SQL Cohort retention I wrote the SQL cohort analysis to find who’s about to leave. Main Goals of the day: Write SQL to calculate intra-week retention (Day 0 to Day 6) Focus on first deposit...

Key Performance Indicators After simulating the “sessions”, it’s time to simulate money movements; so I added the key factors of the iGaming sector: RTP (Return to Player) - How much mo...

More volatility, more risk I changed the session distribution for more realism. I selected the binomial distribution for the simulation at first, then I changed it to Poisson. For a simple and s...

Enhancing sessions and players Today I realised I’m simulating a “small sample”: I modified it. From 500 sessions → 10,000 sessions. From not identified players → 1,200 unique players. I need re...

Improving elements: devices, games, bonuses, etc. I added new rows for the data_generator.py script: adding devices, bonuses, probabilities to win, and lose. Created the .csv dataset of simulated ...

Intro Today I started my iGaming project: a useful repository to analyse data on customers on the iGaming sector… …Without even working there!! Main Goals of the day: Writing the structure of...

Introducing Analysis Dashboard On Day 8, I started structuring the foundation for my Market Analysis Dashboard. The focus was on creating reusable functions to download and clean financial time se...

Data Visualization On Day 7, I wrapped up Week 1: Python Basics by exploring one of the most important tools for financial analysis: data visualization. Plotting time series helps us understand as...

Handling values in a Dataset Today I focused on cleaning financial time series data and exploring basic descriptive statistics with pandas. Handling missing values and computing summary stats are ...

Rolling Statistics: Moving Average This day I explored rolling statistics in Pandas, applied to financial time series; this lets me smooth data and analyzing volatility trends. Main Goals: Ca...