You can Build a Successful Data Science Career with an Arts Degree

In this article, we are going to explain how art degree graduates can fit into a data science career.

Data science combines multiple fields, including statistics, scientific methods, artificial intelligence, and data analysis, to extract value from data. Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data.

Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. While the transition from academia to data science is fairly common for STEM degree holders, it's so far been much rarer for graduates with non-STEM backgrounds. This article is going to explain how art degree graduates fit into a data science career and where to start for those considering a career in data science from non-STEM backgrounds.

Some practical advice:

Data scientist builds software that can be used by the business to make predictions, personalized recommendations, and more. In data science, the evidence would generally be based on numerical data, and the storytelling is led by data visualization but the underlying quest and patterns of thinking are similar.

A degree in Humanities is one of the most useful backgrounds for an industry data scientist. A humanities degree makes an expert in going deep into the topic, stating research questions, etc. Build knowledge of probability and statistics and get basic knowledge of writing code like Python, R, SQL, etc, and visualizing data. These two areas will get you 90% of the way to being a functional data scientist.

Here are the some of the tips help you to bring your career towards data science:

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.

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