Is Data Analysis a Stressful Job? What You Need To Know

The data analyst is valued across all channels of an organization due to the need to understand and analyze information, and to state that analysis in digestible pieces. As an indispensable element of doing business, demand for experienced data analysts is at an all-time high. Therefore, the day to day workload is bound to be overflowing. 

Data analysis is a stressful job. Although there are multiple reasons, high on the list is the large volume of work, tight deadlines, and work requests from multiple sources and management levels. 

Hence, to understand the stress load a career in data analysis carries, this article defines the career, qualifications, and transitional path. Additionally, a deeper look at why this area of business is such high pressure is also discussed in this article. 

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Data Analysis as a Career Defined

Data related professionals are in critical demand due to the need to make sense of the constant stream of incoming information. Job openings are plentiful, qualified applicants are hard to find, and the demand is getting larger. Companies are much more aware of the increasing need to have employees with experience in data handling and analysis in some capacity on board.

While it is easy to understand that a data analyst needs to comprehend data, analyze it, and design various analytical models to optimize operations, it is not as evident for unrelated occupations and industries. For example, a scientist such as an epidemiologist also needs to have a data analysis skillset.

By definition, an epidemiologist researches disease patterns, therefore, also utilizes data analysis and statistical methodologies. Most employees in our digitized world would benefit from some level of data competency in their respective positions. 

A data analyst manipulates data queries and translates the results of big data to support the conclusion. Additionally, a data analyst will need to make projections for the future based on the data.

An analyst’s work can be compared to managing a fantasy football team because many people are relying on the accuracy of your predictions as to how the players will perform and basing decisions from the data you have supplied.  

Using statistical analysis to view past, current, and future predictions, communicating information, and answering questions, create stressful situations but are part of the data analysts’ work. Knowing which skills are short in supply but high in demand will help you leverage your position to transition into this career space.

Data Science

Data science has evolved in conjunction with technology because of how computer science plays an important part in processing and analyzing big data. Today, machines can be trained with a data-driven approach.  

By broad definition, data science is almost everything that pertains to data. Furthermore, it is a combination of computing power and data mining. A data scientist (DS) solves company issues by using data. The DS may need to perform all of the following skills in a small company, but responsibilities are specialized in a large company:

  • Define the problem
  • Acquire data
  • Store data, data warehousing
  • Clean and transform
  • Analyze, model, and visualize
  • Experiment
  • Determine resolution
  • Monitor resolution in production

For example, a trucking company uses data science for logistics to enable prediction of delays, find the most efficient route for delivery, and notify the customer of changed delivery times. In a big company, a large volume of data is needed at a quick velocity.

Consequently, data science supports the needs of the business to manage and gather insight from the massive amounts of data in a very quick amount of time.

Big Data

As a field of study, Big Data refers to extracts and data handling far too complex for traditional databases. Within the Big Data realm, predictive analytics techniques are used to gather historical data and statistically forecast future unknowns using data mining and modeling, among other processes.

Additionally, data translation or data visualization is used to concentrate on creating a data picture for the purpose of organizational usefulness. For example, identifying market trends, operational efficiencies, profit margins, and revenues.

Machine Learning, Data Mining, and Deep Learning

As it pertains to data science, in order to make business decisions, data insights, and relationships are studied. Due to advances in the volume and speed of computing available today, machine learning, data mining, and deep learning have evolved into the mainstream methods for delivering the needed analysis and insights. As such, this is another area that is highly valued in the data scientists toolkit. 


After broadly understanding the data science field, consider the many roles, or job titles, that relate to this profession. For instance, the following list relays a few titles, but the field is rapidly increasing, and with that, the areas of need and specialization are also growing:

  • Biostatistician
  • Business Intelligence Analyst
  • Computer Systems Engineer
  • Database Administrator
  • Database Architect
  • Database Developer
  • Data Engineer
  • Data Mining Analyst
  • Data Scientist
  • Data Warehousing Specialist
  • Economist
  • Financial Quantitative Analyst
  • Statistician
  • Systems Analyst

Additionally, at the top, as part of the executive team, the Chief Data Officer takes on the position which understands and oversees a company’s data systems, analyzes gaps, mitigates risk, and develops strategies to align with the organization’s goals. Potentially, the following are a few of the areas of expertise necessary:

  • Track potential talent shortages.
  • Ensure the availability of solid onboarding and professional development programs.
  • Assume responsibility for data privacy and security risks.
  • Control costs.
  • Communicate insights, status, and importance of data systems and information.
  • Escalate, strategize, and resolve data issues.

Data Analyst Qualifications and Career Path

A data analyst is a stepping stone to becoming a data scientist because you are gaining valuable experience in the data science environment. Your knowledge is used to determine resolutions to problems using computer systems. Also, to identify and report what is behind the numbers, discover trends and new opportunities. 

Although the importance of certain areas of expertise depends on the needs of the company, if you can apply your talent to the following list, you can fulfill the basic analytical skills as it pertains to data science.

  • Analyze the data to explain patterns in the data.
  • Clean and transform data to prepare for analysis. Thus making analysis easier. The two well-known tools are:
    • NumPy or Numerical Python, is an open-source software library in Python used for dealing with arrays.
    • Pandas is another open-source software library in Python used for manipulating time series and numerical tables.
  • Aggregate data, create reports, and dashboards.
  • Perform testing, for example, A/B testing.
  • Create and run models as a basis for determining business strategy.
  • Experimentation.
  • Report performance metrics and risks. 
  • Present the data-driven insights and discoveries.

In order to accomplish these things, a data analyst has to shift back and forth from operations to strategy. With this in mind, it is helpful to learn statistics and to be able to code. A data analyst must be a team player who is thinking about the big picture but not afraid to jump into the numbers as needed. 

Math and statistical concepts that are useful to be familiar with to become a data analyst are:

  • Confidence intervals
  • Predictive modeling
  • Quantitative methods
  • Sampling
  • Test control cells

Another area that a data analyst may touch on in some companies is predictive analysis. Data scientists develop statistical or mathematical models, while the data analyst uses those models as tools on a known set of data to predict future insights.

In a larger company, statisticians or programmers may also be involved in the process, but data analysts collect the data and are involved in presenting results. Executives rely more frequently on analytics to make informed decisions about future business goals and direction. Therefore, predictive analysis is an increasingly important part of the job. 

Mathematical models frequently used to leverage predictive analysis are regression models, clustering models, optimal estimation, linear regression, and text mining. While most of these techniques have been used in the past to look at historical trends, the significance of predicting future trends is more prevalent. 

Data scientists spend time learning how to develop new tools while the data analyst’s role is to interpret those tools and utilize what is already in existence. The scientist is a senior position and has more years on the job as well as education. An analyst aspires to be at that higher level by working and learning on the job and possibly getting additional education.

Industry-Standard Programming Software

As an Excel user, you are familiar with statistical programming as it is used to manipulate data and analyze results. While Excel is still needed and is part of a data analyst’s qualification requirements, it is not quick or powerful enough to handle huge datasets. Therefore additional items need to be added to your repertoire. Most critical of these additional skills are as follows: 

R – Programming Language

R is a popular, free, open-source programming language supported by R Foundation for Statistical Computing and used for statistical analysis. Although it is free, there are third party packages (over 9000 available), such as RStudio, built to make it easier to use. To gather more information, go to the Reddit R Programming Language forums for real-life insight and resources.

To immediately get started programming, an introduction to the basics of R programming language is demonstrated in this 2-hour video. The host walks you through everything from the installation of R, RStudio, demos, and screenshots. 

Python: Scripted Programming Language

Another industry-standard, Python is used to handle massive datasets and is a free, open source scripted programming language. While there are differences, the preference is yours and the company you work for. Although, as long as your work is accurate and timely, most companies will not care which you choose.

SQL: Structured Query Language

Structured Query Language (SQL) is used to examine data from related tables to perform analytics. Relationship databases contain subject-related tables that are connected in some way. SQL is flexible, portable, and has been in existence since the 1970s. 

If interested in learning more, view this tutorial for beginners. This video delves into installing the software to writing codes and queries, starting with a review of relational databases.

Tableau: Visualization Tool

As a data visualization software tool, Tableau is user friendly and enables you to aggregate data and weave a captivating picture together. Quickly and clearly getting the point across is crucial, especially when presenting impactful information to large groups or C-suite executives. For example, data may be used to visualize market trends, affecting which direction the company moves toward. 

In summary, Excel, SQL, R (or Python), Tableau, and the ability to communicate an influential story to your stakeholders are recommended to perform as a data analyst. But most qualifications for the job are not mandatory. However, an organization is looking for a proven record of technical qualifications and interpersonal skills. 

Accordingly, as an analyst, you are solving problems in a particular domain. Experience in other careers often encompasses transferable skills. Supplying information and providing resolutions are satisfying but challenging parts of the job.

Research an industry that you want to break into or are already involved in another capacity. Most industries see the value of data analysis employees, and to a great extent, businesses are already hiring and are reaping the rewards, including the following:

  • Enterprise Management
  • Finance
  • Information Systems
  • Insurance
  • Manufacturing
  • Professional Services
  • Scientific Services
  • Technical Services
  • Utilities

There are massive amounts of reading material that deliver information specific to your industry for the basics on becoming a data analyst. The book entitled “Data Analytics For Beginners” on Amazon provides a wide range overview. 

How to Transition Into Data Analysis?

While the path to any career is not a straight line, consider some or all of these concrete suggestions that will position you correctly:

  • Start as a budget analyst or compensation analyst.
  • Create a portfolio. 
    • College projects.
    • Personal projects.
    • Certification course projects.
    • Work or internship projects.
  • Job shadow the position you would like to move into.
  • Expand your network.
  • Get certified in a statistical programming language. For example, the University of Washington offers a certificate in Statistical Analysis With R Programming. 
  • Quickly learn new programming languages.
  • Refine critical thinking skills.
  • Confidence and integrity.
  • Develop problem-solving and common sense reasoning abilities.
  • Practice presentation skills with a powerful delivery, as shown in this 4-minute video:
  • Earn a college degree.
Bachelor of Science or Bachelor of ArtsMaster of Science
Computational and data scienceBusiness analytics
Data scienceBusiness Intelligence and data analytics
Data science and predictive analysisData analytics and policy
Data science and visualizationData analytics and engineering
StatisticsData science

Why Data Analysis Is Stressful?

A data analyst looks at the pros and cons of the business, makes queries to data to generate information and statistics, reports, and advises how to take action to course correct, if needed. At the same time, Key Performance Indicators (KPIs) must be monitored, and results communicated or escalated to the appropriate people. 

If done insufficiently, managers and colleagues turn to the data analyst team and ask why they do not have this information. To do it right, a lot of balls are in the air at the same time, including this list of performance demands are expected from the analyst:

  • Accuracy and excruciating detail are mandatory.
  • Provide service to multiple department heads or stakeholders.
  • Report to many areas of the company, including the C-suite.
  • Presentation and communication of data for decision making can put you on the spot and requires preparation and quick thinking.
  • Be aware of looming deadlines and the immediate need for output.
  • Data results may be published for public scrutiny and audits, not just internal information.
  • Work overload. If there is a shortage of qualified workers, more work will fall on your shoulders.
  • Time for professional development is needed to stay ahead of new technological changes.

How to Overcome Job Stress?

Acknowledge the pressure and take steps to improve the situation. Here are some tips for taming the stress:

  • Build a work routine.
  • Take breaks throughout the day.
  • Daily exercise.
  • Take an assessment to determine from the outset if this career is right for you. An example is Career Test from Career Explorer.
  • Learn to shut down and step away at the end of the day. 

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


After discussing what a data analysis career is, and the qualifications needed to break into the field, it is evident that there is a learning curve to becoming an expert. And even more learning and experience is needed to progress to a data scientist role. However, due to the relevance of data analytics for business decision-making now and in the future, the journey is well worth the trip. 

Although a data analyst feels a great deal of pressure from the ever-changing environment, deadlines, directives from management, and the need for accurate output, a career in data analysis pays well.

There is job security rendering it an excellent choice. By steadily building a career trajectory, you will be able to work in most industries and the chance of being in huge demand in the years to come is very strong.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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  3. Grolemund, G. (n.d.). Installing R and RStudio | hands-on programming with R. Site not found · GitHub Pages.
  4. A refresher on a/B testing. (2017, June 28). Harvard Business Review.
  5. Understanding key performance indicators (KPIs). (n.d.). Investopedia.
  6. What is big data? (2019, October 7). University of Wisconsin Data Science Degree.
  7. What is predictive analytics and why is it important? (2019, October 29). Online Business UMD.
  8. Why is data visualization important? What is important in data visualization? · Harvard data science review. (2020, January 31). Harvard Data Science Review.

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Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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