Data science internships provide opportunities to learn from the pros, gain practical experience and build a strong professional network. But, getting a data science internship is not straightforward, you need to showcase your expertise and enthusiasm to work for the company. Firstly, one would require a strong foundation of data science concepts. Then the next step would be to build projects starting with the simpler ones to the ones that have real-world use cases. These add value to your overall skills and take you a step closer in your data science journey.

It takes a lot of work, from basics like creating a resume and LinkedIn profile to posting them on job boards, a lot of effort goes into it. Some of the tips which are essential to get a better data science internship are listed below. (Keep in mind, basic steps like preparing a strong resume and cover letter are something that is important too).

Solidify Knowledge

Getting an internship is similar to getting a job. No matter which one it is, one has to be proficient in the basics of data science concepts that are a must-have — statistics, programming languages like Python or R are essential for any data scientist.

One can take up online resources that can prove to be more flexible or opt for offline classes. Also, one needs to follow sites like AIM, KDNuggets, and Kaggle, to stay updated with everything that’s going on in the data science community.

Soft Skills

One of the crucial things recruiters will look at while hiring a data science intern is soft skills. These include strong written and verbal communication skills. Soft skills help interns to develop a relationship with stakeholders for the company’s betterment. Besides, it will assist you while collaborating with different departments of an organisation. Data scientists are expected to share ideas, insights, and criticism to successfully communicate their results. In a large company, one needs to have soft skills to successfully collaborate with stakeholders.

Research

One of the most advisable courses of action for your data science strategy for landing a better internship would be by making a list of companies that you would be interested in. This list can be based on the industry, culture, location and brand appeal. It’s important to know about these factors when it comes to selecting these companies. When you have successfully made a list, then its time to understand what they are looking for and how well you can contribute to that company. Once you have the list, look at the skills required for these companies. This will help you streamline your options and possibly help you choose the right internship.

Building An Attractive Portfolio

Building a strong portfolio doesn’t only involve working on projects and displaying them on your resume. Part of building a portfolio can also involve blogging or showcasing projects on platforms like Kaggle or GitHub. Besides, try to contribute on StackOverflow to gain reputations as it will communicate the expertise you have in the data science techniques. You can also participate in MachineHack to test your skill against other data science enthusiasts and learn in the process. Participation is key for aspirants than winning a hackathon. This demonstrates your enthusiasm and interest to thrive in the competitive industry.

Finding A Mentor

To thrive in a competitive landscape, you need to adopt best practices, which you can get from mentors. Good guidance from data science leaders can help you in devising a perfect strategy to obtain an internship. You can register here to get your questions answered from prominent data scientists. Mentors can not only show you the right path but also motivate you to keep going in the competitive data science space.

Internship Search

After you have taken care of the basics that involve making an attractive resume and cover letter, the next step is to put this information on a job board, particularly the ones that are related to data science. Some of the recommended job boards and places you can search for internships are: Internshala, Github, AngelList, Kaggle Jobs, Analytics India Magazine, LinkedIn.

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The Pew Research Center, a nonpartisan think tank, once asked 979 researchers, tech pros, and policy leaders about the future of artificial intelligence (A.I.). The results weren’t exactly comforting: while 63 percent of respondents believed that A.I. will improve individual lives by 2030, some 37 percent said that the technology won’t benefit humanity. What’s needed, some of those experts claim, is machine “empathy.”

“A number of the thought leaders who participated in this canvassing said humans’ expanding reliance on technological systems will only go well if close attention is paid to how these tools, platforms and networks are engineered, distributed and updated,” read Pew’s report.

The report also quoted a number of those experts. “I strongly believe the answer depends on whether we can shift our economic systems toward prioritizing radical human improvement and staunching the trend toward human irrelevance in the face of A.I,” said Bryan Johnson, founder and CEO of Kernel, a developer of advanced neural interfaces. “I don’t mean just jobs; I mean true, existential irrelevance, which is the end result of not prioritizing human well-being and cognition.”

Other experts had similar suspicions. “A.I. will drive a vast range of efficiency optimizations but also enable hidden discrimination and arbitrary penalization of individuals in areas like insurance, job seeking and performance assessment,” said Andrew McLaughlin, executive director of the Center for Innovative Thinking at Yale University.

Before A.I. can conquer the world (and take a percentage of jobs currently done by humans), it will have to overcome some big technological hurdles. For example, A.I. has no common sense; while you can use machine learning to train a software platform to react to a narrow band of circumstances, it won’t respond in predictable ways to similar situations. Just because you train your vacuum-cleaning robot to not ram into one type of obstacle doesn’t mean it’ll have the judgment to avoid different obstacles.

Organizations such as Google and DARPA are already working on those nuanced problems. However, the experts queried by Pew point to another, more refined issue that may prove critical to A.I. development: empathy.

Based on the collective advice of these experts, Pew recommends that tech pros “build inclusive, decentralized intelligent digital networks ‘imbued with empathy’” that will “aggressively ensure that technology meets social and ethical responsibilities.” Some level of “regulatory and certification process,” it added, “will be necessary.”

Some organizations, most notably OpenAI, are actively trying to create an ethical framework around artificial intelligence and machine learning. Google is also pushing a new program, AI for Social Good, which is meant to tackle some of the planet’s biggest challenges. Will those kinds of initiatives persuade tech pros to move with caution when it comes to this new, potentially dangerous technology? That could become the biggest existential question of all, and whoever figures out “machine empathy” and “artificial soft skills” could end up a winner in the competition to make the perfect A.I.

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