2 answers
Arvaiya
Student
Rochester, Minnesota
7
Questions
Asked
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can Data Science be overwhelming?
If it is overwhelming can you tell me why it is? What is the most overwhelming task in this career or you have done?
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2 answers
Pamela Moriarty
Data Scientist
2
Answers
Seattle, Washington
Updated
Pamela’s Answer
Data science work can be very ambiguous and/or require thinking about many different aspects of a project at once, which can feel overwhelming at times. But there's also some things you can do to avoid being overwhelmed:
- As a junior data scientist, your manager and team should help scope projects down to chunks that are manageable and more clearly defined. You can proactively ask for assistance in doing this.
- You can ask teammates for help in deciding next steps, when you're not sure how to proceed on a project.
- Have regular meetings with your manager to make sure you're focused on the most important work.
- As a junior data scientist, your manager and team should help scope projects down to chunks that are manageable and more clearly defined. You can proactively ask for assistance in doing this.
- You can ask teammates for help in deciding next steps, when you're not sure how to proceed on a project.
- Have regular meetings with your manager to make sure you're focused on the most important work.
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.