4 answers
Paul
Student
1
Question
Asked
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What are the top skills required for a successful data scientist, and how can I develop them to advance my career in this field?
What are the top skills required for a successful data scientist, and how can I develop them to advance my career in this field as a student in the college
3 answers
Eesha Gothwal
Product Manager
3
Answers
Redmond, Washington
Updated
Eesha’s Answer
To become a successful data scientist, you'll need a mix of technical and soft skills. Key technical skills include programming (Python, R), statistics, data wrangling, machine learning, data visualization, database management (SQL), and cloud computing (Azure, AWS, Google Cloud). Soft skills like communication, problem-solving, and collaboration are equally important.
As a college student, you can develop these skills through online courses on platforms like Coursera and DataCamp, participating in projects and internships, joining data science communities on GitHub and Kaggle, staying updated with the latest trends, and practicing regularly with open datasets. This combination of skills and practical experience will set you on the path to a successful career in data science.
As a college student, you can develop these skills through online courses on platforms like Coursera and DataCamp, participating in projects and internships, joining data science communities on GitHub and Kaggle, staying updated with the latest trends, and practicing regularly with open datasets. This combination of skills and practical experience will set you on the path to a successful career in data science.
Nicholas Lisac
AI & Data Analytics
4
Answers
Portland, Maine
Updated
Nicholas’s Answer
The two most important skills you need are:
1) Curiosity, like an unrivaled amount of it.
2) The ability to learn on your own many things across domains.
Example Data Science Project: Youl want to know how something works, so you learn about different ways of finding that out. You learn that XYZ technique can help isolate the cause/effect for the thing you're interested in, so you learn how to do XYZ technique. You find out that XYZ technique needs a specific data type/transformation called ABC that you've never heard of. You research ABC data transformation - now you know what kind of data you need and what to do with it. You then need to search to find data that will work for your project but find it isn't easily accessible (like in a nice .csv for download), so you look into where you can find the data. You find that it is either a) on the web in web pages, b) behind some kind of API, c) available, but in a weird format you've never used before (maybe a downloadable JSON), so you need to learn how to access/process the data using tools like Python or R. Then you need to learn how to set up Python/R, learn about environments, learn about packages and libraries...
You see where this is going.
You do all of those steps, not only because you're curious about the original question you wanted to answer, but because you're curious about EACH of the steps in the process. If you get stumped, you need to pivot or power through. Eventually there will be a problem you find with no safety net or resource, and you'll need to figure out the blocker on your own.
The single best way to develop the skills you need BEYOND your inherent curiosity and ability to learn is to follow that example blueprint above for problems/questions/projects you are interested in doing on your own. You don't need to bring them all to completion, but you do need to try. Each success or failure will add to your knowledge bank and the next project will be easier/faster. You can even put them into a portfolio or blog that you can link to on your resume. That will make you stand out. It will show initiative and drive and also highlight to prospective employers that you have the 2 most important skills; curiosity and the ability to learn.
Best of luck on your journey!
1) Curiosity, like an unrivaled amount of it.
2) The ability to learn on your own many things across domains.
Example Data Science Project: Youl want to know how something works, so you learn about different ways of finding that out. You learn that XYZ technique can help isolate the cause/effect for the thing you're interested in, so you learn how to do XYZ technique. You find out that XYZ technique needs a specific data type/transformation called ABC that you've never heard of. You research ABC data transformation - now you know what kind of data you need and what to do with it. You then need to search to find data that will work for your project but find it isn't easily accessible (like in a nice .csv for download), so you look into where you can find the data. You find that it is either a) on the web in web pages, b) behind some kind of API, c) available, but in a weird format you've never used before (maybe a downloadable JSON), so you need to learn how to access/process the data using tools like Python or R. Then you need to learn how to set up Python/R, learn about environments, learn about packages and libraries...
You see where this is going.
You do all of those steps, not only because you're curious about the original question you wanted to answer, but because you're curious about EACH of the steps in the process. If you get stumped, you need to pivot or power through. Eventually there will be a problem you find with no safety net or resource, and you'll need to figure out the blocker on your own.
The single best way to develop the skills you need BEYOND your inherent curiosity and ability to learn is to follow that example blueprint above for problems/questions/projects you are interested in doing on your own. You don't need to bring them all to completion, but you do need to try. Each success or failure will add to your knowledge bank and the next project will be easier/faster. You can even put them into a portfolio or blog that you can link to on your resume. That will make you stand out. It will show initiative and drive and also highlight to prospective employers that you have the 2 most important skills; curiosity and the ability to learn.
Best of luck on your journey!
Santos Rivero
Analyst
2
Answers
Panama City, Panama
Updated
Santos’s Answer
Before getting into the technical area, set some time to understand the math behind first (statistics, regression models, matrix, derivatives). As Eesha mentioned, get some practice with Coursera, DataCamp, Google, Azure learning materials, which are easy to follow. Then look for some opportunities to get involved in real business projects...business acumen is key for the context. Try Kaggle if you don't have available data. At least you will practice what you've learned.
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