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What are the best online resources that I can use to develop the skills necessary for a career in data science?

I am a senior in high school, and I am interested in majoring in statistics, and my goal is to land a job in data science.

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Heidi’s Answer

This is a great question! My son is in data science. Here were some of his suggestions on where to go. If you want to build data-science skills, these online resources are some of the best:

1. Coursera: Great beginner-to-advanced courses (Google Data Analytics, IBM Data Science, Python, machine learning).
2. Khan Academy: Free, clear lessons for brushing up on math and statistics.
3. Kaggle: Practice with real datasets, learn through mini-courses, and enter competitions.
4. Codecademy: Hands-on practice for Python, SQL, and data tools.
5. YouTube (StatQuest, Data School, Corey Schafer): Excellent free explanations of key concepts.
6. LinkedIn Learning: Short, practical courses on Python, Excel, Tableau, and analytics.

Also, don't forget to follow those influencers on the topic!
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Valerie’s Answer

In doing some research, the best online resources for developing data science skills include comprehensive learning platforms like Coursera and edX for structured courses and certifications, DataCamp and Dataquest for interactive coding practice, and Kaggle for real-world projects and community engagement.
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Faith’s Answer

Hi Victoria,

It's great that you're planning to build skills for data science early on. As a high school senior, starting with beginner-friendly resources can really help.

Here are some great online options:

1. Free Courses and Tutorials
- Khan Academy (Statistics & Probability): A great way to strengthen your math and stats basics.
- Coursera & edX: Check out beginner courses like "Introduction to Data Science" or "Python for Everybody." Many are free if you audit them.
- DataCamp: Offers hands-on coding exercises in Python and R, which are key for data science.

2. Coding Practice
- Codecademy: Provides interactive lessons in Python, SQL, and data analysis.
- LeetCode (Beginner Level): Helps you with problem-solving and logic, important for data science.

3. Real-World Projects
- Kaggle: A wonderful place to learn through tutorials and join beginner-friendly challenges. It's also great for building a portfolio.

4. YouTube Channels
- StatQuest: Explains complex statistical concepts in a simple way.
- freeCodeCamp: Offers full-length tutorials on Python, data analysis, and machine learning.

5. Books & Blogs
- "Python for Data Analysis" by Wes McKinney is good read.
- Follow blogs like Towards Data Science on Medium for the latest trends and tips.

Start small by learning Python basics, practicing with simple datasets, and gradually exploring statistics and machine learning. By the time you start college, you'll have a solid foundation and maybe even a portfolio to showcase.
Thank you comment icon Thank you for your response and advice, I really appreciate it! Victoria
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Val’s Answer

Hi Victoria,

I'm Val, and I work for a health plan in California. If you're diving into data science, getting comfortable with large, real-world datasets is a great start. The Healthy Places Index (HPI) is a fantastic resource to explore. It provides detailed community-level information on housing, education, transportation, and social conditions. Using this dataset will help you practice cleaning, joining, visualizing, and understanding complex data. Working with HPI will teach you how to manage big data, deal with its variations, and handle real-world challenges, which are essential skills for any data scientist.
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Sid’s Answer

Hi Victoria,

I hope you're doing well. I'm a software engineer at Dell Technologies in Austin, and I'm really interested in data science. I work on many data engineering projects. Here's how you can get started:

1. Learn about mathematical modeling. Coursera offers courses on this topic. Apply for financial aid to save money while learning valuable skills.
2. Join bootcamps. Learn from your peers and also teach them. I've gained a lot from these experiences.
3. Connect with communities on Reddit and LinkedIn. They help you stay updated.
4. Use Kaggle to participate in online competitions and start building models.

Don't wait for the perfect time to start. Begin building models as you learn the basics. You won't know everything at first, but starting early and improving over time will make you a better data scientist.
Thank you comment icon Thank you for the advice and encouragement! Victoria
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