[This is a series of articles where I will be testing A.I. with questions and publishing, in verbatim, the responses]

A.I. Author Response (ChatGPT):

Sure, here are some steps that someone with a non-technical background can follow to get into data science:

1. Start by gaining a solid understanding of the fundamental concepts of computer science and programming. This will require some self-study and dedication, but there are many online resources, such as tutorials and courses, that can help with this.
2. Once you have a strong foundation in computer science and programming, you can begin to focus on learning the specific skills and technologies used in data science. This may include topics such as statistics, data visualization, machine learning, and big data tools and technologies.
3. As you learn, it’s important to practice your skills by working on real-world data science projects. This will not only help you to apply what you’ve learned, but it will also allow you to build a portfolio of work to showcase your skills to potential employers.
4. Network and build connections in the data science community. This can be done through attending conferences and events, joining online forums and communities, and collaborating with other data scientists on projects.
5. Consider earning a certification or degree in data science. While this is not necessarily required to become a data scientist, it can help to demonstrate your knowledge and skills to potential employers.

Overall, becoming a data scientist from a non-technical background will require dedication and hard work, but with the right approach and resources, it is possible to develop the necessary skills and knowledge.

Answer 2:

1. Start by learning the basics of programming, such as Python or R, through online tutorials and resources.
2. Understand the fundamentals of statistics and probability, including concepts such as hypothesis testing, regression, and distributions.
3. Get familiar with common data science tools and techniques, such as data visualization, machine learning, and natural language processing.
4. Join online communities and forums to learn from experienced data scientists and network with other professionals in the field.
5. Pursue a certificate or degree in data science or a related field, such as computer science or statistics, to gain more in-depth knowledge and skills.
6. Build a portfolio of data science projects to showcase your abilities and experience to potential employers.
7. Consider internships or entry-level positions in data science to gain practical experience and build your resume.
8. Continue to learn and develop your skills through workshops, conferences, and continuing education opportunities.