Tier A in CORE terms means selective, well-cited, respected, one step below the flagships. For most researchers, most of the time, that is the sweet spot: real prestige, better odds, reviewers who read.
- Unlike ML, data science deadlines scatter across the year; you can usually find a strong venue that fits your timeline - Scattered deadlines are exactly the ones that slip; track them or lose them - Watch scope drift: what a venue published three years ago may not match what it wants now - Read recent proceedings; venues evolve faster than their reputations - Do the boring logistical check: location, cost, whether your department funds it
VenueFit is free and lists tier A data science conferences with their submission dates, so you write backwards from a real deadline.