Data science offers lucrative career options. Thus, millions
of graduates aspire to break into **data
science career**. But data science is also a complex field, making it
extremely challenging for aspirants to fully understand and do well, deterring
many aspirants. This is especially true for data science aspirants who come
from non-technical, non-STEM background, and working in a completely different
domain.

Fortunately, many people with a non – technical and non-STEM background have transitioned to data science without pursuing a degree in data science. In this article, we will explore how someone with a non-related background can learn **data scientist skills **and break intoa** data science career. **

**Level 0 – Get the basics **

Data science is a complex field which requires a strong understanding of statistics, machine learning, visualization, and more. The better fundamentals, the easier it is to work in data science. You will need strong exposure to the fundamentals –statistics, programming, and mathematics.

*Statistics and probability – * Data scientists are modern statisticians. If you’ve had exposure to statistics and probability in high school, review your fundamentals and get hang of the advanced statistics. The better knowledge you have, the smoother journey you will have in data science. Concepts like measures of central tendencies, dispersion, probability distributions, etc. will make advance data science concepts easier for you.

*Mathematics* – There are three major areas that you must focus on in mathematics- calculus, integrals, and linear algebra. Calculus is important for optimization. Integrals is important for hypothesis testing and probability distributions. Similarly, linear algebra is important for machine learning and deep learning and concepts like principal component analysis and recommendation systems.

*Programming* – Everything you learn in data science is implemented with programming. So the better programming you know, the better you will be able to apply data science. SQL, Python, and R are fundamental programming languages that you should master.

Level
1 – Specialize

Once you have cleared your fundamentals, the next step is to specialize in one of the areas of data science. You can specialize in analytics, natural language processing, machine learning, deep learning, computer vision, etc.

There are other options as well. So it’s best to explore before you specialize.

** Level 2 – Practice **

Unless you put to use what you learn, it will be hard to remember. There are free resources that you can use to put your learnings to work. The following three resources will get you going —

1. Leetcode – Leetcode has a repository of problems that you can work on and get practical experience of solving data science problems.

2. Github – Github has a repository of pandas practice problems. Completing these practice problems will equip you with data manipulation skills which is frequently required for data scientists.

3. Kaggle – This will give you access to thousands of datasets that you use to build machine learning models. Additionally, you can participate in competitions to get a better understanding of solving problems.

**Level 3 – Demonstrate your skills **

1. Do projects – Take a few projects outside of your practice and do it from start to finish. You can take ideas from Kaggle after discussion or have your own. Once you complete the project, publish it online and keep the link to share.

2. Get a data science certification – Taking a globally –recognized **data science certification** helps to prove your competence as a data scientist. This is especially helpful for beginners as without industry experience, it is extremely difficult for recruiters to assess your skills. A global **data science certification** proves your knowledge and competence. A certification will also refresh your knowledge and make you aware of the gap in knowledge or skills you may have.

**Final word**

Data science is one of the most lucrative and sophisticated career option today. While most people break into a data science career after pursuing a degree, many people have transitioned with complete self-study and preparation. Above approach is a comprehensive approach for anyone who are unable to take a degree and are highly interested in a data science career.