How does big data relate to data science and a career as a data scientist?
If you are someone who has an interest in pursuing a career in Information Technology, then I’m sure you’re already aware of the terms “big data” and “data science.” But what exactly do these terms mean and how do they relate to a career as a data scientist?
As data becomes more valuable so does the demand for highly skilled data scientists with proper data science qualifications. An article by The Economist in 2017 went as far as to say that “the world’s most valuable resource is no longer oil, but data.”
Understanding the difference between big data and data science
Data Science is the scientific approach to analysing big data and extracting key information that is used by decision-makers of companies and organisations. In other words, within big data lies the treasure and data science is the essential tool required to unearth the treasure. Of course, none of this can be possible without skilled, qualified data scientists to operate the tools.
Here’s how we can distinguish the difference between big data and data science:
- Data that contains greater variety arriving in increasing volumes and with even higher velocity (Gartner, 2001).
- Humongous volumes of data requiring tools and database programming to be organised
- Involves a combination of structured, semi-structured, and unstructured data
- Without proper and effective analysis the data is useless
- Data Science is an umbrella term that covers statistics, predictive analytics, and machine and deep learning that has evolved from the presence of big data
- Used to make sense of big data turning it into valuable business information
- Allows data scientists to inform decision-makers on appropriate business decisions
- Machine learning is an artificial intelligence tool that automates data processing and is commonly used by data scientists
How is big data related to a career in data science and analytics?
As companies around the world become more data-driven the urgent realisation to collect, preserve, and utilise the massive amounts of data is more evident than ever. This allows companies to stay ahead of their competitors and increase revenues via data science and analytics.
Here’s a breakdown of the data science process:
- Ask questions and define the business problem
- Evaluate whether the data is readily available – if not, devise a scheme for acquiring that data
- Collect and review the data – this is where the data scientists step in.
- Process the data – This is where the data is cleaned, and data scientists test the values to see if they make sense
- Explore the data (the fun part) – Data scientists will now employ algorithms to try and extract meaning from the data
- Perform the in-depth analysis – This is the stage where valuable insights are made and a solution to the business’ problems is crafted
Communication of results – Through the use of the analysis a picture is painted describing the cause of the problem and a proposed solution for the business to act upon.
I have skills in Hadoop and Spark, am I a data scientist?
Having experience with Hadoop and Spark doesn’t qualify you as a data scientist, however, it does go a long way in knowing these programs if you’re aiming to become one.
A study by Paradigma4 found that 48% of data scientists used Hadoop or Spark affirming that although a useful skill, Hadoop and Spark are not necessities. On the other hand, another survey conducted by CrowdFlower, established Apache Hadoop to be the second most important skill for a data scientist with a 49% rating.
Being proficient in both Hadoop and Spark will make your life easier as a data scientist. Experience with Hadoop and Spark is a common job requirement mentioned in many data science job descriptions; hence you will be more job-ready if you’re willing to learn these skills.
What skills and training does a data scientist have and can a big data professional work as a data scientist?
Typically, to become a Data Scientist a bachelor’s degree of four years full time in information technology, computer science, engineering or a related field is required or a master’s degree in Data or a related field.
Here are some technical skills that a data scientist will learn:
- Python coding
- Hadoop Platform
- SQL Database/Coding
- Machine Learning and Artificial Intelligence
- Data visualisation
- Apache Spark
Any big data professional can become a qualified data scientist with a certification in Data Science with the Institute of Data. Get certified in as little as 12 weeks.
How can I become a data scientist from a big data background?
For anyone, regardless of a big data background or not, the easiest way to become qualified as a data scientist is through a data science course. Although, having a background in big data is advantageous, obtaining a data science certificate with no prior programming skills is still achievable.
If you’re still unsure if a career as a data scientist is for you then feel free to gain more insight and information at a career event near you.
Curious to see where a fulfilling career in big data could take you? Talk to a career expert now.