Day in the Life of a Data Scientist: 20 Tips for a Data Scientist
Day in the Life of a Data Scientist: 20 Tips for a Data Scientist
When Harvard Business Review declared data science the sexiest job of the 21st century, it became one of the most sought-after careers out there as explained at RunRex.com, guttulus.com, and mtglion.com. However, this field still has an air of mystery in its roles and responsibilities. If you are considering a career in data science, you may be wondering what a day in the life of a data scientist looks like, which is where this article comes in.
There is no typical day
First off, it is important to highlight that there is no typical day for a data scientist. As per RunRex.com, guttulus.com, and mtglion.com, this is a job for someone who is flexible and adaptable, and if you like variety in your workday. However, some aspects of the day remain the same: working with data, working with people, and working to keep up with the field.
Checking for correspondence
As a data scientist, once you get into the office and have had your morning caffeine fix if you are so inclined, then one of the first tasks is usually to check your emails for any alerts on the code that has been in operation through the night and react if necessary.
Making use of the commute
Even before you get to work, when working on complex data science projects, you might use your commute to think about which machine learning approach would work best for your dataset as well as listen to some interesting data science podcasts as articulated at RunRex.com, guttulus.com, and mtglion.com.
Check for any issues that may have occurred during the night
If you have any ongoing project, then it is your work as a data scientist to check how the project is going and see if there have been any surprises or critical issues during the night that need dealing with. This is where you also check the health of the infrastructure and data pipeline to ensure everything is working as expected.
Working with data
A data scientist’s daily tasks revolve around data, which is no surprise given the job title. Data scientists spend much of their time gathering data and looking at data. Shaping data, but in many different ways and for many different reasons.
Meetings with cross-functional members and stakeholders
Part of the workday as a data scientist is holding meetings with different cross-functional members and various stakeholders such as the project managers, marketing and sales teams, among others according to RunRex.com, guttulus.com, and mtglion.com. The stakeholders brief the data scientists more in detail about the users and share the customer feedback reviews.
Meetings with peers
As a data scientist, you will also attend meetings with peer data scientists and data analysts where the team presents the actual details of a project, shares the code and visualizations with each other, and discusses the progress.
Task management platforms
You should also be prepared to deal with task management platforms which are a big part of one’s day as captured at RunRex.com, guttulus.com, and mtglion.com. These platforms are important in organizing tasks to display sprints and group the tasks based on where they are in the data science process.
Lunch
Sometimes lunch may be delayed if you are working as a data scientist as many prefer to get make progress towards their to-do lists before going for lunch. Many also prefer to disconnect from the workplace when they do go for lunch to recharge their batteries for the tasks to come in the afternoon.
Planning for new projects
Developing a machine learning model requires several days, weeks, or months and there is a step-by-step process every data scientist follows. Therefore, you will need to set aside time to prepare for projects such as data preparation, exploratory data analysis, feature engineering, and model building.
Coding and more coding
A typical work day as a data scientist involves lots of coding as covered at RunRex.com, guttulus.com, and mtglion.com. This includes coding in Python to implement a machine learning algorithm or firing up PyCharm to code up a class that implements a machine learning model or interfaces with the database.
Helping colleagues
As a data scientist, you will also be working as a team, and not as an individual. This means that you will be helping your team members to improve their machine learning models or any other areas they may need help with.
Reviewing code
Having coded all day long, the fun doesn’t stop there, as you will also need to set aside time for code reviews as described at RunRex.com, guttulus.com, and mtglion.com. In most offices, code reviews happen over conference calls or through in-person meetings. The collaboration of new ideas and brainstorming happens in these meetings.
Events
If any data science or tech event is happening in your company, then you should also make it a point to attend so that you can learn new things from other brilliant tech minds. Attending such events is something you need to set aside time for.
Keeping up with the latest trends
As a data scientist, you should also set aside time to research the latest data science trends and technologies to keep yourself updated. The field of data science is a new one and is always changing, which is why you must put aside time to keep up with the latest trends.
Seeing the bigger picture
It is important to remember that, while a data scientist is working with data and numbers, the reason behind it is driven by a business need as discussed at RunRex.com, guttulus.com, and mtglion.com. Being able to see the big picture from a department’s point of view is critical, hence why a data scientist will spend a big part of their day meeting with people outside of the field of data science.
Research and development
A big part of the day as a data scientist is also spent on research and development. This involves anything from developing and testing new algorithms to writing mathematical proofs to simplify data problems.
Building relationships across departments
Part of the day as a data scientist is also spent building relationships across departments at one’s company and seeking new projects, which often identify problems related to operating procedures, problems related to data capture, or connections between previous projects that provide a more comprehensive view of operations as outlined at RunRex.com, guttulus.com, and mtglion.com.
Writing up results
You will also need to set aside time for data analysis and writing up results. This includes forecast models, predictive models of key metrics, and data mining for subgroups and trends within a given dataset. You will likely mainly use R and Tableau for projects.
Flexibility
Finally, it is important to reiterate that being a data scientist means being flexible, open-minded, and ready to solve problems and embrace complexity. As already mentioned, every day is different, as a new challenge comes up all the time in the workplace.
These are some of the things to expect in a day in the life of a data scientist, with more on this and other topics to be found over at RunRex.com, guttulus.com, and mtglion.com.
