2 answers
Arvaiya
Student
Rochester, Minnesota
7
Questions
Asked
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different parts of data science ?
In data science is there more than just finding data for businesses? Is there other tasks?
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2 answers
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.
This is super helpful but I'm wondering if you can touch on the student's question a little more. What careers can you do with data science?
Gurpreet Lally, Admin
Sylvia Deng
Data Scientist
1
Answer
New York, New York
Updated
Sylvia’s Answer
Data scientists cover a lot of areas related to data and not just finding data. More importantly, they need to analyze and interpret complex data, draw insights from the data they collected to make actionable suggestions to help organizations make informed decisions. Here's a breakdown of what they typically do:
- Data Collection: Gather large sets of structured and unstructured data from various sources.
- Data Cleaning: Process and clean the data to ensure accuracy and completeness.
- Data Analysis: Use statistical methods and tools to analyze the data and identify trends, patterns, and insights.
- Model Building: Develop predictive models and algorithms using machine learning techniques.
- Experimentation: Design and conduct experiments to test hypotheses and validate models.
- Visualization: Create visualizations and dashboards to present findings in an understandable way.
- Communication: Communicate insights and recommendations to stakeholders to drive business decisions.
In terms of subject area, it may be related to a new product that a company wants to release users, it may be related to a feature or model change that engineering team wants to put production, or it may be a new UX design to enhance user experience. And you have the chance to working on all these different kinds of subject areas depending on the stakeholders that you are working with.
Hope this can give you some insights about what data scientists do at their day to day job.
- Data Collection: Gather large sets of structured and unstructured data from various sources.
- Data Cleaning: Process and clean the data to ensure accuracy and completeness.
- Data Analysis: Use statistical methods and tools to analyze the data and identify trends, patterns, and insights.
- Model Building: Develop predictive models and algorithms using machine learning techniques.
- Experimentation: Design and conduct experiments to test hypotheses and validate models.
- Visualization: Create visualizations and dashboards to present findings in an understandable way.
- Communication: Communicate insights and recommendations to stakeholders to drive business decisions.
In terms of subject area, it may be related to a new product that a company wants to release users, it may be related to a feature or model change that engineering team wants to put production, or it may be a new UX design to enhance user experience. And you have the chance to working on all these different kinds of subject areas depending on the stakeholders that you are working with.
Hope this can give you some insights about what data scientists do at their day to day job.