Once you’ve mastered the basics, there are a few advanced topics that can really help set you apart as a data analyst:

• Logistic Regression: Used when the dependent variable is binary (yes/no, 0/1). For example, predicting whether a customer will churn or not.

  Real-life Example:
  A telecommunications company might use logistic regression to predict whether a customer will cancel their service based on variables like contract length, usage, and customer service interactions.

• Time Series Analysis: Used to analyze data that is collected over time (e.g., stock prices, weather data).

  • Moving Averages: A technique used to smooth out short-term fluctuations and highlight longer-term trends.
  • ARIMA Models: Used for forecasting based on time series data.

  Real-life Example:
  A sales analyst might use time series analysis to forecast future sales based on past sales data, accounting for trends and seasonal variations.