Difference Between A Data Scientist and Statistician

The growth of data has been exponential. According to an IBM report, 2.5 quintillion bytes of data are created per day. This has created a new class of professionals — data scientists. The question is, is data science another ‘hot’ job or a new form of science? In the Hollywood movie 21, six students, brilliant with numbers, make money at the blackjack tables of Las Vegas casinos by using numbers, codes, and hand gestures. Can we call them data scientists?

In this blog, we are going to understand the difference between data scientists and a statistician. This will help us in understanding the subtle differences between the two!

Who is a Data Scientist?
A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician. Data scientists generally analyze big data or data repositories that are maintained throughout an organization or website’s existence but are of virtually no use as far strategic or monetary benefit is concerned. Data scientists are equipped with statistical models and analyze past and current data from such data stores to derive recommendations and suggestions for optimal business decision making.
Data scientists are mainly part of the marketing and planning process to identify useful insights and derive statistical data for planning, executing and monitoring result-driven marketing strategies.

Who is a Statistician?
Statisticians collect data and analyze it, looking for patterns that explain behaviour or describe the world as it is. They design and build models using data. The models can be used to help understand the world and to make predictions.
It is proven that the celebration of birthdays is healthy. Statistics show that those people who celebrate the most birthdays become the oldest.
A statistician develops and applies statistical or mathematical theories to obtain and summarize useful information to help solve real-world problems. They collect and analyze data and use it in several industries, such as engineering, science, and business. The numerical data collected helps companies or clients understand quantitative data and track or predict potential trends that can be beneficial in making business decisions.

Data Scientist
1. Education
Data scientists are highly educated — 88% have at least a Master’s degree and 46% have PhDs — and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.
2. R Programming
In-depth knowledge of at least one of these analytical tools, for data science R is generally preferred. R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 per cent of data scientists are using R to solve statistical problems. However, R has a steep learning curve.
3. Python Coding
Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists.
4. Hadoop Platform
Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.
5. SQL Database/Coding
You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate and work on data. It gives you insights when you use it to query a database. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries.
7. Machine Learning and AI
A large number of data scientists are not proficient in machine learning areas and techniques.