The question, What does a data scientist actually do all day? or How is the daily life of a data scientist? is perhaps one of the most often asked by anyone studying data science or hoping to become one.
Comprehending the daily tasks might provide you with important information about your ambitions and whether your career really fits with your interests and objectives.
An entire day in the life of a data scientist will be covered in this comprehensive article, including the morning routines of a data scientist and evening reflections. If you’re interested in a career in technology, healthcare, finance, or entertainment, this article will give you a good idea of what to expect.
This article will give you a real-world data science experience and improve your confidence in your chosen career.
A Day In The Life Of A Data Scientist
Here, we will discuss what a data scientist does from morning to evening, step by step.
8:00 AM to 8:30 AM – Starting The Day
Data scientists usually work in technology-driven companies that provide them with a flexible environment. The morning routine of a data scientist starts at 8:00 AM to 8:30 AM, with checking emails, reviewing Slack messages, and planning the day.
Some individuals check the overnight performance of their data science models, scan dashboards for anomalies, and prioritize the tasks that they are going to do throughout the day while enjoying their morning coffee. If they get some free time in the morning, they listen to data science podcasts, their favourite music, or read interesting books.
9:00 AM – Reach At Work
After completing the morning routine at home and breakfast, a data scientist heads to the workplace. He/she heads to the desk with a cup of coffee and reviews the progress of ongoing projects. The data scientist checks if there have been any issues or blockers during the night that need to be solved.
9:30 AM to 11:00 AM – Daily Stand-Up Meetings
Data science projects progress through teamwork, and it is crucial for teammates to meet daily to discuss their progress.
The data scientist joins the stand-up meeting of the team with data engineers, analysts, product managers, stakeholders, and other team members. They share their work progress, what they are doing today, and what challenges they are facing with each other.
They plan their next steps, how to deal with challenges and complete projects before deadlines. Such meetings can last up to 2 to 3 hours, depending on the agenda.
Most of the time, data scientists spend coding and meeting with teammates and stakeholders to plan and share ideas.
11:30 AM to 1:00 PM – Working With Data (exploring and processing data sets)
After meetings, the data scientist heads to work and works with data depending on the project requirements.
As per the demand of the project, the data scientist may clean large and messy datasets, draw data from databases or cloud storage with SQL queries, merge and join data from various data sources, create insightful variables from raw data that boost the model performance, design visualization models to perceive relationships and patterns in data, etc.
Bad data can lead to bad insights and affect the final outcome. So, data scientists spend a lot of time exploring and preparing data.
1:00 PM – Lunch
Typically, lunch timings in most companies start at 12:30 onwards. However, a data scientist goes for lunch when he feels that he is progressing toward the end result of the project. The data scientist does his lunch between 1:00 PM and 2:00 PM.
It is not to mention that data scientists are curious to learn new things, so they might discuss the progress of the project with their colleagues at the lunch table or learn new skills from online tutorials.
2:00 PM to 5:00 PM – Developing, Experimenting, And Testing Models
After lunch, the data scientist gets enough time to focus on the work, and most of this time goes into coding and collaborative problem-solving.
This includes tasks like data gathering, data preparation, feature engineering, machine learning model building, reviewing code via GitHub, running A/B tests for product features, brainstorming feature ideas with a product manager, writing and documenting code, optimizing model performance, preparing reports, building dashboards, etc.
Contingent upon the demand of the project, the data scientist may do any of these tasks in the afternoon. Also, the data scientist fixes any of the problems if he finds them during these tasks.
5:00 PM to 6:00 PM – The Code Is Reviewed By The Team
As the day winds up, the data scientist prepares to present the findings and changes made in the code to teammates for review and seeks help from other data scientists in the team when he faces any challenges in the code.
Individuals in the team review each other’s code on GitHub, tackle problems collaboratively, share the progress of the project with stakeholders, and create documentation of the progress.
6:00 PM – The End Of The Restless Day
The data scientist’s task at the workplace ends at 6:00 PM, and he comes home. However, if there is any event, meeting, or pending task in the company, he must present there for that.
After returning home, the data scientist spends some time learning new trends and technologies in data science to keep himself updated. Then, he has his dinner and goes to sleep.
Variations in the daily routines of a data scientist
As illustrated above, a typical day for a data scientist includes multiple meetings, lots of coding, and several challenges. However, this is not the same for every data scientist in every industry or company.
Yes, a data scientist’s role is different in different industries or companies. This is usually determined by the size and the structure of the team. Here is what data scientists do in different industries.
Data Scientists In Healthcare
In the healthcare industry, data scientists prepare and analyse sensitive patient data to build models that provide a personalized user experience. Also, they ensure that they comply with HIPAA regulations while handling patient data.
Data Scientists In Gaming Company
The role of data scientists in the gaming industry is to analyze in-game behaviour and build models to provide a personalized experience to users. Also, they keep an eye on anomalies and fraudulent behaviour.
Data Scientists in E-commerce
Data scientists analyze customer behaviour on product pages, detect the reasons for the bounce back of users, optimise product pricing, and build recommendation engines to enhance the customer experience.
Data Scientists In Finance
In finance, data scientists help prevent fraudulent activities. They analyze customer behaviour and transactions to detect risks. Risk modelling, time series forecasting, and fraud detection are prominent tasks of data scientists.
What tasks do data scientists do?
Recognizing the problem to solve
The first step of the data science pipeline is understanding the business problems of business owners or stakeholders. It is crucial for a data scientist to understand the business problem from the owner’s point of view in order to make the appropriate resolution for it.
This involves asking the right questions to understand the business requirements, objectives of stakeholders, and the challenges they are facing. By understanding the business model from stakeholders and combining it with data and technical knowledge, data scientists can deliver correct insights and help in making error-free business decisions.
Collecting raw data for analysis to solve the problem
After knowing the business problem, the next task is to collect the required data from various data sources. This includes identifying data sources and gathering relevant data in one place that will help solve the business problem.
If the company can provide the required data, it will ease the task of the data scientist. However, if the available data is not enough for analysis, the data scientist may collect new data through customer feedback, surveys, experiments, interviews, APIs, observations or auto data collection systems like website cookies.
After collecting enough data for analysis, a data scientist cleans and organizes data to make applicable corrections and spot duplicate records. This is a time-consuming process, so data scientists spend a lot of time acquiring correct data for analysis.
Deciding the right technique to solve the data science problem
After understanding the problem and gathering enough data for analysis, the data scientist looks for appropriate methods to find solutions. Here, the challenge is to find the most effective method out of the best methods available.
A data scientist has to decide which method to use to address a particular data science problem. Here, I am listing some of the top algorithmic techniques used for data science problems.
Two-class classification technique
This method is used when a data science problem has two possible answers.
Multi-class classification technique
This technique is used when a data science problem has multiple possible answers.
Reinforcement learning algorithms
When the results of a data science problem can’t be predicted, a data scientist chooses the reinforcement learning algorithms method to solve it. This technique helps figure out the possible solutions for the given data science problem.
Regression
The regression technique is used for questions with real-valued answers.
Clustering
The clustering technique is used when the data points have to be classified into specific groups to answer the questions.
Thoroughly analyse the data to find insights, patterns and relations
The next step is to thoroughly analyze the data and obtain valuable insights that will help in making business decisions. The data scientist has to select the appropriate method to answer the questions and use the right tools to extract valuable information from huge datasets.
They can use open-source data science tools and libraries using Python and R languages to analyse the data and gather information.
A data scientist can also try machine learning methods to analyse data and see which one is performing better. This includes building a machine learning model, validating it against the collected data, performing statistical analysis, perceiving the results using various data visualization tools, and comparing the results of this technique with the results of other data analysis techniques.
Convey the findings to stakeholders
The final step of a data science pipeline is to present the findings to the stakeholders in an understandable way and communicate the next actions to be taken based on the results.
Data scientists use various data visualization tools like Tableau, Matplotlib, QlikView, Power BI, etc., to create explicit presentations to let stakeholders understand the outcomes of the data science problem easily.
What skills or tools are used by data scientists daily?
As a data science student, you might wonder what tools or skills data scientists use daily. Here is a list of tools and their purpose in the field of data science.
| Tools/Skills | Tools/Skills |
|---|---|
| Python | Scripting, model building, data manipulation |
| R | Statistical computing |
| SQL | Querying and data extraction |
| Scikit-Learn | Machine learning model building |
| Tableau/Power BI | Data visualization and storytelling |
| Git | Version control |
| Docker | Environment management and deployment |
| Jupyter Notebooks | Prototyping and documentation |
| Numpy/Pandas | Data manipulation |
Related: The Data Science Toolkit: 12 Most Used Data Science Tools Every Data Scientist Needs to Know
Pros and cons of being a data scientist
For a better understanding, let’s have a look at the pros and cons of being a data scientist.
Pros
- High demand in companies and higher salary packages
- A data scientist job role is in demand across industries
- High growth potential
- Improved intellectual
Cons
- May face failure in tough data science projects
- Repetitive tasks like data cleaning and preparation
- Pressure from stakeholders
Final Thoughts – Is This The Right Career For You?
Becoming a data scientist can be the perfect career choice for you if you like working with data, solving problems, and telling stories. Even if the path might seem overwhelming, keep in mind that everyone begins somewhere.
As a student or future professional, concentrate on developing a solid foundation in communication, coding, and statistics. Engage in projects, enter Kaggle contests, and maintain your curiosity.
You might be closer than you realize to a future “day in the life.”
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