Must have skills to become a DATA SCIENTIST
Data scientist! Doesn’t it sound too boring for ears itself?
What in the world is a data scientist & why would anyone be interested in becoming one?
Yes! This is the most common expression I have come across whenever we talk about data scientists. Some people even think data scientist are nothing but people who only keeps data from one place to another, nothing much.
Well you in for surprise now because data scientist are not actually boring people with boring jobs instead they are real creative people with vast expense of enthusiasm & creativity in their work. Data actually is their rhythm and they tune them into beautiful creations.
So let’s start with actual dictionary definition for data scientist
Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them.
A data scientist is a job title for an employee or consultant who excels at analysing data, particularly large amounts of data, to help a business gain a competitive edge.
As already mentioned the term data scientist is sometimes disparaged because it lacks specificity and can be perceived as an aggrandized synonym for data analyst.
A data scientist possesses a combination of analytic, machine learning, data mining and statistical skills as well as experience with algorithms and coding. Perhaps the most important skill a data scientist possesses, however, is the ability to explain the significance of data in a way that can be easily understood by others.
Here are some of the skills every Data scientist should possess.
- Education is the basis of today’s world, no one can disagree to that, however minimum of 88% of Data Scientists are expected to havea Master’s degree. Data science is an incredibly diverse field, with no real consensus along with varying levels of business, technical, interpersonal, communication, and domain skills.
- Folks come from academics, industry, computer science, statistics, physics, other hard sciences, engineering, architecture… and they hold PhDs, Master’s degrees, undergraduate degrees.
- Curiosity is the key ingredient required to bring out creativity in any person so intellectual curiosity is the key point to look for if you are willing to become a data scientist.
- No matter which field you choose the most important aspect remains the basic knowledge of the domain, analytically speaking.
- Even when you are a speaker or a story teller the domain knowledge will let you rapture the attention of your audience.
- Now we can take a purely technical role: if you are developing algorithms, pipelines, or workflows for an organization, without a solid understanding of the fundamentals of the industry and the goals of the firm, you won’t be able to appropriately leverage your technical abilities to make a difference in the long run.
- And let’s face it: The way you put it is the essence of data scientist.
- Well in today’s global market who doesn’t need good communication skill. If you can’t express it in speech how good can you put in words
- The “data scientist must be able to summon the needs of technicians in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately.” All in all a data scientist must make a package deal
- If you want to maintain a more technical role permanently, plan to keep those technical skills in tip-top shape.
- So keep in your mind the day you laid hands on any work start its planning the outcome.
- And it doesn’t stop at “R or Python?”
- All sorts of languages and libraries are useful for data science. Of note, Java and Scala have their place in big data processing, thanks to their prevalence in the ecosystems which grew up around the popular frameworks.
- A lot of low level coding is done in C++, especially for algorithm development, thanks to the speed and control associated with being closer to the metal. Tools are just that; they aren’t meant to become ideological expression of dogma we associate with to form our identities.
Data Mining Skills
- This refers to both theoretical and practical skills.
- You don’t want someone with no idea of how to write even a basic c level program if you are going to pay him to develop vast programs for your new project.
- Basic as well as some higher level knowledge in your domain is a must.
Big Data Processing Platforms
- Datais growing, and as a data scientist you have to understand that data processing frameworks are a part of the datascience landscape; having an understanding of these frameworks is vital.
- As in other fields be it technical or non-tech you will always need a platform to execute your work & as such data scientist also need a platform to actually formulate their work.
Structured Data (SQL)
- Structured == Relational == SQL
- At the very least, it is expected that data scientists can write and execute non-trivial SQL scripts against stored data.
Curiosity about Data and Passion for Domain:
- If you are not passionate about the domain/business and curious about data then it is unlikely that you will succeed in a data scientist role.
- Well it is a well-known proverb “ too much information is harmful” but we guess this is not applicable to data scientist as you really need to gobble up any information given to you and often have to add to them.
- Too many things is much worse condition than not having anything at all therefore ability to scoop information out of partners and customers, even from the unwilling ones is extremely important.
- The data you are looking for may not be sitting in one single place. You may have to beg, borrow, steal and do whatever it takes to get the data.
- Visualization is the basic nature of humans the ones who lacks them lacks the zeal for living.
- Visualization is a must attribute of a data scientist as his ability to look into volumes of data sorting & organizing it make a profound impact on the person.
Specific situations of data scientist
- Extraction of huge volumes of data from multiple internal and external sources.
- Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use .
- Situation where out of volumes of data all you need is 1 page article and you need to sort them out.
- Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
- Devise data-driven solutions to the most pressing challenges
- Invent new algorithms to solve problems and build new tools to automate work
- Communicate predictions and findings to management and IT departments through effective data visualizations and reports