5 answers
Kang
Student
Fremont, California
5
Questions
Asked
404 views
What does a day in the life of a data scientist look like?
Looking to potentially become a data scientist in the future.
Login to comment
5 answers
PRASANJIT PASWAN
Service
112
Answers
Bengaluru, Karnataka, India
Updated
PRASANJIT’s Answer
Data scientists spend much of their time gathering data, looking at data, shaping data, but in many different ways and for many different reasons.
I appreciate you taking the time to answer this.
Kang
Adelyn Arens
Data Analyst Intern
5
Answers
Columbus, Ohio
Updated
Adelyn’s Answer
Hey Kang, the biggest part of being a data scientist is cleaning data. Real world data is often not in a good or useable form, so you must spend a lot of time making it look pretty. Once you do this, you can then run it through a model but that is a lot less time since the computer will do the majority of the work. An important skill of a data scientist is to be a creative thinker. You must determine what inputs to the model will give the desired result.
Pooja Shah
Software Engineer
2
Answers
Pune, Maharashtra, India
Updated
Pooja’s Answer
Being a data scientist is equal to being an overthinker. A data scientist has to consider a number of scenarios under consideration. The maximum time a data scientist spends is in data discovery and then applying correct statistics.
Thank you for sharing your perspective.
Kang
Ben Gof
Finance
27
Answers
Wayne, New Jersey
Updated
Ben’s Answer
I am actually more of a data analyst that is in a Business Intelligence role. Some people use data analyst and data scientist synonymously.
The majority of the time is spent working with the Business Partners I support in my company and trying to address their data needs. For example, they may ask to see if their is a correlation between the amount of money spent on equipment vs average life of the equipment. Or does more spend on maintenance, really show that the equipment breaks down less. A data scientist can also work with the marketing teams to try and determine the cause and effect of spending money on ad campaigns and how that may affect sales. There's dozens of these types of scenarios that are analyzed on a daily basis in business. So the data scientists role is crucial.
Going back to the original question of a typical day, it could look like this:
- Meet with the business partner to determine their needs. What are they trying to do, solve or analyze.
- Determine where you can access the data needed.
- Once obtaining the data, review it and clean it up. For example, may need to remove or replace null values, remove duplicates, etc.
- Start creating charts and arranging the data that addresses your business partners request.
- Revisit with the BP to review your initial findings. Take notes.
- Work on revising your initial findings after revisiting with the Business Partner.
The goal of the team I am on is as follows:
• Automate any rule based reports. Use tools like Python, Alteryx, Power BI, Tableau, Qlik
• Build Dashboards providing insight to the Business partners, which allow them to make sound informed business decisions.
• Respond to requests that are either new or to revise existing reports and dashboards.
• Provide deep dive analysis wherever applicable.
There's never a dull moment and if you enjoy statistics, working with numbers, charts, tables, you'll enjoy this career as I do.
The majority of the time is spent working with the Business Partners I support in my company and trying to address their data needs. For example, they may ask to see if their is a correlation between the amount of money spent on equipment vs average life of the equipment. Or does more spend on maintenance, really show that the equipment breaks down less. A data scientist can also work with the marketing teams to try and determine the cause and effect of spending money on ad campaigns and how that may affect sales. There's dozens of these types of scenarios that are analyzed on a daily basis in business. So the data scientists role is crucial.
Going back to the original question of a typical day, it could look like this:
- Meet with the business partner to determine their needs. What are they trying to do, solve or analyze.
- Determine where you can access the data needed.
- Once obtaining the data, review it and clean it up. For example, may need to remove or replace null values, remove duplicates, etc.
- Start creating charts and arranging the data that addresses your business partners request.
- Revisit with the BP to review your initial findings. Take notes.
- Work on revising your initial findings after revisiting with the Business Partner.
The goal of the team I am on is as follows:
• Automate any rule based reports. Use tools like Python, Alteryx, Power BI, Tableau, Qlik
• Build Dashboards providing insight to the Business partners, which allow them to make sound informed business decisions.
• Respond to requests that are either new or to revise existing reports and dashboards.
• Provide deep dive analysis wherever applicable.
There's never a dull moment and if you enjoy statistics, working with numbers, charts, tables, you'll enjoy this career as I do.
I am really grateful you took the time to answer this question.
Kang
Nicole Baillie
Data Analyst
463
Answers
Atlanta, Georgia
Updated
Nicole’s Answer
Hi Kang L. Thanks so much for the forward-looking question!
In my experience, a day in the life of a data scientist includes some initial understanding that the work of a data science is much more building block-oriented than it is task oriented. What I mean by that is there are jobs where an individual will be given a task and that task may get completed on that same day. The work of a data scientist typically takes a longer time to complete only because the data scientist has a broad range of tools to assess whether or not their outcomes are "right". In order to make good and sustainable assessments, the work that a data scientist does requires time, patience, space for trial and error and collaboration. In other words, one day may be spent pounding out some code, another day may be spent running and adjusting code and analyzing results, another day may be spent sharing those results with others who maybe have a deeper understanding of the business initiative at hand. Their feedback becomes very important and sometimes their feedback requires a new set of ideas/logic to be included in the work of the data scientist.
It is also my experience that the work of a data scientist is never boring :)..If one is truly interested in learning and impacting results in a positive way, the role of a data scientist can be incredibly helpful towards creating improvement. Additional reward can come from the fact that the data scientist can use their tools to help get to great answers faster.
Hope you find this answer helpful. Best of luck to you!
In my experience, a day in the life of a data scientist includes some initial understanding that the work of a data science is much more building block-oriented than it is task oriented. What I mean by that is there are jobs where an individual will be given a task and that task may get completed on that same day. The work of a data scientist typically takes a longer time to complete only because the data scientist has a broad range of tools to assess whether or not their outcomes are "right". In order to make good and sustainable assessments, the work that a data scientist does requires time, patience, space for trial and error and collaboration. In other words, one day may be spent pounding out some code, another day may be spent running and adjusting code and analyzing results, another day may be spent sharing those results with others who maybe have a deeper understanding of the business initiative at hand. Their feedback becomes very important and sometimes their feedback requires a new set of ideas/logic to be included in the work of the data scientist.
It is also my experience that the work of a data scientist is never boring :)..If one is truly interested in learning and impacting results in a positive way, the role of a data scientist can be incredibly helpful towards creating improvement. Additional reward can come from the fact that the data scientist can use their tools to help get to great answers faster.
Hope you find this answer helpful. Best of luck to you!
Thank you Nicole! This is really helpful!
Kang