Programming skills enable someone to be efficient in using various tools. These tools include database querying languages such as SQL and statistical languages such as Python, and R. R programming language is mostly preferred as it is specially designed for data science needs. It is used to solve a lot of problems encountered in data science and statistical issues.
Python is a well-known programming language used by data scientists. This is because of its versatility. It can be used for most of the processes that are involved in the field of data science. It also allows one to create datasets, and it also takes various forms of data as one can easily import SQL tables into their code.
Using the Hadoop platform
In a case where the volume of data is more than the memory of the system you are using, one can send data to different servers, and this is where Hadoop is used. It conveys data to various points in a network, and it can also be used for data exploration, filtration, sampling, and summarization.
SQL (Structured Query Language)
You need to be proficient in SQL since it helps in communicating and accessing data. One can access and manipulate the information and data that is stored in their databases, as well as for creating and altering new tables. It allows users to retrieve the specific data they are looking for when they need it using a simple programming language. To fully understand SQL, it is important to first know exactly what a database is and you can do that through big data analytics courses in Singapore.
A background in statistics
One should have some good grounding in statistical knowledge as it is vital for a data scientist. This will enable them to have an idea of distributions, statistical tests, and maximum likelihood estimators. This is because, in places such as companies, the stakeholders will depend on the data analysts’ support. This is to enable them to make design and decisions. Also, it is to evaluate precise results from the data that has been gathered.
Reporting technical findings
One should also know how to accurately report professional results in such a way that they are understandable to the involved stakeholders. They should clearly and fluently translate technical findings in such a way that it provides quantified insights. The data scientist should pay attention to the results and values that are found in the data being analyzed to give recommendations and conclusions.
Skills in data visualization is a necessity as it enables a data scientist to present data in a visual way rather than words or numbers. One should learn the basic principles of visualizing data efficiently and effectively and also have proper knowledge of using and implementing data visualization tools.
Most companies want to see that a data scientist is a problem solver with data-driven efficiency. This brings the need for you to have a solid understanding of the problem you are about to solve. Also, to have a core understanding of how the business operates to take your efforts to the right place. Discern the problems that are important to solve as they are crucial to the company and find out the best way the business should be leveraging this data.