Data Science involves: Statistics, Excel, Linear Algebra, Power BI, Machine Learning, SQL
Sub Category
- Other IT & Software
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Objectives
- Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems. Mining data from primary and secondary
- Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
- 1. Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML
- 5 Different Practical Data Science projects with ipython Notebooks
Pre Requisites
- There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. etc. but I never learnt it, yet I am able to work on machine learning.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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