Why I Disappeared From Medium for Almost a Year

2026-06-27 · Source: Data Science on Medium · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

The author, returning to Medium after nearly a year, explains their hiatus from documenting their Data Analytics and Data Science learning journey. Initially, they aimed to share their progress and insights, but academic pressures including GATE preparation, final-year exams, projects, assignments, and placements disrupted their routine. This led to a realization that waiting for an ideal time to resume writing was ineffective. In January, the author made a quiet commitment, enrolling in a Data Science course without public announcements. Since then, they have consistently engaged in daily learning, project development, interview preparation, and problem-solving, steadily improving their skills. This experience fostered a new belief: that tangible work and achievements should precede verbal declarations of goals. The author now plans to use their Medium platform to chronicle the full spectrum of their learning process, encompassing both successes and challenges, with the aim of supporting others pursuing Data Science or Machine Learning.

Key takeaway

For AI students or aspiring Data Scientists embarking on a learning journey, prioritize consistent, quiet effort over public declarations. Your tangible work and daily progress are more valuable than announcing goals. Focus on building projects and solving problems, documenting the entire process—including challenges—to create a genuine record of your growth. This approach fosters true skill development and provides authentic content for sharing.

Key insights

Consistent, quiet effort and tangible work are more impactful than announcing goals.

Principles

• Action and achievement validate hard work.
• Don't wait for ideal conditions to start.
• Document the full journey, not just wins.

Method

Quietly enroll in a course, commit to daily learning, build projects, prepare for interviews, and solve problems consistently.

In practice

• Start learning a new skill without public announcements.
• Consistently engage in daily practice and project building.
• Document learning process, including mistakes.

Topics

• Data Science Education
• Machine Learning Journey
• Skill Development
• Personal Productivity
• Learning Documentation
• Goal Achievement

Best for: AI Student, Data Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.