How to learn data science from scratch?

If you’re someone willing to learn data science from scratch, then you’re at the right place. Data science is the trending career option as it has become a dream job for many aspirants. Well, you need not have an undergraduate in engineering or a background in stats/math, but if you have, then it is beneficial. There are various data science certification courses available online and offline that can help you build your career. 

To build a career in data science you can do it in three ways: 

• A bachelors or master’s degree in data science
• Participate in a Bootcamp program
• Self-learning

Computer science is a vast area. Data science is just a part of a computer science course. Now, if you’re thinking about a computer science course, then it is all about building and utilizing computers efficiently. Everyone comes from a different background, so in this article, we have provided the details of how to start data science from scratch. 

Steps to Learn Data Science From Scratch

1.Accept the Challenge

Your initial step is to approach any mental barriers that resist you from taking up the challenge. Then learn from the necessary materials and develop data science skills. Data science is not a scare. All you need to do is merge your business know-how and intuition with data science to build a successful career. Although data science is complex, you should have the desire to learn and work hard to excel in the subject. 

2.Focus on the basics

Focus on the fundamentals of data science. There are blogs, articles, videos, experts, and introductory courses that can help you develop your data science knowledge. You need to build a strong foundation in data concepts and best practices to ace complex topics over time. After having a strong command over the basics like the data ecosystem and life cycle, data governance and privacy, data integrity, and data wrangling, you can begin exploring the tools and frameworks required to apply data science in your career. 

3.Learn the tools and frameworks

You can find various data science tools and frameworks for using data science in your work. These frameworks and tools help you to practically enhance your skills. It builds the capability to take a raw dataset, simplify the story it tells, and apply it while answering relevant work questions. 

4.Learn from real-world examples

Real-world examples can be a useful resource to learn data science. You can analyze how other data science professionals solve problems. In a similar way, you can imagine your way of solving the problem, the impact of their actions, and implement that knowledge into practice. 

5.Find a community

While learning data science, you will come across a few professionals with the same goal who can motivate and support you. You can find these professionals in online forums, social media, affinity groups within the organization or geographic area, or a bunch of learners in an online class.

6.As Questions about data

To widen and deepen your skills, you need to ask questions about big data. Each question offers a new opportunity to uncover more insight and gain skills. These questions help you learn a new coding language, analysis method, regression, or visualization tool that helps you answer a specific business question. 

Conclusion

Learning data science can be an investment in your career and company. Having a good command over the basics, tools, frameworks, real-world examples, a strong community, asking questions, and confidence will help you communicate and drive insightful, data-driven decisions for your work.