A/B testing is the backbone of data-driven decision making. But running experiments at product scale is very different from testing two button colors on a landing page. When millions of users, hundreds of metrics, and long-term outcomes are on the line, experimentation becomes both a science and an art. At Meta, our philosophy was simple: "If it moves, measure it. If it’s measurable, experiment with it." Behind that principle sits a framework designed to ensure experiments are run responsib...| DEV Community
Today, I explored common data issues in pandas and how to handle them. Here’s what I learned: 1. Handling Empty/Null Values Definition: Empty or null values are missing data points in a dataset that can affect analysis. importpandasaspdimportnumpyasnpdata={'Name':['Ramya','Aruna',None,'Sekar'],'Age':[25,np.nan,22,28]}df=pd.DataFrame(data)# Check null values print(df.isnull())# Fill null values df['Age'].fillna(df['Age'].mean(),inplace=True)print(df) 2. Removing Duplicates Definition: Duplic...| DEV Community
Are your teams turning today’s data deluge into AI-powered insight, or still drowning in information? We’ve seen that, with the right approach, firms can spot untapped mandates before competitors, free Sales, Marketing, Client Service, and Product teams for high-value conversations, and predict redemptions early enough to protect revenue. Read our…| Asset & Wealth Management - Alpha FMC
