years of AAPL stock.

Implementation of Joel Greenblatt's magic formula, which he described in his book - "The Little Book That Still Beats the Market".

A monte Carlo simulation for Options Pricing, using Geometric Brownian Motion in Python.

Fetching financial data for technical & fundamental analysis and algorithmic trading from a variety of python packages and sources.

Deciding the strike price for writing options, based on the Open Interest from the option chain data from NSE(National Stock Exchange)

stanford ner jars

Usage of LabelEncoder on a Pandas Dataframe, and encoding multiple Columns at once.

Computing the confidence intervals using Bootstrapping. It is very useful when we have to compute the confidence interval for any other statistic apart from mean(where CLT can be used) given we don…

Pair trading in Python - Testing for Correlation and Co-integration.

Implementation of Heikin Ashi candlesticks in Python

Identify zones of demand/supply(also called Support/Resistance) for financial assets using Python.

Identifying market regime through Gaussian Mixture Models and Markov Regression

Intraday 1 minute tick Data for Nifty and Bank Nifty for backtesting purposes.

The notebook demonstrates Monte Carle Simulation to optimize weights for portfolio allocation to obtain maximal expected Sharpe Ratio

Best Blogs for Data Science and Machine Learning

Small Snippet of Python Code to convert historical OHLC data for a particular stock to Candlestick charts.

Demo Notebook for my talk "Top Notch Categorical Encoding using Python", presented at PyCon ID 2020

Use of ATSPy, a library to automate time series forecasting, using multiple models and ensembles.