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Sollers is a graduate school located in New Jersey, specializing in clinical research, drug safety and pharmacovigilance training.

Our graduate certificate and masters programs cover a wide range of subjects tailored to this fast growing industry, and our graduates go on to highly successful careers in the pharmaceuticals industry and healthcare industries.

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Sollers Blog

Data Scientist!

Posted by Phil on Jun 6, 2017 2:18:39 PM

The role of data scientist has become the most sought-out professions that have gained the attention of a number of professionals for its wide range of career opportunities. Data science is the concept that deals with interdisciplinary fields about various processes, systems, and scientific methods. Data scientists should have the ability to pull out necessary knowledge or insights out of a large pool of data sets including structured and unstructured ones. It is the process equivalent to potential knowledge discovery in databases (KDD). IBM Predicts Demand For Data Scientists Will Soar 28% By 2020 –Source: Forbes

Preparation to become a Data Scientist

One cannot just get into the idea of becoming a data scientist until the candidate is perfectly prepared. Preparation should be as much effective as a real professional only to compete with the fellow data scientist aspirants.  Among the different set of data manipulation skills, the candidates should equip themselves with effective software engineering skills and statistical knowledge. As far as a necessary qualification is concerned, recruiters don't have particular constraints as of which degree the candidate should possess. They find people from a variety of backgrounds including a basic college education, PhDs and doctoral degrees interested in taking up the role of Data Scientist. The ultimate requirement that most of the skilled recruiters look for is abundant creativity which would lead them to learn other core skills once they have entered into the field.


A data scientist aspirant should be able to crack down complex mathematical problems, statistical data (classified and unclassified) and relevant algorithms. The concept of machine learning has been widely sought-out in potential data scientists. Inevitably, Machine learning is seen as the buzz word in the data scientist industry. It is the process of understanding the intricate data linked with big data which would then be converted into value with the employment of artificial intelligence algorithms. The key here is the values will be obtained without the usage of explicit programming. Another pre-requisite for a data scientist is to learn to code. They should be able to understand the codes and manipulate it in such a way the computer analyses the data. To begin with, the open source language like Python will help the candidates.

It is very important and necessary to have a comprehensive knowledge of data lakes, distributed storage and databases for the purpose that all three are interconnected within the flow of data management. When you analyze the bigger picture of how data works, the data is stored in distributed networks, databases or across multiple distributed networks which can later be retrieved and used for the analysis of big data solutions.

The two key factors included in data scientist’s toolbox are data munging and data cleaning. The conversion of raw data into some other format in order to make it easier for analyzing and accessing is called data munging. Data cleaning is nothing but the elimination of bad or duplicate data. Though it is not mandatory to become an expert graphic designer, the basic knowledge of data visualization and reporting will increase the calibre of a data scientist.

After developing the above skill sets, data scientists shall master the other data science tools aiming to master their profession. Other programming languages such as R, Hadoop, and Spark will enhance the advanced skill sets to become an expert. Practice and community building are the final endeavors to set a milestone in the field of data science. One can have a dilemma on how to practice before even getting into the core. It is very simple but requires hard-work. People should start developing their own pet project from any open source data. Then try to enter into competitions followed by networking with active data scientists will do the trick. The process can be taken forward by joining a boot camp or volunteering as an intern. Community building is keeping oneself of aware of current trends in the industry by reading industry blogs and relevant websites.

Topics: Data Science