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Data science is a booming career, but it is also extremely challenging. It can be a great career choice for the right person, but make sure you are aware of what you are getting into.

The work of a data scientist is stressful. The vast data workload, strict deadlines, and the pressure from management to find a viable solution from data are some things that make it stressful—not forgetting the mental strain and emotional involvement in the whole data analysis process.

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Is Data Science a Safe Career?

When thinking of a career path to follow, it’s essential to consider the safety of the job and the security that comes with it. You don’t want to pursue a dying career. To understand the future of data science and determine how safe it is, we’ll need to look at how it has evolved and the role of technology in it today.

In the late ’90s and early 2000s, companies would collect big data everywhere and store it in the company’s data warehouse.

There wasn’t much done with it since no one can make sense of it, and if there were, the personnel would be limited to do some basic reporting from the data.

Business decisions back then depended mainly on gut-feeling and assumptions.

The advancement in technology introduced machine learning, and by 2010, ML algorithms were used to aggregate data from any computer. This development opened a door for data science, but it was thought of as a job for geniuses who knew strange algorithms.

Today, data science is a universal career anyone can pursue. Companies are coming to terms with the importance of data-based solutions for their businesses, and data science provides a way to make better decisions.

A company or business will likely to feel confident in a decision if there are numbers backing it.

The latest trend in data science has brought in a new hype, Artificial Intelligence (AI), and this is where the scare is. Everything is following the automation path, and soon, machine learning will only need a few clicks, and algorithms will run and analyze the data.

But that’s all to it, and that’s about 10% of what a data scientist does.

A data scientist manipulates data through data analysis, data wrangling, data janitorial, and data engineering, interfaces with business.

Then, through persuading and selling proposals derived from this data, use it to solve problems by asking relevant questions, researching, finding the problem, and creating a workable solution.

To remain relevant in the data science niche, you must up your skills, especially those in demand. Some of these skills are:

  • Business sense – Develop an intuition that helps you make sense of data by asking the right questions, relating data, and identifying trends that help solve a problem. As a problem-solver, your communications skills must be top-notch so that you can guide other non-technical team members in a precise data-based decision-making process.
  • Statistical fundamentals – AI cannot realize when absurd assumptions are made in the data analysis process. That’s why solid statistical knowledge will remain crucial.
  • Computing and programming – Even though machine learning can be automated, someone with deep machine learning theoretical and practical knowledge will be required to program and execute the commands.
  • Data analysis – Maintain a high proficiency in data analysis programming software languages and a competent knowledge of algorithms. Curiosity will keep you learning and keeping ahead with what the industry may require in the future.

Companies are now focusing on building data analytics teams to help analyze historical data for better future decisions; therefore, the demand for more data scientists is rising, and more job openings are showing up.

If you’re worried about not getting a job as a data scientist, you can sweep that under the carpet.

Generally, data science is a safe career in terms of job security. Automation should be the least of your concerns. There’s a fast-rising demand in new roles for data scientists, even for those at entry-level, yet the supply is barely enough.

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Is Data Science a Stressful Job?

A data scientist’s job is demanding, and mistakes are intolerable. Company managers and stakeholders use Key Performance Indicators to check the quality of the work done by data analysts and keep them on toes with tight deadlines.

The team of data analysts is tasked with the responsibility to churn the data, make queries, find solutions to any problems the business is facing, and communicate results and advise the appropriate people who implement corrective actions where needed.

The stress on this job may come from the many expectations tagged with the responsibilities of a data scientist and the performance demands for the job. Some of these expectations include:

1. The information in their results must be excruciatingly detailed and accurate. One tiny error in the data can cause inconsistency in the data analysis and give a false result.

2. A data scientist is expected to be versatile and work effectively across multiple departments providing services in this line.

3. The urgency of the job calls for tight deadlines, so a data scientist is always looking over the shoulder for looming deadlines and the short time limits.

4. The advancing data science technology and improvement of software tools for data analysis require that a data scientist find time to self-educate and develop professionally to stay updated with new technological changes. Professional development will keep a data scientist relevant in the data science field, even technological advancements.

5. There is a huge workload in companies, yet skilled data scientists are scarce. Therefore, the few qualified data scientists in the field have to shoulder the heavy workload and work round the clock to meet the demand.

6. A data scientist is expected to deliver and present their findings and proposals to a non-technical team, a task that requires adequate preparation and fast thinking to answer any questions that may arise.

7. Some algorithms may not work for some kinds of data, especially when dealing with huge unstructured data. So, a data scientist is tasked with the responsibility to troubleshoot, debug, investigate, and, if need be, create new algorithms to handle the data.

This can be very stressful as it will involve long hours of work with no guarantee of a convenient solution.

8. An unhealthy work environment and an uncooperative team can be a source of stress tool, considering that data science is a job that requires teamwork.

Imagine a team member using old data to prepare a report that requires updated data; that produces low-quality results and undermines team effort.

To deal with all this stress and enjoy an adventurous data science career, a data scientist can employ some of these tips:

Create a favorable work schedule – It’ll help you know when to work and when to rest so that you don’t overwork yourself.

Divide your work with timed breaks through the day – The breaks will give you time to walk around and stretch, thus relieving physical body tension.

Exercise – As the job involves sitting for long hours facing the computer, daily exercise will keep the body rejuvenated, and the brain will relax too.

Assess yourself depending on the work output and the effort put in – Constant assessment will help you determine your progress and the improvements you need to make to achieve your set goals.

Take time and deflate – You have to intentionally shut down and stay away from work-related things at the end of the day. Doing something you love like cycling or listening to music will play a massive role in taking away the day’s stress.

When working in a team, explain the importance of quality data, setting out the team goals, and the expected results. A motivated team performs better than one under pressure.


Every job has its measure of stress, and data science is no exception. Learning to manage it and work through the learning curve will make it a better experience for you.

Although job security for a data scientist is in question, progressive-leaning to become an expert will set you aside and keep your career in a forward trajectory.