The best jobs in the market right now include titles like data scientists, AI specialists, and big data engineers.

A report by IDC predicted the total data content would grow up to 44 zettabytes, an estimated 44 trillion gigabytes by the end of 2020. However, with the ongoing pandemic, it is likely possible for data to expand more than it has been predicted.

Some of the predictions made in 2014 eventually became true such as,

  • In the present day, the daily output of data is over 2.5 quintillion bytes.
  • A wide range of data science roles will be driving these massive truckloads of data. Voilà, data scientists are now needed by almost every industry.
  • An IBM report also predicts the demand for data scientists would increase by 28 percent by the end of 2020.

No wonder why the world needs a data scientist now more than ever.

But, wait. What does a data scientist exactly do?

Data science is a vast field, and the responsibilities a data scientist entails may vary from industry to industry.

Most of the tasks include experimenting with massive online experimental frameworks for developing products. All in all, data science can be used in multiple ways, not just being dependent on the industry but even different business strategies and different business goals.

Some of their tasks include

  • Gathering a large amount of data and run an analysis on it.
  • Utilizing data-driven methods to solve business problems.
  • Spotting trends and patterns from the data extracted.
  • Communicating the results with business stakeholders and IT leaders.
  • Data preparation and text analytics deployment.
  • Transforming data into visualizations that are compelling for stakeholders to understand.

Some of the skills needed by a data scientist

  • R and Python programming skills.
  • Data visualization tools such as Tableau and ggplot.
  • Data mining skills.
  • Big data Hadoop tools.
  • Machine learning and algorithms.
  • Business acumen and communication skills.

Roles and responsibilities of a data scientist

Day to day tasks can be predictable at times, however, sometimes it might be something out of the ordinary. If you have the knack for crunching data, then perhaps a data science career is perfect for you. It is never too late to start a career in the data science field. Most IT professionals have started seeking data science certificate to get entry into the world of data science.

The main task of a data science professional is to analyze data to obtain actionable insights by performing certain tasks as mentioned below.

  • Collect structured and unstructured data from multiple sources.
  • Analyze these data to find trends and patterns crucial for the organization.
  • Identify data analytics problems, one of the greatest value for any organization.
  • Work closely with unstructured data such as images and videos.
  • Clean and validate the data to find there’s accuracy, uniformity, and completeness.
  • Communicate the findings to the business leaders and stakeholders using data visualization tools.
  • Discover new methods and solutions by analyzing the data.

Often data scientists spend their time gathering data, cleaning, and transforming them into positive and actionable insights using data visualization tools and techniques. Not to mention, data cleaning is one of the major responsibilities of a data scientist. However, such responsibilities need an extensive understanding of working with data and how to use various tools and techniques such as statistics, machine learning algorithms, and programming skills. It is crucial for the data science professional to have a detailed understanding of how to debug output from the code.

Once the data is cleaned, the data science professional further converts these data into visual insights using data visualization tools. At the end of the day, data scientists are more concerned about the models they create.

What’s your pick today?

Data science as mentioned is a vast field. The data scientist role itself requires the individual to have a deep understanding of statistical and mathematical models. They seek for better ways to apply their theoretical and practical knowledge in the field of statistics and algorithms to find the best and relevant way to solve a complex problem.

On the other hand, you can also opt to become a big data engineer or a big data architect.

One of the major differences between a data scientist and a data engineer is the fact that data engineers are efficient in handling a large amount of data using their software engineering along with programming skills.