As a absolute beginner to Data Science, I have recently completed the Python for Data Science at Cognitive Class. When I progressed to Data Analysis, I found myself struggling to understand the statistical concepts and syntax as it began to skip explanations.
As my lecturer did not teach us about the concepts behind statistical methods, I have little knowledge on the concepts and only know how to follow the formulas. I wish to pursue a Data Sciene internship but I doubt I can complete so many courses in a month or two.
My questions are:
- How do I work towards Data Science?
- Assuming that I'll be using ML frameworks such as scikit-learn, what are the mathematics required to achieve this? Should I start from pre-calculus?
- If I'm not using frameworks, what kind of mathematics would I need?
- What mathematics foundation would I need to start learning Data Science?
Note that artificial intelligence, machine learning, data science and data analytics have some overlap, but they're not the same. A good first step might be to be more clear about your goal.
How do I work towards Data Science?
There's courses and books, but don't forget to practice, e.g. on Kaggle.com.
What mathematics foundation would I need to start learning Data Science?
Statistics is very important and linear algebra is also useful.
I doubt I can complete so many courses in a month or two.
No, you cannot.
Simply start to work in Data Science field and practice,practice,practice and again - practice on different DS problems. As Mark said - Kaggle.com is a very good site for DS practical training.
Data Science is multidisciplinary field and involves mathematics, statistics and computer science. I would say that there is no a "key" field in math, which would make you a super-strong in data science. Everything equally counts as it broadens your knowledge and data analysis "intuition". The more you know - the more tools you can choose from and more insights you can gain from the data. So if you wish a recommendation - you can start from calculus, differential equations, series, limits, group theory, sets and many more. Of course you MUST know linear algebra, curve fitting, least-squares and other error estimation methods, ROC curve - ML classification quality analysis, etc, etc, etc. In the end - just START :-)