* Prerequisites: Basic understanding of mathematics (especially statistics for Data Science roles), and some familiarity with computers and software. Specific programming languages (Python, R, Java, etc.) will be learned within the chosen path. * Learning Sequence: * Phase 1: Foundational Skills (All Roles): * Learn SQL: Focus on querying and manipulating data. Use online courses, tutorials, and practice exercises. * Data Analyst: * Phase 2: Choose a Career Path and Specialize: * Deepen SQL skills. * Develop strong communication skills: Practice explaining complex information clearly and concisely. * Learn Data Visualization Tools: Start with Excel for basic visualizations, then progress to Power BI or Tableau. * Consider introductory statistics courses. * Data Engineer: * Develop strong business acumen. * Master data visualization tools (Power BI, Tableau). * Learn data wrangling techniques. * Gain experience with databases (SQL and NoSQL). * Learn about data processing tools (Airflow, Spark, Hadoop). * Learn about streaming technologies (Kafka, Kinesis). * Develop cloud platform expertise (AWS, Azure, GCP). * Learn Python or Java. * Strengthen statistical knowledge (hypothesis testing, regression, etc.). * Learn machine learning algorithms and techniques. * Gain experience with model deployment and evaluation. * Data Scientist: * Master Python (including libraries like scikit-learn, pandas, NumPy). * Strong foundation in software engineering principles. * Experience with system design, distributed training, and model deployment. * Consider a formal education (Master's degree). * Machine Learning Engineer: * Proficiency in Python and relevant ML frameworks (TensorFlow, PyTorch). * Learn data structures and algorithms. * Gain experience with software development lifecycle (SDLC). * Software Engineer (with focus on ML/Data): * Master at least one programming language (Python, Java, JavaScript, etc.). * Deep understanding of machine learning algorithms and their limitations. * Develop expertise in relevant areas like MLOps. * Personal Projects: Work on projects that apply your skills to real-world datasets. Contribute to open-source projects. * Online Courses and Certifications: Utilize platforms like Coursera, edX, Udacity, DataCamp, etc. to gain structured learning and certifications. * Practice Suggestions: * Learn about cloud services and DevOps practices. * Career Progression: Consider the long-term career goals and potential for growth within each role. The video suggests that Data Analyst can be a stepping stone to other roles, but each path has its own unique progression. The choice should align with individual strengths and interests. * Build a Portfolio: Showcase your projects and skills through a portfolio website or GitHub profile. * Networking: Attend meetups, conferences, and workshops to connect with professionals in the field. * Mentorship: Seek out mentors who can provide guidance and support. * Kaggle Competitions: Participate in Kaggle competitions to test your skills and learn from others.