Data Science is a hot topic among the highly skilled professionals and organizations that focuses on the collection of data and derive meaningful solutions from it to grow the business. “The job of a data scientist is one of the most looked after jobs of the 21st century”. This quote appeared for the first time in the October 2012 issue of the Harvard Business Review, and it continues to remain relevant even today.
Finding the perfect aspirant for the role of a data scientist can be a complicated and tedious task. But with correct knowledge of what Data Science is and what skills one should look in an applicant, the hiring managers can save their organization from getting into a loss by recruiting the wrong candidate.
Data Science is about extraction, examination, and maintenance of data. It is a cross-disciplinary field that utilizes logical strategies and procedures to draw bits of knowledge from information. As we entered the age of Big Data, the need for storage of data grew manifold. A perfect candidate should master the following fields:
- Statistics and Mathematics
- Programming and Machine Learning
- Data Intuition
- Domain/ Business Knowledge
- Unstructured Data
1. Statistics and Mathematics
A candidate applying for the position of a data scientist must have a decent understanding of numerous mathematical ideas and concepts. The candidate should have a good amount of knowledge in topics such as statistics (both descriptive and inferential), linear algebra, probability, and calculus. Apart from maths, the applicant should also have their educational background in the field of statistics such as linear regression.
2. Programming and Machine Learning
The candidate who is applying for the role of data scientist must possess strong programming skills in R, Python, MATLAB, and SQL, and a good understanding of data structures, like, trees and graphs. Also, the candidate must possess good knowledge of excel, but if the candidate does not know any skills apart from Excel, then they do not qualify to become a data scientist. Many times, knowledge of Hadoop is not compulsory to be known by the data scientist. However, it is still majorly preferred in many cases; wherein one encounters a vast volume of data that is Big Data which exceeds the memory of your system. So, to transfer all such data one needs the help of the Hadoop language. These programming languages are used for data filtration, exploration, and data summarization.
The concepts that a candidate must have in the understanding of Machine Learning are:
- Clustering algorithms such as k- means
- Boosting and Bagging
- Bias-variance tradeoff
- Binary multi-class and multi-label classification
- Supervised learning and unsupervised learning
3. Data Intuition
Having a degree or certificate in data science does not truly mean having a good sense of data intuition. A candidate can only be labelled as a good data scientist only when they can identify the patterns between the structured and unstructured data. Data scientists have to constantly work on different sets of data using the skills that they have mastered. As a hiring manager, always ask the candidate to discover a particular type of pattern in your data, to find out whether the candidate is a perfect fit or not.
4. Domain/Business Knowledge
The applicants must have in-depth knowledge of the industry wherein they apply for as data scientists. They should be efficient in analyzing the problem which a company is facing and tackling that issue by reading through the data. It is imperative to remember that the depth of domain knowledge will depend on the experience of the applicants.
5. Unstructured Data
Every data scientist needs to work on unstructured data. Unstructured information is unclear data that does not fit into the database tables like blog entries, client surveys, internet-based life posts, video feeds, and so on. They are substantial writings put together. Arranging the information is troublesome because it is not streamlined. Many people term unstructured data as “Dark Analytics” because of its complex nature.
The required non-technical skills in a Data Scientist
Apart from the data scientist skills, there have been certain non-technical skills that must be seen by a recruiter or the hiring manager while recruiting a data scientist. The non-technical skills are as following:
“I have no special talent. I am only passionately curious.” – Albert Einstein.
This phrase implies to Data Scientists.
Curiosity can be characterized as a craving to secure new learnings. As a data scientist, one should be able to ask questions about data since data scientists spend most of their time finding and planning data.
One should regularly update their knowledge by researching on the content related to their work either online or by reading certain relevant books. Also, try not to grasp the information alone but seek knowledge of identifying and implementing different solutions to a single problem. Hence, it is only the curiosity and interest that will empower one to filter through the information to discover answers and to gain more knowledge.
2. Business understanding
You will be requiring a strong comprehension of the business, that you will be working in, and recognize what business issues your organization is attempting to solve. Also, one should know the effects it can have on the business. This is the reason why one has to think about how organizations work so that one can coordinate his/her endeavors the correct way.
3. Communication skills
Organizations look for a strong data scientist who can easily and smoothly make an interpretation of their specialized discoveries to a non-specialized group, for example, the Marketing or Sales department of the company. A data scientist must empower the business to settle on choices by equipping them with evaluations, and making their non- technical associates understand the same, so that they use the information properly.
As a data scientist, one needs to realize how to make a storyline around the information to make it simple for anybody to understand it. For example, showing a table of information isn’t as powerful as sharing the bits of knowledge from that information in a narrating manner.
Lay more focus on the results and values as most business owners don’t want to know what you have analyzed, they are interested in how it can impact their business positively.
A data scientist can never work alone. One has to work with other executives to develop strategies. Sometimes they work with the product managers and designers to create better products, marketing team to launch campaigns that can be fruitful for the company, and software developers to create data pipelines and improve workflow. The data scientist literally has to work with everyone in the organization, including the organization’s clients.
Whether an individual is the hiring manager or recruiter these are certain qualities which one can look when hiring a successful data scientist. Also, one must look at a few non-technical skills in a candidate such as curiosity, teamwork, etc. Recruiting a successful data scientist with all these qualities will guarantee growth and prosperity for your organization.