This video discusses the journey to becoming a data scientist, emphasizing the importance of a strategic learning approach. It begins by highlighting the significance of deep understanding of statistics, programming, machine learning, and business in data science. The speaker shares their own path, which includes a Master's in Computer Science and Applied Mathematics, and working closely with data science colleagues at Microsoft.
The video then breaks down the path into three key areas: statistics, programming, and machine learning. For statistics, it recommends the free Coursera course "Data Science Math Skills" by Duke University for basic mathematical concepts. For programming, it suggests Python due to its versatility beyond statistics and machine learning. The video provides a link to "learnpython.org" for hands-on coding exercises.
For machine learning, the speaker provides two recommended courses: "Applied Machine Learning" by Michigan University on Coursera, and "Machine Learning Specialization" by Andrew Ng on Coursera. It emphasizes that while these courses provide a good foundation, deep understanding typically requires additional hands-on practice, which can be gained from Kaggle.
Lastly, the video stresses the importance of business knowledge in data science, using a hypothetical example of a YouTube experiment. It concludes by encouraging viewers to learn from their mistakes, providing free resources, and offering tips for job applications.
1. Scientists have long been interested in machine learning, but it poses challenges for computers due to the complexity of the subject.
2. Becoming a data scientist requires a deep understanding of statistics, programming, machine learning, and business.
3. A basic knowledge in these domains can be achieved in a few months, but becoming an employable data scientist is where the real challenge lies.
4. The speaker has found a path that provides necessary skills and prepares for data science interviews at big tech companies.
5. The path includes a master's in computer science and applied mathematics, working closely with data science colleagues at Microsoft.
6. The speaker recommends a free 4-week course on Coursera called "Data Science Math Skills" by Duke University for beginners.
7. The speaker also recommends an advanced course called "Introduction to Statistics" by Stanford University for more in-depth understanding.
8. Python is recommended as the programming language for data science, with resources like learnpython.org for learning.
9. The speaker emphasizes the importance of understanding data to apply machine learning algorithms, suggesting websites like UC Irvine's machine learning repo for data.
10. SQL is crucial in data science, with resources like W3Schools for learning.
11. Data visualization is another important aspect of data science, with libraries like Matplotlib and Seaborn recommended for learning.
12. The speaker recommends the "Applied Machine Learning" course by Michigan University on Coursera for a more applied approach to machine learning.
13. For a deeper understanding of machine learning, the "Machine Learning" specialization by Andrew Ng on Coursera is recommended.
14. Practicing on Kaggle is suggested after completing the courses to gain confidence and build a portfolio.
15. The speaker advises focusing on the basics of machine learning and AI first, and only expanding knowledge if time permits.
16. Business knowledge is crucial for data scientists, as it helps in defining business metrics and making data-driven decisions.
17. The speaker suggests watching a specific video on YouTube to help answer metrics-based questions commonly asked in data science interviews.