There is no denying that data science has been one of the most in-demand and exciting career options over the last decade. A data scientist does various things, such as predicting customer behavior, improving healthcare results, and essentially helping organizations make smarter decisions with data-driven insights.
Now, if you are considering becoming a data scientist, you do not have to be a genius at mathematics or get a PhD first to get started. Your focus point should be having the right road map, being curious, and building a career with consistent practice, even if you are starting from scratch.
Data Scientists – What Do They Do
Before we analyze the steps that you need to explore if you want to become a data scientist, you must understand the role first. You should know that as a data scientist, you will be collecting, analyzing, cleansing, and interpreting data to solve business problems. In this role, you will be working with datasets, visualizing data insights, building models, and communicating your findings to potential stakeholders.
Now that you know what this rule comprises, you are in a better position to decide whether or not this career aligns with your potential strengths and interests.
Build A Sound Foundation in Programming & Statistics
There is no denying that having a solid foundation in math, statistics, and programming is essential, especially if you are serious about learning how to be a data scientist in a practical way. With that said, we recommend focusing on statistics, basic calculus, and linear algebra.
When it comes to programming, learning SQL and Python is absolutely mandatory. The best part is that you can choose from many online courses and free education resources that can make your learning experience accessible, especially if you are self-studying.
You might want to improve your skills by partnering with data companies, such as Intuit, which will allow you to analyze real-world business scenarios.
Learn the Use of Visualization & Data Analysis Tools
Once you have really understood the basics, it is time to move on to specific data analysis and visualization tools that are used routinely in this field. You can take advantage of libraries, such as Pandas and Matplotlib, as these can help you visualize and analyze data effectively. Power BI and other tools are also valuable for presenting your data insights clearly.
Since you want to have a career as a data scientist, you should know that potential employers look for data scientists who can transform raw data into stories that drive desirable actions.
Take on Projects for Hands-On Experience
The best way to polish your skills as a data scientist comes down to getting hands-on experience through projects. Of course, you cannot rely on theory alone to get a job. Your best bet is to start taking on small projects where you analyze public data sets, build predictive models, and create dashboards. Do not hesitate to share your work on GitHub or establish a personal portfolio website.
You need a platform to display your real project so that your potential employers can see how you apply your knowledge to real-life problems.
