How to become a data scientist?

Answered by

a year ago

Obtain a Bachelor's degree in a related field: A Bachelor's degree in fields such as mathematics, Statistics computer Science, or engineering can provide you with a strong foundation in the necessary skills for data Science. Many universities offer specialised data Science programs at the undergraduate level. Pursue a Master's degree in data Science or a related field: A Master's degree in data Science or a related field can provide you with advanced knowledge and skills in areas such as statistical modeling, data visualization, and machine learning.

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Answered by

Vidita Mehta | Contributor-Level 8

4 years ago

Hi,
Good to know that you're thinking to learn more about one of the most booming and career-worthy fields in current time, which is Data Science. It is quite popular and demanding among a lot of students in terms of learning and making a good career in it. Coming back to your query, you can become a Data Scientist by considering a few short-term courses from well-known organizations. For learning Data Scientist from a scratch, I would highly recommend Imarticus Learning, which is an educational institute provides such training programs on specific fields like - Analytics, Finance, Technology, etc. The following training programme, you

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Answered by

Roshni Choudhary BA (H) Economics at St. Stephen's College. | Guide-Level 13

6 years ago

The conventional order of operations: (1) start studying and learning skills, (2) write your resume, and then (3) search for jobs. Another method –
1. Choose one industry. Limiting your initial search to one industry has many benefits. Not only will this reduce the number of topics to study, but it will also allow you to start building invaluable domain knowledge and adding relevant portfolio projects. These will give you a huge edge during the interview process.
2. Next, go to a job board such as Glassdoor, LinkedIn Jobs, or Indeed and search for data Science positions in your chosen industry. Don't just limit your search to data sci

...more

The conventional order of operations: (1) start studying and learning skills, (2) write your resume, and then (3) search for jobs. Another method –
1. Choose one industry. Limiting your initial search to one industry has many benefits. Not only will this reduce the number of topics to study, but it will also allow you to start building invaluable domain knowledge and adding relevant portfolio projects. These will give you a huge edge during the interview process.
2. Next, go to a job board such as Glassdoor, LinkedIn Jobs, or Indeed and search for data Science positions in your chosen industry. Don't just limit your search to data scientist. Try other terms such as data analyst, machine learning engineer, or quantitative analyst.
3. Create a skills profile. Distill the useful and actionable information into a skills profile.
4. Write your future resume. If your resume has a Skills section, go ahead and list the ones from the skills profile. If you're currently working, are there any projects you can join that would give you relevant skills or experience? If so, include them. If you're currently in school, are there any classes you can take that would give you relevant skills or experience? If so, include them. Are there any side projects you'd like to tackle and add to your portfolio? If so, include them. Bonus points for projects related to the industry you'd like to join.
5. Start studying and practicing. Now that we have our skills profile and future resume, were finally ready to start learning, studying, and filling in any gaps. In other words, it's time to achieve that future resume.
6. Weave in end-to-end projects. While you study, attempt an end-to-end project every other weekend. Start with a real-world dataset, pose an interesting question, and then try to answer it on your own.
7. Hit your milestones, and then just apply.
The limbo between study mode and the formal job hunt causes many candidates to stagnate. Pick concrete milestones beforehand and once you hit them, just start applying. Project-based milestones are recommended.
8. Prepare for the interview.

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The conventional order of operations: (1) start studying and learning skills, (2) write your resume, and then (3) search for jobs. Another method – 1. Choose one industry. Limiting your initial search to one industry has many benefits. Not only will this reduce the number of topics to study, but it will also allow you to start building invaluable domain knowledge and adding relevant portfolio projects. These will give you a huge edge during the interview process. 2. Next, go to a job board such as Glassdoor, LinkedIn Jobs, or Indeed and search for data Science positions in your chosen industry. Don't just limit your search to data scientist. Try other terms such as data analyst, machine learning engineer, or quantitative analyst. 3. Create a skills profile. Distill the useful and actionable information into a skills profile. 4. Write your future resume. If your resume has a Skills section, go ahead and list the ones from the skills profile. If you’re currently working, are there any projects you can join that would give you relevant skills or experience? If so, include them. If you’re currently in school, are there any classes you can take that would give you relevant skills or experience? If so, include them. Are there any side projects you’d like to tackle and add to your portfolio? If so, include them. Bonus points for projects related to the industry you’d like to join. 5. Start studying and practicing. Now that we have our skills profile and future resume, were finally ready to start learning, studying, and filling in any gaps. In other words, it’s time to achieve that future resume. 6. Weave in end-to-end projects. While you study, attempt an end-to-end project every other weekend. Start with a real-world dataset, pose an interesting question, and then try to answer it on your own. 7. Hit your milestones, and then just apply. The limbo between study mode and the formal job hunt causes many candidates to stagnate. Pick concrete milestones beforehand and once you hit them, just start applying. Project-based milestones are recommended. 8. Prepare for the interview.

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R

Answered by

rahul r | Scholar-Level 18

6 years ago

Broadly speaking, you have 3 education options if your considering a career as a data scientist:
1) Degrees and graduate certificates provide structure, internships, networking and recognised academic qualifications for your rsum. They will also cost you significant time and money.
2) MOOCs and self-guided learning courses are free/cheap, short and targeted. They allow you to complete projects on your own time but they require you to structure your own academic path.
3) Bootcamps are intense and faster to complete than traditional degrees. They may be taught by practicing data scientists, but they won't give you degree initials after yo

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