A data scientist performs research and analyses the resulting data in order to help organizations detect risks, trends, weaknesses and business opportunities. Based on the huge amounts of data they collect, the data scientist will communicate informed conclusions and recommendations to organizational leaders. In order to be successful, data scientists will need the right academic training with a particular set of skills.
Get a Degree
There are basically three majors for data scientists: math, statistics or computers. Regardless of the degree choice, all data scientist students will need to study a particular business domain such as finance, logistics or even education. When it comes to graduate degrees, there are many choices. For example, a Master of Science in Data Science is a new interdisciplinary degree that combines statistics, computer science and data visualization. These programs usually adopt a project-based approach that facilitates the development of the technical skills to solve problems and soft-skills to influence data decisions.
On the other hand, a Master of Science in Business Analytics will help data-driven students sharpen their abilities to interpret complex data, recommend actionable decisions and encourage informed decision making. Business analytics degrees include topics like data modeling, predictive analytics and management decision making. Lastly, a Master of Science in Business Intelligence degree will merge real-world applications and project-based curriculums that will teach topics like data cubes, analyses and warehouses.
Take the Right Classes
Students who have a strong technical background may be able to directly become a data scientist through selectively studying classes and reinforcing certain core competencies. The most basic class that a data scientists needs to take is an introduction to programming course that teaches the basics of PHP, Javascript and HTML/CSS and the C languages. A class devoted entirely to Python is a must because students will learn about trees, classes, exceptions and simple algorithms.
Data scientists should take a variety of programming classes, such as the cognitive principles of computing and interactive Python programming, which will focus on creating graphical user interfaces. Coursework on algorithmic thinking will introduce the concepts of data structures and graph algorithms. Statistics modules will enhance the learners’ understanding of concepts like the Bayes Theorem, First Step Analysis and Law of Total Probability. Other abstract concepts will be covered, such as joint, discrete, marginal, continuous and probability distributions, according to Forbes.
Cultivate the Right Skills
Data scientists will need technical skills related to programming, data analytics and computer science, according to Payscale. As far as programming goes, they should master Python, which is the most common coding language used in data science research. Other useful codes for data scientists include C/C++, Java and Perl. Data scientists should be highly familiar with Hadoop Platform, which is a big data application framework used to process mountains of raw data.
Related Resource: Data Scientist
Even though big data platforms like Hadoop and NoSQL are quite popular, knowing traditional SQL coding and database management will help. When it comes to non-technical skills, data scientists should have strong intellectual curiosity and be passionate about analyzing unstructured data. They need a solid understanding of the industry they are working in, which will enable them to identify which business problems are most critical. Data scientists also need to be someone who can clearly and concisely translate their technical findings for non-technical audiences.
