This video provides a path to becoming an employable data scientist, covering topics such as statistics, programming, machine learning, data visualization, and business. The speaker emphasizes the importance of being strategic in learning and avoiding getting too deep into specialized topics or trying to learn everything. The video also recommends various courses and resources for each topic and offers advice on creating a strong resume.
1. Scientists have been interested in Machine Learning for a long time.
2. Computers started out from a low level making them bad students.
3. A PhD is required to be a Data Scientist due to the deep understanding of Statistics, Programming, Machine Learning, and Business required.
4. Having a basic knowledge in all these domains is achievable in a few months.
5. Becoming an employable Data Scientist is the real challenge.
6. Knowing statistics is necessary to be a good Data Scientist.
7. A good knowledge of Mathematics is required to learn Statistics.
8. Statistics heavy Data Science roles make up a small minority of 5% of total roles.
9. Programming is the second pillar of Data Science.
10. R and Python are the two programming languages primarily used for Data Science.
11. Machine learning requires knowledge of the data to be applied, often using SQL.
12. Data scientists use data visualization to gain insights from data.
13. Matplotlib and Seaborn are popular data visualization libraries for Python.
14. Deep learning is an advanced concept not required for most Data Science jobs.
15. Knowledge of business metrics is important for Data Scientists.
16. Metrics-based questions are common in Data Science interviews.
17. Many people struggle to get a good job due to their lack of knowledge on resume-making.