What kind of Data Scientist are you?
Guide to help you become the right data scientist the world deserves.
Alright, so you have taken the decision to be a Data Scientist (or analyst or whatever, you get the point!). That’s Great!
Hey! it’s the sexiest job of 21'st century. You should obviously go for it.
But there is a problem. Now before I tell you the problem let me tell you what data science field is made up of. Knowing this will help you understand the problem in a better way.
Data Science = ?
Data science in itself is not a field but a combination of many. Mathematics, Statistics, Programming, Data Mining, Databases, and Visualization are some of the key component domains which come together to create the world of data science.
Also on the other hand data science and analytics is a field that is one of the most rapidly advancing fields out their today. Every day there is some new advancement in the field of computer vision, natural language processing or in some other sub field of analytics and data science. It’s like an endless supply of innovation and improvements happening in the domain.
The Problem
Being such a huge field with so many interconnected disciplines and so many advancements coming out regularly, It’s difficult for a person to start and easy to get overwhelmed.
The problem is a lot of jobs which are really different from each other are termed as a single job — Data Scientist. This can lead to a lot of confusion in terms of the path to be followed and skill set to be acquired to get the job.
Data science is divided into multiple sections and areas which are practiced in industry, so it becomes very important to be clear about what kind of profile and role you are targeting for.
The Solution
The best solution to above problem is to be informed about different types of roles present in the industry under the flag of data scientist and to filter out the role which you want for yourselves.
Here are the most common data science roles which are practiced in industry today.
Machine learning Engineer / Data Scientist
Now I am discussing these roles simultaneously but there is a slight difference between these two roles.
Data Scientists are focused on improving the model relative to business metrics such as conversion ratio etc. They are more inclined towards improving existing machine learning systems using new approaches and ideas and through their strong mathematical background.
Machine learning engineers are more focused on the production level deployment of machine learning models, their efficiency, reliability, and speed.
A typical machine learning project team consists of both, data Scientists as well as machine learning engineers.
Average Payscale (in India)
For Data Scientist: Rs 7,86,000/annum
For Machine learning Engineer: Rs 7,50,000/annum
Average Payscale (in US)
For Data Scientist: $ 96,000/annum
For Machine learning Engineer: $ 101,000/annum
Skill-set: Statistical Analysis, Machine learning, Deep Learning, Python, R, Mathematics
Data Analyst
Today everybody wants to be a data scientist directly but, companies are really skeptical while offering the role of a data scientist to newcomers. Data analyst profile turns out to be a relatively easy target for a person who is looking for a start in this field.
This role demands a strong hold on data analysis, existing tools and mainly deal with data processing, summarization, and ad-hoc reporting. This profile does not generally require a strong understanding of advanced machine learning concepts or a strong mathematical background, however, a basic understanding of statistics, data wrangling and visualization is necessary.
Average Payscale (in India): Rs 4,97,000/annum
Average Payscale (in US): $ 57,000/annum
Skill-set: SPSS, SAS, Tableau, Statistics, SQL, Microsoft Excel
Data Visualization Expert
Analysis of data is just one part of the data science puzzle. Another important part which is generally overlooked by newcomers is the data visualization. Data visualization plays an integral role in a variety of tasks, from exploring new datasets to explaining the results to upper management and users. It’s an important part of the storytelling process.
Most data science teams nowadays have started having a Data Visualization expert. To play this part well, you must be well versed in visualization libraries and tools, data visualization concepts and must have a basic knowledge of statistics.
Average Payscale (in India): Rs 5,95,000/annum
Average Payscale (in US): $ 72,000/annum
Skill-set: Statistics, Tableau, Power BI, QlikView, d3.js, ggplot, Visual Art Design, Storytelling Skills
Business Analyst
Business Analyst is a professional who is domain expert in the organization and are more involved in business domain of the organization to understand its data requirements and how to store and present that data in form of data warehouses, dashboards, and reports.
Professionals at this level don’t generally perform any data analysis but must be good at SQL so as to integrate data from different sources.
Average Payscale (in India): Rs 5,84,000/annum
Average Payscale (in US): $ 67,000/annum
Skill-set: OLAP, Reporting, ETL, SQL, Business Objects Design
Big Data Engineer
Big data engineers are the professionals with the adequate skill set to set up and monitor the infrastructure required by the data scientists and machine learning engineers to execute their analytical models. Generally, they descend from a software engineering background and doesn’t necessarily need the know-how of machine learning and analytics to perform well in their roles.
Average Payscale (in India): Rs 7,84,000/annum
Average Payscale (in US): $ 90,000/annum
Skill-set: Hadoop, MapReduce, Pig, Spark, Hive, MySQL, NoSQL, SQL, Linux, Networking, MongoDB
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