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Why Most Data Science Roadmaps Don’t Work & How to Build Your Own
- Many data science roadmaps fail because individuals tend to frequently switch resources, leading to a lack of concrete progress.
- Building a personal roadmap in data science involves considering factors like current skill level, professional situation, existing knowledge, and learning style.
- Identify topics you know and those you need to learn, dedicating 5% of your time to understand concepts and 95% to practice them.
- Practice by solving exercises, discussing concepts, and engaging in Exploratory Data Analysis (EDA) on simple datasets daily.
- Regularly practice Python, SQL, as well as machine learning (ML), deep learning (DL), and natural language processing (NLP) concepts on varied datasets.
- Focus on data cleaning and EDA, undertake at least 20 projects, and prepare for interviews by simulating interview scenarios and seeking feedback.
- Engage in mock interviews, seek guidance from experienced data scientists, and build a portfolio website showcasing your projects and skills.
- Apply for internships, seek mentorship, and continuously improve by learning from others and refining your approach to learning data science.
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