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A Day in the Life of a Data Scientist

Table of Contents

  • [toc headings="h2" title="Table of Contents"] For people who are seeking career opportunities with substantial future growth potential, data science is a smart choice. The Bureau of Labor Statistics predicts a 35% growth rate through 2032, with an average of 17,700 new jobs available each year during that decade.  Of course, you don't necessarily want to choose a career path simply based on the numbers. For students, career switchers, and others considering a career in data science, understanding the typical responsibilities, day-to-day activities, work environment of this role can help you decide if this type of job is a good fit. 

  • What do data scientists do?

  • The short answer to this question is that data scientists use analytics and problem-solving skills to gain insights from large volumes of data. This information is then used by their company in a variety of ways, from development of new business strategy to improving their marketing to customers.  One of the problems with pinpointing what data scientists do is that the field is quite varied. Organizations across industries have a need for data science skills but they don't all use the insight gained from those techniques for the same things. The exact approach these professionals use to create data visualizations and models to analyze a dataset will vary depending on the project and what results the organization hopes to achieve.  In a big-picture sense, the work of a data scientist starts with research. Before they can conduct an analysis, they need to determine what data sources are available and gather the relevant information from databases, web-scraping tools, and other techniques. Once they've collected the right data, the next step is to categorize and "clean" this raw data into structured, readable sets.  The steps a data scientist will take from there depends on what they plan to do with this data. Frequently, they will develop machine learning algorithms to model the data. These models must be tested and validated before they can be used to derive data-driven insights valuable for business decision-making. The final responsibility of a data scientist in many cases is to create presentations that convey their findings to leadership and other stakeholders.  While there are a variety of approaches to gather, process, and analyze data, there are some core data scientist skills that professionals need across the industry. Creating AI and machine learning algorithms requires coding knowledge, usually in Python or R. Skills in analysis and statistics are necessary during the analysis stage. Not all of a data scientist's work is in the realm of technology, however. Success in this role also requires effective communication to convey insights to the leaders who use them to make decisions. The truth is, advancement in this field requires a combination of technical and soft skills, and this can be one of the challenges of building a data science career. 

  • Where do data scientists work?

  • Most people think of data science as a tech industry role, and you certainly will find these professionals in technology businesses ranging from innovative start-ups to large international corporations. While computer systems design is the largest employment segment for data scientists, though, it only accounts for roughly 13% of data science jobs, according to the BLS. The truth is, data science helps organizations identify trends, meet customer demands, and drive innovation in an array of fields. Rounding out the top five employment categories are insurance companies, enterprise management, technical consulting, and scientific research. Government agencies, manufacturers, logistics firms, healthcare institutions, and retailers also employ data science teams, and that list is far from comprehensive.  Regardless of their industry, data scientists do tend to share a similar work environment. They typically work in an office setting, although remote positions are becoming increasingly popular, as well, and the flexibility offered to data scientists is one appealing part of the job for many. While these roles are typically full-time and may involve long hours, they normally offer a slower pace and lower stress than other high-paying tech roles.

  • Typical day-to-day tasks and workflow of a data scientist

  • The day-to-day work of a data scientist depends on their specific role, the projects they work on, and the size of the data analysis team. Let's take a closer look at the most common tasks that fill a typical data scientist's time. 

  • Gathering and processing data

  • The collection and cleaning of data is often the most time-consuming part of a data scientist's work, and is often where entry-level professionals especially spend the bulk of their time. In a Forbes survey of data scientists, respondents reported spending nearly 80% of their time on this process, with 19% devoted to collecting data sets and 60% cleaning and organizing data.  Granted, not every data science job will hold to these specific ratios. This trend is shifting, as well, thanks to the expansion of automation tools to take over some of this tedious work. Even so, data scientists working today and in the near future will still likely spend a good portion of their time collecting and processing raw data. 

  • Writing code and algorithms

  • Data scientists work with large volumes of data. Identifying patterns and usable insights from these massive datasets would be an overwhelming task for an unaided human. This is where programming and developing algorithms come into play.  As the data science field evolves, there are more existing algorithms for professionals to draw from when they need to analyze a dataset. Because of this, the types of data being worked with, and the insights someone wants to derive from them, will determine the extent of the coding involved. Some data scientists don't write any new code, or only utilize this skill to refine and improve existing systems to deliver better solutions. 

  • Data modeling and visualization

  • Data modeling is the process of creating diagrams that represent a data set and show how these categories of data relate to each other. This helps to identify the underlying structure of the data and helps to ensure it's stored effectively and represented accurately.  Data visualization takes this data and puts it into a visual form using charts, graphs, and tables. Creating this kind of image representation makes it easier to identify patterns and trends that could be overlooked when the data is presented as just text.  While they are separate processes, data modeling and visualization are closely related tasks that are often performed at similar stages of a project. In both cases, the end goal is to bring order to raw, unstructured data that makes it easier to make sense of. 

  • Data analysis

  • Modeling and visualizing data allows data scientists to observe high-level correlations, trends, and characteristics. Most of the time, though, it's necessary to dig a bit deeper to derive truly valuable insights and predictions from it. This is where data analysis comes into play, and is the stage of a data scientist's work where problem-solving, creativity, and business acumen are the most useful.  This analysis can be conducted in a variety of ways. Machine learning often comes into play in this stage of the process, as well, along with statistical analysis, predictive modeling, and similar techniques. 

  • Meetings and presentations

  • No matter how advanced the strategies to analyze a data set, the insights they derive are useless if the data scientist can't turn them into something a layman can understand. This is the stage where communication skills are absolutely vital. Even if the data scientist isn't personally presenting their findings to clients or leadership, they need to convert them into a comprehensible report or presentation that shows how the data applies to the real world.  How a data scientist's time breaks down between these big-picture areas will depend on several factors. Their experience and job title will make a difference here. Generally, more senior data scientists will spend more of their time analyzing data and presenting their findings, while more junior team members handle the bulk of the collection and cleaning. The size of the company and team will play a role, as well. Someone working for a startup or other small team will be more likely to handle all the stages of the process themselves, for example. Ultimately, this gets back to what is both an advantage and a challenge of careers in data science: the variety in the field. As professionals grow their data science career, it's important to focus on opportunities that utilize their strongest skill sets and areas of interest, increasing their odds of devoting their time to work they find meaningful.

  • FAQs about the data science career

  • How much does a data scientist make?

  • Data science is among the most consistently high-paying technology sectors, and professionals can expect to earn an above-average salary at all stages of their career. The average base salary for data scientists is $124,000 per year, according to Indeed. Even at the entry level, with one year or less of experience, it is common to earn $80,000 to $100,000 a year in this profession. 

  • What is the difference between a data scientist and a data analyst?

  • These are related fields, with both primarily focused on using data to improve decision-making. The main difference between them comes down to the types of insights they derive from this data. Data analytics typically focuses on analyzing data from the past to inform business strategy and decisions in the present. Data science, on the other hand, has a forward-looking focus. They aim to predict future behavior, trends, or outcomes based on both present and historical data.  This difference in objectives also affects the skills required for the role. Data analysts generally don't need to have programming knowledge because they don't develop machine learning algorithms or other programs. Instead, they use existing tools to transform and analyze raw data from a data lake or warehouse. Data scientists are also more likely to be involved with developing and optimizing the organization's data infrastructure, giving them a higher-level role within the company's overall data approach. 

  • How does someone become a data scientist?

  • There are a variety of paths that can be taken to start a career in this field. Like most others, though, it typically starts with education. While only 33% of data science job postings on LinkedIn require a degree specifically in data science, a bachelor's degree in some related field is expected, with top options including statistics, computer science, and engineering. Advanced degrees are also common in this sector, with 34% earning a master's degree and 13% holding a doctorate.  Those who start with a degree in a related field may want to expand their skill sets before looking for opportunities. There are a number of certifications and bootcamps to target relevant skill sets like programming languages, data visualization, and machine learning.  Most employers look for data scientists who have some relevant experience along with education, which can make it a challenge to break into the data science field. Many people take their first role in a related area, for instance as a data engineer, business intelligence analyst, or data analyst. This professional experience working with data is often the last piece of the puzzle, demonstrating your value to potential employers as a data scientist with their organization.

For people who are seeking career opportunities with substantial future growth potential, data science is a smart choice. The Bureau of Labor Statistics predicts a 35% growth rate through 2032, with an average of 17,700 new jobs available each year during that decade. 

Of course, you don’t necessarily want to choose a career path simply based on the numbers. For students, career switchers, and others considering a career in data science, understanding the typical responsibilities, day-to-day activities, work environment of this role can help you decide if this type of job is a good fit. 

What do data scientists do?

The short answer to this question is that data scientists use analytics and problem-solving skills to gain insights from large volumes of data. This information is then used by their company in a variety of ways, from development of new business strategy to improving their marketing to customers. 

One of the problems with pinpointing what data scientists do is that the field is quite varied. Organizations across industries have a need for data science skills but they don’t all use the insight gained from those techniques for the same things. The exact approach these professionals use to create data visualizations and models to analyze a dataset will vary depending on the project and what results the organization hopes to achieve. 

In a big-picture sense, the work of a data scientist starts with research. Before they can conduct an analysis, they need to determine what data sources are available and gather the relevant information from databases, web-scraping tools, and other techniques. Once they’ve collected the right data, the next step is to categorize and “clean” this raw data into structured, readable sets. 

The steps a data scientist will take from there depends on what they plan to do with this data. Frequently, they will develop machine learning algorithms to model the data. These models must be tested and validated before they can be used to derive data-driven insights valuable for business decision-making. The final responsibility of a data scientist in many cases is to create presentations that convey their findings to leadership and other stakeholders. 

While there are a variety of approaches to gather, process, and analyze data, there are some core data scientist skills that professionals need across the industry. Creating AI and machine learning algorithms requires coding knowledge, usually in Python or R. Skills in analysis and statistics are necessary during the analysis stage. Not all of a data scientist’s work is in the realm of technology, however. Success in this role also requires effective communication to convey insights to the leaders who use them to make decisions. The truth is, advancement in this field requires a combination of technical and soft skills, and this can be one of the challenges of building a data science career. 

Where do data scientists work?

Most people think of data science as a tech industry role, and you certainly will find these professionals in technology businesses ranging from innovative start-ups to large international corporations. While computer systems design is the largest employment segment for data scientists, though, it only accounts for roughly 13% of data science jobs, according to the BLS.

The truth is, data science helps organizations identify trends, meet customer demands, and drive innovation in an array of fields. Rounding out the top five employment categories are insurance companies, enterprise management, technical consulting, and scientific research. Government agencies, manufacturers, logistics firms, healthcare institutions, and retailers also employ data science teams, and that list is far from comprehensive. 

Regardless of their industry, data scientists do tend to share a similar work environment. They typically work in an office setting, although remote positions are becoming increasingly popular, as well, and the flexibility offered to data scientists is one appealing part of the job for many. While these roles are typically full-time and may involve long hours, they normally offer a slower pace and lower stress than other high-paying tech roles.

Typical day-to-day tasks and workflow of a data scientist

The day-to-day work of a data scientist depends on their specific role, the projects they work on, and the size of the data analysis team. Let’s take a closer look at the most common tasks that fill a typical data scientist’s time. 

Gathering and processing data

The collection and cleaning of data is often the most time-consuming part of a data scientist’s work, and is often where entry-level professionals especially spend the bulk of their time. In a Forbes survey of data scientists, respondents reported spending nearly 80% of their time on this process, with 19% devoted to collecting data sets and 60% cleaning and organizing data. 

Granted, not every data science job will hold to these specific ratios. This trend is shifting, as well, thanks to the expansion of automation tools to take over some of this tedious work. Even so, data scientists working today and in the near future will still likely spend a good portion of their time collecting and processing raw data. 

Writing code and algorithms

Data scientists work with large volumes of data. Identifying patterns and usable insights from these massive datasets would be an overwhelming task for an unaided human. This is where programming and developing algorithms come into play. 

As the data science field evolves, there are more existing algorithms for professionals to draw from when they need to analyze a dataset. Because of this, the types of data being worked with, and the insights someone wants to derive from them, will determine the extent of the coding involved. Some data scientists don’t write any new code, or only utilize this skill to refine and improve existing systems to deliver better solutions. 

Data modeling and visualization

Data modeling is the process of creating diagrams that represent a data set and show how these categories of data relate to each other. This helps to identify the underlying structure of the data and helps to ensure it’s stored effectively and represented accurately. 

Data visualization takes this data and puts it into a visual form using charts, graphs, and tables. Creating this kind of image representation makes it easier to identify patterns and trends that could be overlooked when the data is presented as just text. 

While they are separate processes, data modeling and visualization are closely related tasks that are often performed at similar stages of a project. In both cases, the end goal is to bring order to raw, unstructured data that makes it easier to make sense of. 

Data analysis

Modeling and visualizing data allows data scientists to observe high-level correlations, trends, and characteristics. Most of the time, though, it’s necessary to dig a bit deeper to derive truly valuable insights and predictions from it. This is where data analysis comes into play, and is the stage of a data scientist’s work where problem-solving, creativity, and business acumen are the most useful. 

This analysis can be conducted in a variety of ways. Machine learning often comes into play in this stage of the process, as well, along with statistical analysis, predictive modeling, and similar techniques. 

Meetings and presentations

No matter how advanced the strategies to analyze a data set, the insights they derive are useless if the data scientist can’t turn them into something a layman can understand. This is the stage where communication skills are absolutely vital. Even if the data scientist isn’t personally presenting their findings to clients or leadership, they need to convert them into a comprehensible report or presentation that shows how the data applies to the real world. 

How a data scientist’s time breaks down between these big-picture areas will depend on several factors. Their experience and job title will make a difference here. Generally, more senior data scientists will spend more of their time analyzing data and presenting their findings, while more junior team members handle the bulk of the collection and cleaning. The size of the company and team will play a role, as well. Someone working for a startup or other small team will be more likely to handle all the stages of the process themselves, for example.

Ultimately, this gets back to what is both an advantage and a challenge of careers in data science: the variety in the field. As professionals grow their data science career, it’s important to focus on opportunities that utilize their strongest skill sets and areas of interest, increasing their odds of devoting their time to work they find meaningful.

FAQs about the data science career

How much does a data scientist make?

Data science is among the most consistently high-paying technology sectors, and professionals can expect to earn an above-average salary at all stages of their career. The average base salary for data scientists is $124,000 per year, according to Indeed. Even at the entry level, with one year or less of experience, it is common to earn $80,000 to $100,000 a year in this profession. 

What is the difference between a data scientist and a data analyst?

These are related fields, with both primarily focused on using data to improve decision-making. The main difference between them comes down to the types of insights they derive from this data. Data analytics typically focuses on analyzing data from the past to inform business strategy and decisions in the present. Data science, on the other hand, has a forward-looking focus. They aim to predict future behavior, trends, or outcomes based on both present and historical data. 

This difference in objectives also affects the skills required for the role. Data analysts generally don’t need to have programming knowledge because they don’t develop machine learning algorithms or other programs. Instead, they use existing tools to transform and analyze raw data from a data lake or warehouse. Data scientists are also more likely to be involved with developing and optimizing the organization’s data infrastructure, giving them a higher-level role within the company’s overall data approach. 

How does someone become a data scientist?

There are a variety of paths that can be taken to start a career in this field. Like most others, though, it typically starts with education. While only 33% of data science job postings on LinkedIn require a degree specifically in data science, a bachelor’s degree in some related field is expected, with top options including statistics, computer science, and engineering. Advanced degrees are also common in this sector, with 34% earning a master’s degree and 13% holding a doctorate. 

Those who start with a degree in a related field may want to expand their skill sets before looking for opportunities. There are a number of certifications and bootcamps to target relevant skill sets like programming languages, data visualization, and machine learning. 

Most employers look for data scientists who have some relevant experience along with education, which can make it a challenge to break into the data science field. Many people take their first role in a related area, for instance as a data engineer, business intelligence analyst, or data analyst. This professional experience working with data is often the last piece of the puzzle, demonstrating your value to potential employers as a data scientist with their organization.