Daily Life of a Data Scientist

Although there is no “typical day” for a Data Scientist, it helps to know the sorts of tasks and procedures you will likely encounter in this career. Understanding the tasks of a Data Scientist, the work environment you can expect depending on your field of interest, and the kind of teams and workflow common in such fields will help you to determine if becoming a Data Scientist is right for you. The following sections will also compare what a day in the life of a Data Scientist might look like based on different career paths, such as what a freelance Data Scientist’s day might look like compared to that of a Data Scientist working for a corporation.

What is a Data Scientist?

Data Scientists extract meaning from raw data to detect patterns and propose solutions that meet an organization’s needs, especially the needs to compete and grow. A Data Scientist’s responsibilities include finding valuable data from data sources, developing machine learning algorithms, improving data collection procedures, cleansing and validating data integrity to ensure accuracy, and detecting patterns and solutions based on data. Data Scientists build models based on data, create data visualizations that communicate patterns and findings to stakeholders, and automate collection processes. Because data plays a critical role in the success of any organization, Data Scientists can build careers in business, technology, finance, nonprofits, and many other industries. 

Those who wish to become a Data Scientist should develop the analytical, statistical, and programming skills needed to manage and interpret raw data. These skills include understanding statistics, machine learning, and reporting tools. Aspiring Data Scientists also benefit from understanding the programming languages R, SQL, Python, Java, and C++. 

Read more about what a Data Scientist does.

Data Scientist Specializations

What are the different job titles related to Data Scientists, and what do these different jobs entail? Data Scientists may hold job titles that include the words “scientist,” “engineer,” and sometimes “analyst.” These job titles include: 

• Junior/Senior Data Scientist
• Machine Learning Engineer
• Data Engineer
• Data Science Instructor
• Big Data Analytics Consultant
• Data Analyst

Junior/senior positions typically indicate the years of experience and level of knowledge acquired by a Data Scientist. Machine Learning Engineers focus on machine learning algorithms and functionalities and should have a thorough understanding of artificial intelligence as pertains to data sets. Data Science Instructors may work in academia teaching aspiring Data Scientists the skills needed to start a new career. Consultants may work freelance or run their own businesses analyzing big data for organizations. Specializations among Data Scientists include:

•  Data mining and statistical analysis: refers to the process of analyzing large data sets to extract meaning. Data Scientists specializing in data mining use statistical analysis and predictive models to detect patterns, correlations, and trends that can be leveraged to predict outcomes within industries and organizations
• Database management and architecture: involves working with stakeholders within an organization to design the organization of its data, including the digital framework
• Machine learning engineering: uses theoretical models to feed machine learning software that amplifies that model to work on a greater scale. 
• Business intelligence and strategy: uses data analytics, visualizations, and modeling to detect patterns in data that reveal the current state of the organization and inform its future strategy. 
• Data visualization: illustrates findings within data sets in a visual manner, such as with graphs and charts.
• Operations data analysis: uses data to identify opportunities for improvement in business operations. 
• Marketing data analysis: uses data to determine the effectiveness of marketing campaigns and identify opportunities for increased effectiveness.

Read more about other job titles related to Data Scientist. 

Starting Your Day

A full-time Data Scientist with an organization will most likely work in an office environment full-time or through a hybrid schedule where some days are spent in the office and others are worked remotely. This type of Data Scientist would start the workday by preparing for the office and commuting to it. Data Scientists can work for many different types of organizations, but those working for software companies or other organizations in the technology sector can expect to work in a modern, most likely open-concept office. Freelance Data Scientists tend to work either from a home office or a co-working space, depending on preference.

9 AM:

Like many workers, Data Scientists might start their morning in the office by getting a cup of coffee, greeting coworkers, and checking email, Teams, or Slack. A Data Scientist working in an office may start the day by reviewing the schedule for the day or week, including any meetings on the books. Software companies using the Agile working method may have daily morning “standups” where teams gather to check in with one another. Each person will state what they are working on, identify any blocks that are hindering progress, and note any tasks that are or are coming their way. The team will also discuss overall goal progress. 

A freelance Data Scientist might start the day by answering client emails, checking in with clients, and reviewing opportunities for future work. 

11 AM:

What are the big projects that a Data Scientist might be working on? How do they break down their work into manageable steps? How will they interface with their team or clients?

By late morning to midday, a Data Scientist will likely be well into a current task or assignment. The exact daily tasks of a Data Scientist fluctuate, however, you can be sure that you will always be working with data in some form or fashion. You might spend time pulling, merging, and analyzing data, identifying patterns and trends, creating data visualizations such as charts or graphs, developing and testing algorithms, outlining recommendations to improve data collection processes, developing predictive models, preparing reports for stakeholders, and putting together presentations. 

You might check in with different teams depending on the task at hand. For example, if you are determining the effectiveness of marketing campaigns and examining data from marketing efforts, you might request access or information from a marketing manager, digital marketing specialist, or another member of the marketing team.

2 PM:

The afternoon may involve meetings with teams within the organization, or for freelance Data Scientists, meetings with clients. If you lead the meeting, you can expect to present your findings and recommendations to stakeholders. Materials you should prepare for such meetings include: 

• Data visualizations such as charts and graphs. Data visualizations illustrate patterns, trends, and other information in a way most stakeholders can easily digest.
• Reports. Reports gather and organize your findings to help stakeholders understand the effectiveness of past efforts, the current health of the organization, areas that can be improved, predicted future outcomes, and other opportunities. This information is key to helping stakeholders make informed decisions.
• Presentations. You may need to compile a presentation containing data visualizations, your reports, and other relevant information. Practicing public speaking can help you lead presentations effectively and engagingly.

4 PM:

In the last part of the day, a Data Scientist might review their daily checklist and prepare a to-do list for the next day. If time permits, you may also review any updates to the field of data science. Data science is at the forefront of technology, so Data Scientists must keep track of new methods and tools relevant to the job. This will help you discover ways of improving processes, leveraging new technology, and more. This includes watching for news about artificial intelligence, machine learning, and updates to programming languages such as R, Python, and Java. You may want to join a Slack channel based on these topics or even join a professional organization. Email newsletters and podcasts can also help you stay informed.

Work/Life Balance

Most Data Scientists work a standard 40-hour week. It is important to maintain healthy boundaries that contribute to a work/life balance. This is not only beneficial to your personal health but ultimately benefits the organization you work for, as well. Stressed, burned-out employees cannot put forth the same innovation and productivity as one who is well-rested and ready to tackle the challenges of the day. Data Scientists constantly solve problems, analyze complex data, and work on difficult tasks, so self-care off the clock matters for both your personal and professional benefit. 

Some standard methods of maintaining work/life balance include: 

• Not reading emails, instant messages, or other work-related communications on the evenings and weekends. Barring a true emergency, most communications can wait until office hours. 
• Getting a good night’s sleep, exercising, and eating right. Taking care of your mental health means taking care of your physical health, too.
• Spend time with friends and family. Spending 40 hours of the week working means time with friends and family is that much more precious and important.
• Dedicate time to your hobbies. Part of self-care means engaging in things you find fun and inspiring. Data Scientists have a natural curiosity, so pursuing hobbies helps to keep that curiosity and creativity alive.
• Use your vacation time. Multiple studies have shown that burnout by leaving your vacation days unused benefits no one. Employees that take a step back from work return happier, healthier, better rested, and ready to take on new challenges.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.