For gaining the necessary prerequisite knowledge for data science. There are various books through which you can acquire knowledge of data science subjects.
Mathematics, linear algebra, probability, and statistics are the foundation of data science.
Learn how data scientists speak, work and think. Data Science has various components like data extraction, data transformation, cleaning, visualization, and prediction.
Spend time looking at the kernels in Kaggle or TopCoder competitions to learn from how other Kagglers approached the competition.
Doing this not only offers valuable hands-on experience with data science but also gets you the chance to be noticed while collaborating with some of the country’s top data scientists.
Data science is more of a practical field, in which to attain the true knowledge you have to solve real problems by working on live projects.
Find a data science project, whether it be a problem you would like to solve or learn, into a project that you will put as a portfolio piece.
Explore real-time case studies like how big enterprises are using data science to help them improve the organization and its profits.
And these case studies will help you in finding out problems to solve, and how to approach solving a particular problem.
Statistics, mathematics, python, R, SQL, business knowledge, and data visualization are some of the resources that can help you to become a data scientist with an art degree.