Data Scientist can become an ambiguous professional title, given the overlap and cross-functional relationships a data scientist bears with other related domains. We will try to deconstruct this ‘mystical’ domain and give you a lowdown on the different types (sub-domains) of data scientists.
Following is a family tree of data scientists.
1. Data Scientist with a focus in Statistics.
Data Science applied to statistics is Data Analysis. Data scientists involved in dealing with the crunching of numerical data are required to exhibit their prowess in:
- Sampling, clustering, and data reduction
- Statistical modeling
- Experimental design
- Confidence intervals
- Predictive modeling, testing, and other related techniques.
Data analysis when packaged with domain knowledge (such as marketing, or risk analysis, or actuarial science) works wonders for the professional and the business. A data scientists can use his skill set to develop various new statistical theories and experiment with them. The fruit of the labor – predictive modeling – can be used to make business decisions and devise strategies.
2. Data Scientist with a focus in Mathematics.
Think of mathematicians and we think of boring, old, professors stuck in their university offices with their equations and theories. But with the importance of Data Science in the corporate world has spiked the demand for professionals with a focus in mathematics.
Owing to their mastery in numbers, data, quantity, structure, space, models, and operations research they are in great demand in the fields that demand analytics and optimization. From defense to astronomy, from inventory management to pricing algorithm, and various other domains like supply chain, quality control, yield optimization, the avenues for a mathematical Data Scientist are manifold. In these roles, they are required to carry out digital signal processing, series analysis and transformative algorithms.
So if you like to mathematically model complex problems and solving them, this category of Data Science is for you.
3. Data Scientist with expertise in Data Engineering.
Data Engineers at times have been considered as Data Scientists. However these two roles are poles apart. While data engineers are concerned with ETL (Extract/Load/Transform), Data Scientists are involved in DAD (Discover/Access /Distil).
Data Engineers focus on software design and production algorithms with the aim of capturing and managing the ever-expanding data. They ensure a smooth flow of data.
While data flow is ensured by Data engineers, data processing is handled by Data Scientists. In data processing, they extract value from data.
However, they need to work in close association to make sure impactful results are delivered. And if a Data Scientist needs to gain expertise in data engineering then he has to develop command over Hadoop, database/memory/file systems optimization and architecture, API’s, Analytics as a Service, optimization of data flows, and data plumbing.
4. Data Scientist with expertise in Machine learning.
If you wish to make a machine smart enough to make its own decisions, you need to develop spot-on algorithms. That is what Data Scientists proficient in machine learning do. They equip the otherwise puppet computer systems with algorithms that enable them to make intuitive decisions.
Using such algorithms, the machine figures out a pattern and gives outputs accordingly. There are a myriad of applications ranging from face recognition and search engine optimization to product recommendations and personal assistant.
5. Data Scientist with expertise in Business Analysis.
All the grind in data science is done to meet certain business objectives. Data science is a means to an end. A perfect amalgam of business acumen and numerate skills turns Data Scientists to business analysts.
As a Data Scientist involved in business analysis, you might have to work on ROI analysis and optimization, dashboards design, performance metrics determination, high level database design, etc.
6. Data Scientist with expertise in Software Engineering.
A coder who can utilize one’s programming capabilities to handle data and find insights makes this category. You have got to have your left brain functioning really well to be a Data Scientist while being a programmer.
You have got to have a command on a number of programming languages such as R programming, Python, Apache Hive, Pig, Hadoop etc.
7. Data Scientist with expertise in Spatial Data.
With the advancement of GPS based systems, spatial analysis is becoming an indispensable pillar of the industrialized world. Being a Spatial Data Scientist, you might have to work in any of the four disciplines- GIS, DBMS, Data Analytics, and Big Data Systems. Finding its application in domains like Google maps, navigation systems and weather forecast, spatial data is gaining paramount importance.
So, which of these 7 focus areas do you plan on specializing in?