6 answers
Arvaiya
Student
Rochester, Minnesota
7
Questions
Asked
744 views
what characteristics do you need for Data Science?
How do you have to act when being a Data Scientist? Do you have to be really good at math? Do you need to be a person who does not get distracted easily?
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6 answers
Christa Hammond
Data Science Manager
1
Answer
Denver, Colorado
Updated
Christa’s Answer
I've been managing Data Science teams for about 7 years now (after being a Data Scientist myself). You will need to be comfortable with math in order to complete the training required to become a data scientist. In addition to technical skills (math and computer programming), the best data scientists also have the ability to communicate with non-technical stakeholders, and can understand the big picture of why their work makes a difference to the business. This often means not being afraid to ask questions.
Different Data Scientists may work on multiple projects at once, or may spend significant time in meetings with stakeholders. However, in order to be successful, you will need to be able to spend some time focusing on data preparation and analysis. Data Scientists are also usually salaried positions with relatively high starting pay, so there is generally an expectation of professionalism at work. I hope this is helpful to you, best wishes!
Different Data Scientists may work on multiple projects at once, or may spend significant time in meetings with stakeholders. However, in order to be successful, you will need to be able to spend some time focusing on data preparation and analysis. Data Scientists are also usually salaried positions with relatively high starting pay, so there is generally an expectation of professionalism at work. I hope this is helpful to you, best wishes!
Patrick Meyer
Executive - Data Analytics and AI
780
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Arvaiya, you have asked a series of excellent questions. To be successful in Data Science, in my view, a mix of essential traits is necessary. Above all, a robust analytical mindset is key, as data scientists have to make sense of intricate data sets and extract valuable knowledge.
Being good at math is useful, but you don't have to be a math genius; a firm grasp of statistics and algebra, however, is crucial.
Arvaiya, another critical area is possessing strong communication skills, which are crucial for explaining findings to both tech-savvy and non-tech-savvy individuals. Remember that it's important to clarify analytical models and calculations to people with various levels of technical understanding.
Additional vital skills include being meticulous and tenacious in solving problems, given the complex nature of data analysis.
While being focused is beneficial, being adaptable is just as important, as the Data Science domain frequently requires dealing with changing technologies and approaches. The only other aspect to concentrate on in this field is to maintain ethical and honest practices when creating and/or examining data.
Being good at math is useful, but you don't have to be a math genius; a firm grasp of statistics and algebra, however, is crucial.
Arvaiya, another critical area is possessing strong communication skills, which are crucial for explaining findings to both tech-savvy and non-tech-savvy individuals. Remember that it's important to clarify analytical models and calculations to people with various levels of technical understanding.
Additional vital skills include being meticulous and tenacious in solving problems, given the complex nature of data analysis.
While being focused is beneficial, being adaptable is just as important, as the Data Science domain frequently requires dealing with changing technologies and approaches. The only other aspect to concentrate on in this field is to maintain ethical and honest practices when creating and/or examining data.
Manesh Tailor
Field Engineering Director
5
Answers
London, England, United Kingdom
Updated
Manesh’s Answer
Hello, I notice that you're brimming with questions about the field of Data Science. It's wonderful to witness your keen interest and determination to delve deeper into this area, aiming to comprehend how you can thrive in it. In an attempt to address your multitude of queries, I'll consolidate my responses into a single, comprehensive answer. Please bear with me as this might turn out to be quite lengthy.
While I must admit that I'm not a Data Scientist by profession, I hold a degree in Statistics and have a solid background in Monte Carlo Simulation and Bayesian Analysis. I also work in close collaboration with our Data Science Team, so I'm more than willing to offer my perspective on this subject.
In response to your initial question about the characteristics needed to become a Data Scientist, a robust understanding of Mathematics is crucial. A deep comprehension of Statistics is essential as it forms the backbone of data interpretation and results analysis. If you have a passion for Statistical Math, you're off to a great start. Another vital trait is curiosity. You should be the kind of person who loves to ask questions and seeks evidence. Moreover, you should be willing to challenge your own hypothesis. It's often easy to justify a hypothesis or viewpoint using data, but striving to disprove it is a unique skill.
Additional skills and knowledge that will significantly aid you include the ability to query data using SQL. Despite the existence of numerous No-SQL databases, the fundamental understanding of joins, filters, relationships, and data navigation from a Database is indispensable. Complementing this is the need for some programming skills. You don't necessarily need to master a specific language like Java, Python, or NodeJS (although that would be beneficial), but having a mindset that grasps programming logic, iteration, parsing, and programmatic operations is a critical skill.
One common frustration among Data Scientists is the lack of control over certain aspects. These include:
a) The data source - initially, you have little control over what data is collected, the collection method, and frequency.
b) The data's accuracy and completeness - issues like incomplete or inaccurate data collection can arise.
c) The systems used for data mining - the suitability of the data storage for your analysis type and the budget for acquiring better tools.
d) Time estimation - it can be challenging to predict how long it will take to obtain specific answers, which can be stressful when under pressure as businesses increasingly rely on data science results for crucial decisions.
However, these challenges are balanced by the rewarding outcomes of your work. The impact you can make on a business or research output can be exhilarating. The significant contributions you can make to companies can be incredibly rewarding and satisfying.
While I must admit that I'm not a Data Scientist by profession, I hold a degree in Statistics and have a solid background in Monte Carlo Simulation and Bayesian Analysis. I also work in close collaboration with our Data Science Team, so I'm more than willing to offer my perspective on this subject.
In response to your initial question about the characteristics needed to become a Data Scientist, a robust understanding of Mathematics is crucial. A deep comprehension of Statistics is essential as it forms the backbone of data interpretation and results analysis. If you have a passion for Statistical Math, you're off to a great start. Another vital trait is curiosity. You should be the kind of person who loves to ask questions and seeks evidence. Moreover, you should be willing to challenge your own hypothesis. It's often easy to justify a hypothesis or viewpoint using data, but striving to disprove it is a unique skill.
Additional skills and knowledge that will significantly aid you include the ability to query data using SQL. Despite the existence of numerous No-SQL databases, the fundamental understanding of joins, filters, relationships, and data navigation from a Database is indispensable. Complementing this is the need for some programming skills. You don't necessarily need to master a specific language like Java, Python, or NodeJS (although that would be beneficial), but having a mindset that grasps programming logic, iteration, parsing, and programmatic operations is a critical skill.
One common frustration among Data Scientists is the lack of control over certain aspects. These include:
a) The data source - initially, you have little control over what data is collected, the collection method, and frequency.
b) The data's accuracy and completeness - issues like incomplete or inaccurate data collection can arise.
c) The systems used for data mining - the suitability of the data storage for your analysis type and the budget for acquiring better tools.
d) Time estimation - it can be challenging to predict how long it will take to obtain specific answers, which can be stressful when under pressure as businesses increasingly rely on data science results for crucial decisions.
However, these challenges are balanced by the rewarding outcomes of your work. The impact you can make on a business or research output can be exhilarating. The significant contributions you can make to companies can be incredibly rewarding and satisfying.
Wow! thank you for taking the time to write all of this. This really helped me and my studies of data science. Again thank you so much for this wonderful information!
Reid Allen
Project Manager - Analytics and Innovation
3
Answers
Vancouver, British Columbia, Canada
Updated
Reid’s Answer
Hello,
I graduated from university with an undergraduate degree in Materials and Manufacturing Engineering. I worked as an engineer for a few years then pivoted to data, analytics, data science and strategy type roles.
Engineering gave me a strong background in math and statistics which I believe is important but to me what is even more important is the ability to learn new things and being resourceful. No one will expect you to memorize specific formulas, however it is valuable to have a general knowledge of statistical methods so you know where to look to find more information quickly and understand how to apply what you learn.
I wouldn't say it's necessary to have extremely good focus (however it is generally important in any field), what works for me (especially when working on projects with higher complexity that require more focus) is to block out large chunks of time in my calendar dedicated to working on the project. This helps me prioritize the work and the large chunk of time allows me to get myself in the right mindset, reduce distractions/interruptions and then begin development.
If you're in a business analyst role compared to research heavy role, a very important skill which is never really talked about (or taught) is the ability to communicate effectively to your stakeholders/customers. This entails tailoring language/presentations for the intended audience by choosing wording and visuals that will effectively communicate the information/data. This is important because without it, the work you've put into the complex development could become underutilized or not utilized at all if it's not understood at some level by your intended audience.
This did not come naturally to me, I am typically focused on the technical/numerical aspects. I worked with an executive in my company who mentored me specifically for presenting information. We did practice sessions building presentation and presenting them. We recorded the practice presentations and reviewed them together. This was a very effective way to evaluate my presentation skills and identify specific areas of improvement (tone, cadence, effective use of pauses).
Data science/analytics is a really great field to be in, I highly value the transferrable skills it gives me that are applicable across almost any industry.
I graduated from university with an undergraduate degree in Materials and Manufacturing Engineering. I worked as an engineer for a few years then pivoted to data, analytics, data science and strategy type roles.
Engineering gave me a strong background in math and statistics which I believe is important but to me what is even more important is the ability to learn new things and being resourceful. No one will expect you to memorize specific formulas, however it is valuable to have a general knowledge of statistical methods so you know where to look to find more information quickly and understand how to apply what you learn.
I wouldn't say it's necessary to have extremely good focus (however it is generally important in any field), what works for me (especially when working on projects with higher complexity that require more focus) is to block out large chunks of time in my calendar dedicated to working on the project. This helps me prioritize the work and the large chunk of time allows me to get myself in the right mindset, reduce distractions/interruptions and then begin development.
If you're in a business analyst role compared to research heavy role, a very important skill which is never really talked about (or taught) is the ability to communicate effectively to your stakeholders/customers. This entails tailoring language/presentations for the intended audience by choosing wording and visuals that will effectively communicate the information/data. This is important because without it, the work you've put into the complex development could become underutilized or not utilized at all if it's not understood at some level by your intended audience.
This did not come naturally to me, I am typically focused on the technical/numerical aspects. I worked with an executive in my company who mentored me specifically for presenting information. We did practice sessions building presentation and presenting them. We recorded the practice presentations and reviewed them together. This was a very effective way to evaluate my presentation skills and identify specific areas of improvement (tone, cadence, effective use of pauses).
Data science/analytics is a really great field to be in, I highly value the transferrable skills it gives me that are applicable across almost any industry.
Vivienne Thairu
Data Scientist
3
Answers
Ashburn, Virginia
Updated
Vivienne’s Answer
Hello there,
In the world of data science, one of the most valuable traits you can possess is effective communication and teamwork abilities. This is crucial because it's your job to comprehend the business challenges at hand. This means actively listening to your clients or end users and asking insightful questions. Being able to transform a business issue into a data science problem demands a solid grasp of the problem you're tackling.
Additionally, you'll often find yourself liaising with data engineers and other tech-savvy teams as you develop your model and prepare to pass it on to other teams for implementation.
To put it simply, possessing robust communication skills will not only make you a superior scientist but also enable you to assist your clients in overcoming business hurdles. Naturally, this goes hand in hand with having a well-equipped data science toolkit, which includes strong technical skills like coding, building models with Python/R, mathematics, and data visualization.
In the world of data science, one of the most valuable traits you can possess is effective communication and teamwork abilities. This is crucial because it's your job to comprehend the business challenges at hand. This means actively listening to your clients or end users and asking insightful questions. Being able to transform a business issue into a data science problem demands a solid grasp of the problem you're tackling.
Additionally, you'll often find yourself liaising with data engineers and other tech-savvy teams as you develop your model and prepare to pass it on to other teams for implementation.
To put it simply, possessing robust communication skills will not only make you a superior scientist but also enable you to assist your clients in overcoming business hurdles. Naturally, this goes hand in hand with having a well-equipped data science toolkit, which includes strong technical skills like coding, building models with Python/R, mathematics, and data visualization.
Stefan Elser
Data Scientist
2
Answers
Colorado Springs, Colorado
Updated
Stefan’s Answer
Hi Arvaiya,
You've received a lot of great answers to your questions so far, so I'll try a different approach and describe a somewhat typical week for me as a data scientist.
I will attend a variety of meetings, some where I am presenting my progress and analysis to technical peers or business colleagues. This will require me preparing presentation slides, data visualizations, etc. in order to convey my work. I will need to communicate to fellow data scientists and give detailed explanations, but also I will communicate with non-technical people whose feedback is just as helpful. However, I will need to simplify my explanations for that audience. Sometimes I will lead meetings myself. This will be in order to ask for assistance in getting my work deployed to production or to seek out new data sources and ask for access to someone's data in order to make use of it myself.
As has already been said, I am also expected to behave as a professional and send and respond to emails or Slack messages. I also frequently need to document my work. A certain amount of productivity is expected, but I am generally given the independence to decide what order I want to accomplish tasks in. This gives the ability for someone who wants to jump from one task to another to get as much done as someone who focuses on one thing at a time.
The majority of my time is to work on analysis and coding. At the start, this can be relatively quick and maybe even fun when I am brainstorming and creating a simple proof of concept. After this, many iterations are produced in order to improve the results. Before implementing something new in a production model, I need to analyze the impact from many angles to justify the change. I need to convince myself and my team and managers that the change is beneficial. A large amount of time is spent exploring the data and checking it to be sure that the data is clean. If not, I need to clean it. Sometimes I need to take time to research new methods. Once a model gets into production or a change has been approved, I might need to spend a significant amount of time writing the code to implement things.
If any of this sounds difficult or dull, I'm happy to report that I still love my job. The wide variety of skills that I get to employ over the course of a week or month keeps me intellectually engaged. Additionally, this is just one example of what a data scientist might find themselves doing, particularly in a business setting. The best part about the field of data science is that there are many different ways to work in it, and also many different fields of data that you will get to work on!
Have fun and enjoy the journey,
Stefan
You've received a lot of great answers to your questions so far, so I'll try a different approach and describe a somewhat typical week for me as a data scientist.
I will attend a variety of meetings, some where I am presenting my progress and analysis to technical peers or business colleagues. This will require me preparing presentation slides, data visualizations, etc. in order to convey my work. I will need to communicate to fellow data scientists and give detailed explanations, but also I will communicate with non-technical people whose feedback is just as helpful. However, I will need to simplify my explanations for that audience. Sometimes I will lead meetings myself. This will be in order to ask for assistance in getting my work deployed to production or to seek out new data sources and ask for access to someone's data in order to make use of it myself.
As has already been said, I am also expected to behave as a professional and send and respond to emails or Slack messages. I also frequently need to document my work. A certain amount of productivity is expected, but I am generally given the independence to decide what order I want to accomplish tasks in. This gives the ability for someone who wants to jump from one task to another to get as much done as someone who focuses on one thing at a time.
The majority of my time is to work on analysis and coding. At the start, this can be relatively quick and maybe even fun when I am brainstorming and creating a simple proof of concept. After this, many iterations are produced in order to improve the results. Before implementing something new in a production model, I need to analyze the impact from many angles to justify the change. I need to convince myself and my team and managers that the change is beneficial. A large amount of time is spent exploring the data and checking it to be sure that the data is clean. If not, I need to clean it. Sometimes I need to take time to research new methods. Once a model gets into production or a change has been approved, I might need to spend a significant amount of time writing the code to implement things.
If any of this sounds difficult or dull, I'm happy to report that I still love my job. The wide variety of skills that I get to employ over the course of a week or month keeps me intellectually engaged. Additionally, this is just one example of what a data scientist might find themselves doing, particularly in a business setting. The best part about the field of data science is that there are many different ways to work in it, and also many different fields of data that you will get to work on!
Have fun and enjoy the journey,
Stefan