It's important to have a roadmap for Data Science and Analysis before starting a career in this rapidly evolving field. In this thread,l'll share the roadmap that has helped me establish a solid foundation, comprehend the entire spectrum, & keep pace with the latest technologies. Why is it important to have a roadmap?

- Clear understanding of the field: Data science & analysis is a multidisciplinary field encompassing statistics, machine learning, programming, & data visualization. It helps in understanding skills required for each domain, & how they fit

As a Data Science and Analysis aspirant, being proficient in data science and analytical tools is never enough. Here are 10 skills with tools and libraries that have helped me in elevating my expertise and made me an in-demand professional, capable of transcending disciplines. Navigating the vast array of skills required in the constantly evolving field of data science can be daunting. That's why I'm providing this guide to help adopt a straightforward yet highly effective approach that sets you apart from other applicants and scientists in this field.

Are you at the beginning of your Data Science and Analysis journey and feeling unsure about which programming language to choose between Python and R? Check out this thread (Python vs R debate) where I will clarify any confusion you may have. It is imperative to know at least one programming language as an aspiring data scientist or analyst. Python and R are the most important programming language for several data science and analysis tasks. However, the Python and R debate may suggest you choose either Python or R.