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What are some extracurricular activities if I want to be a data scientist?
I’m a senior and I was wondering what I should be doing or consider doing in high school or outside of school so that it would get me ready for data science. What are some resources that can help? #data-science
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8 answers
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Karla’s Answer
Hi Jessica,
I recommend finding an internship - even though if you cannot find something specific to data science, look for similar alternatives. The advantage is that you will learn other activities, understand processes and how companies work. It will give you a different perspective and also you will gain some experience.
Another alternative is to ask your Professors if they know about bootcamps that you can join or activities that can help you strengthen specific skills. Normally at the University or with your professors, you can get several activities that you can do either as learning or volunteering teaching younger students. Besides, there are some professors that also work at companies or do research and sometimes they need somebody to help them with their projects. These are great ways to get extra curricular activities in your field of interest.
I recommend finding an internship - even though if you cannot find something specific to data science, look for similar alternatives. The advantage is that you will learn other activities, understand processes and how companies work. It will give you a different perspective and also you will gain some experience.
Another alternative is to ask your Professors if they know about bootcamps that you can join or activities that can help you strengthen specific skills. Normally at the University or with your professors, you can get several activities that you can do either as learning or volunteering teaching younger students. Besides, there are some professors that also work at companies or do research and sometimes they need somebody to help them with their projects. These are great ways to get extra curricular activities in your field of interest.
This was super helpful, thank you!
Jessica
Updated
Buket’s Answer
Hi, you can start to take introductory coding classes to be familiar with the programming first then you can enroll to more advanced data analytics classes like SQL, Python, R programming and some of the data visualization classes like Tableau. More advanced part of the data analytics is the predictive modeling and machine learning. There are machine learning packages of R and Python that you can get started to be familiar with the machine learning and artificial intelligence.
Thanks for the advice.
Jessica
Updated
Thomas’s Answer
There are many different extracurricular activities that can help you become a data scientist. However, the best way to learn the skills necessary for this field is to get experience working with data. This can be done through internships, webinars, online courses, and bootcamps.
Some great ways to get experience working with data include participating in online data science challenges. These challenges allow you to work with real-world datasets and compete against other data scientists from around the world. Another great option is to join a local meetup group or online forum devoted to data science. These groups typically have members who are happy to help answer questions and share resources.
Some great ways to get experience working with data include participating in online data science challenges. These challenges allow you to work with real-world datasets and compete against other data scientists from around the world. Another great option is to join a local meetup group or online forum devoted to data science. These groups typically have members who are happy to help answer questions and share resources.
Updated
Sahar’s Answer
It's always nice to have a portfolio and an easy way of building one is by doing online coding and data science competitions. Kaggle.com is a great place to start, both for personal project ideas and actual competitions, but there are many sites online that do the same thing. Getting involved in these communities will help you keep up with current ideas and concepts in the field, give you a group of people to make connections with, and build valuable hands-on experience.
Thank you for the advice, Sahar.
Jessica
Updated
Dave’s Answer
Along with the other great suggestions, I would dig into SQL and go pretty deep. SQL is just one part of what data scientists need to understand, but the stronger you are, the better.
"Learning SQL: Master SQL Fundamentals" from O'Reilly is a great starter book. You can use free tools along with it to learn a basis to work from. It's pretty fun!
"Learning SQL: Master SQL Fundamentals" from O'Reilly is a great starter book. You can use free tools along with it to learn a basis to work from. It's pretty fun!
Thank you so much! I will definitely take a look at the book.
Jessica
Updated
Alex’s Answer
Hi Jessica,
You can add to your knowledge by taking free coding classes in SQL, Python and Tableau. Those will be some of the basic skills you'll need to be able to interpret data and chart and present it. The more you practice on these tools, the easier it will be to then apply logic into data problems.
You can add to your knowledge by taking free coding classes in SQL, Python and Tableau. Those will be some of the basic skills you'll need to be able to interpret data and chart and present it. The more you practice on these tools, the easier it will be to then apply logic into data problems.
This was super helpful, thank you!
Jessica
Updated
Shea’s Answer
I'd recommend learning languages like Python, SQL, and R. There are free games online that you can play that help you learn such as TwilioQuest, SQL Murder Mystery, or CodinGame. If you like podcasts I'd recommend checking out the Women in Data Science podcast and a TikTok account I like is @damselindata.
Most importantly I'd also recommend figuring out what in data science you find the most interesting. There are a lot of different paths you can take in the data science field and narrowing down to a few options that feel the most interesting to you will help you better understand what you want to learn.
Most importantly I'd also recommend figuring out what in data science you find the most interesting. There are a lot of different paths you can take in the data science field and narrowing down to a few options that feel the most interesting to you will help you better understand what you want to learn.
Thank you for the advice, Shea.
Jessica
James Constantine Frangos
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James Constantine’s Answer
Hi Jessica,
Boost Your Data Science Career with These Extracurricular Activities
To pave your way towards a successful data science career, consider these extracurricular activities. They can help you gain the necessary skills and knowledge:
1. Mathematics and Statistics Clubs: Boost your quantitative skills, vital for data analysis, by joining math or statistics clubs. They often provide chances to join competitions, workshops, and projects related to data analysis.
2. Computer Science Competitions: Improve your programming skills, essential for data handling, by joining coding competitions like hackathons or programming contests. These events typically involve solving real-world problems using data sets.
3. Data Science Internships or Projects: Gain practical experience with data analysis tools and techniques by seeking internships or personal data science projects. This hands-on experience can solidify your data science foundation.
4. Machine Learning Workshops: Deepen your understanding of advanced data analysis methods by attending workshops or seminars on machine learning. As a critical part of data science, expertise in this area is invaluable.
5. Data Visualization Clubs: Learn to communicate data insights effectively through visual representations by joining data visualization clubs. These skills are key to presenting findings in a clear and engaging way.
6. Online Courses and Certifications: Show your dedication to learning data science by enrolling in online courses or obtaining certifications in areas like statistics, programming languages (e.g., Python, R), machine learning, and data analysis tools (e.g., SQL, Tableau).
7. Research Opportunities: Gain exposure to real-world applications of data science concepts by getting involved in data analysis research projects or collaborating with professors on research studies.
8. Networking Events: Connect with professionals and experts in the field of data science by attending networking events. They can provide guidance, mentorship, and potential growth opportunities.
Boost Your Data Science Skills with These Resources
Supplement your extracurricular activities with these resources to further support your journey towards becoming a successful data scientist:
1. Kaggle: This platform hosts machine learning competitions and provides datasets for practice. Engage with Kaggle competitions and kernels to hone your data science skills through hands-on projects.
2. Coursera: Offering online courses from global universities and institutions on topics like machine learning and big data analytics, Coursera can enhance your knowledge and credentials in data science.
3. DataCamp: An online platform providing interactive courses on topics like R programming, Python, SQL, and machine learning. Use DataCamp’s resources to gain practical skills applicable to a data science career.
By actively engaging in data science-focused extracurricular activities and using resources like Kaggle, Coursera, and DataCamp, you can better equip yourself for a thriving career in this fast-growing field.
Top 3 Credible Sources Used:
Kaggle
Coursera
DataCamp
Best Wishes,
JC.
Boost Your Data Science Career with These Extracurricular Activities
To pave your way towards a successful data science career, consider these extracurricular activities. They can help you gain the necessary skills and knowledge:
1. Mathematics and Statistics Clubs: Boost your quantitative skills, vital for data analysis, by joining math or statistics clubs. They often provide chances to join competitions, workshops, and projects related to data analysis.
2. Computer Science Competitions: Improve your programming skills, essential for data handling, by joining coding competitions like hackathons or programming contests. These events typically involve solving real-world problems using data sets.
3. Data Science Internships or Projects: Gain practical experience with data analysis tools and techniques by seeking internships or personal data science projects. This hands-on experience can solidify your data science foundation.
4. Machine Learning Workshops: Deepen your understanding of advanced data analysis methods by attending workshops or seminars on machine learning. As a critical part of data science, expertise in this area is invaluable.
5. Data Visualization Clubs: Learn to communicate data insights effectively through visual representations by joining data visualization clubs. These skills are key to presenting findings in a clear and engaging way.
6. Online Courses and Certifications: Show your dedication to learning data science by enrolling in online courses or obtaining certifications in areas like statistics, programming languages (e.g., Python, R), machine learning, and data analysis tools (e.g., SQL, Tableau).
7. Research Opportunities: Gain exposure to real-world applications of data science concepts by getting involved in data analysis research projects or collaborating with professors on research studies.
8. Networking Events: Connect with professionals and experts in the field of data science by attending networking events. They can provide guidance, mentorship, and potential growth opportunities.
Boost Your Data Science Skills with These Resources
Supplement your extracurricular activities with these resources to further support your journey towards becoming a successful data scientist:
1. Kaggle: This platform hosts machine learning competitions and provides datasets for practice. Engage with Kaggle competitions and kernels to hone your data science skills through hands-on projects.
2. Coursera: Offering online courses from global universities and institutions on topics like machine learning and big data analytics, Coursera can enhance your knowledge and credentials in data science.
3. DataCamp: An online platform providing interactive courses on topics like R programming, Python, SQL, and machine learning. Use DataCamp’s resources to gain practical skills applicable to a data science career.
By actively engaging in data science-focused extracurricular activities and using resources like Kaggle, Coursera, and DataCamp, you can better equip yourself for a thriving career in this fast-growing field.
Top 3 Credible Sources Used:
Kaggle
Coursera
DataCamp
Best Wishes,
JC.