What kind of background should a data scientist have?
A candidate aspiring to enter the data science field should have a background in a statistical programming language such as R, MATlab, SAS, Python or another database query language like SQL, Hive or even Spark. Having knowledge in these programming languages is not only a great stepping stone for entering a career in data science, but typically a requirement for future advancement.
Data Scientists must also have a basic understanding of statistical data and be able to provide a correct definition of assumed values. They should be familiar with concepts such as statistical simulation, distribution and maximum likelihood estimation. Companies depend on statisticians to help stakeholders make data driven decisions around design and evaluation experiments.
What skills are necessary to excel in a data science career?
When working for a large corporation, these positions are especially data-driven and requires the data scientist to be familiar with machine learning methods such as random forest and ensemble methods. Many of these techniques can be implemented using R or Python library.
Analyzing the data can sometimes be confusing and difficult. Because of this, it is important to know how to handle defect data. Examples of defect data include missing values and string formatting inconsistencies. This knowledge is most important when working with small companies or companies that do not have a data-driven product.
Data visualization and communication is very important when making data-driven decisions as corporate data should be regarded as the first scientist who helps others to make data-driven decisions. Exchanges, meaning the way you describe the data discovery or technology, can be technical and non-technical. It is important to become familiar with the tools necessary to visualize data and visual coding principles that illustrate the exchange of information behind the data.
It is also vital to have a strong software engineering background. Data scientists are often responsible for processing large amounts of data records and potential data-driven product development.
Data science is still a fluid concept with multiple pathways to success. Explore these featured degrees to develop an understanding of the educational paths that are available to launch you data science career.