Regardless of your previous skills or experience, there is a clear path for anyone to become a data scientist. Here is the ultimate checklist of what it takes to become a data scientist.
No matter what type of company you will be joining, or role you will be involved in, you will be expected to know how to use the coding tools of the trade. This means you have to be good in languages such as R,Python and a querying language such as SQL. When it comes to coding, there is always a sharp debate between coding with Python and using Java. However, data scientists prefer Python because ofits advanced libraries and ease use.
Machine Learning and AI
Data science is arguably the #1 profession in America as reported by Glassdoor. Given the high starting salary attracting data science,it matters for tech-savvy to do what it matters to become one. To become a data scientist is not a mere fete. Extracting true value from business requires one to have a unique combination of technical skills such as programming,mathematics, storytelling as well as people’s’ skills.
Machines do a great job in defining computing and categorizing high volumes of structured and unstructured data. However, they are not able to work on their own entirely. They can learn without supervision,identify trends and patterns among other tasks, but they need first to be fed with the required input. As a data scientist, you need to have a basic understanding of machine learning and artificial intelligence.
If you need to solve problems, you need tools and infrastructure that can help you meet the required solutions. You will need to integrate data into an organization’s database and systems. To work with big data, you will need to be good on Hadoop, Spark among other. To collect, store and manage data, you will need to be good at SQL.
To become a data scientist, you must be a good communicator.A data scientist is tasked with working with different stakeholders and professionals to solve enormous problems. They must be very good at listening and have an intuitive understanding of data as well as the domain they work in.They must have a good understanding and articulation of business objectives.They should be able to visualize data and communicate it in a simple manner that is easy for everyone to understand. It becomes difficult for people without technical knowledge to understand patterns in data if a data scientist doesn’t visualize it appropriately.