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How do I find a mentor for data science?

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How do I find a mentor for data science?

Here are a few ways one can find a good data science mentor:

  1. Free online communities.
  2. Networking in tech conferences.
  3. Joining data science courses.
  4. Participating in hackathons & boot camps.
  5. Workplace mentorship.
  6. Wrapping Up.

Where can I find a mentor online for free?

Here are a few resources to get you started on the path to finding a mentor:

  1. MicroMentor. Free online mentor service that connects small business owners with business mentors.
  2. SCORE Mentoring.
  3. ACE Mentor Program.
  4. TiE Global.
  5. Mamas (Mother Attorneys Mentoring Association).

What should I ask my data science mentor?

Ask yourself and potential mentors some questions to determine if this mentorship is a good fit….For a potential mentor:

  • What is your background in this field?
  • Do you feel comfortable guiding me in X?
  • What skill or leadership areas do you not feel prepared to mentor me in?

How can I become a data scientist for free?

Here are the most required skills for a data scientist position based on ReSkill’s analyses of thousands of job posts and free resources to learn each skill:

  1. Python. Learn Python Programming From Scratch by Udemy.
  2. Machine Learning. Machine learning online.
  3. R Language.
  4. Big Data.
  5. Statistics.
  6. Data Mining.
  7. SQL.
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How much should you pay a mentor?

In our experience fees ranging from $50 to $3,000 have been charged, usually depending on the seniority of the cadre of mentors. We suggest that the sweet spot for most association-led programs would be $150-300 per mentee. As well as helping to fund the program, the fee also helps to qualify mentees.

Is mentoring free?

Mentors are free, which makes them priceless in more ways than one. Typically, a mentoring relationship will grow organically through connections within your industry and network.

How do you ask good data questions?

To sum it up, here are the most important data questions to ask:

  1. What exactly do you want to find out?
  2. What standard KPIs will you use that can help?
  3. Where will your data come from?
  4. How can you ensure data quality?
  5. Which statistical analysis techniques do you want to apply?