How to Build best data science portfolio in University – Mayur Rele

data science

Data science is a career that is promising and rewarding. The industry constantly needs competent data scientists and research scholars. Many higher education institutions in the world offer data science courses and research areas in data science. They aim to help their students become expert researchers in data science and build good portfolios.

According to cloud infrastructure expert, Mayur Rele, those who are interested in data science research require a portfolio. Mayur Rele is a cybersecurity expert and cloud automation leader with extensive experience in the IT industry.

Building a unique and professional portfolio is the first step in the right direction. To build a good portfolio, you need to have a goal, understand your career path, and work towards it.

How do you go about it? A data scientist must have excellent computational, analytical, and critical thinking skills. A data scientist must be willing to improve his skills at any given opportunity. This is because technology evolves every time, and people in the IT sector have to evolve too. When they evolve and brush up their skills, their competency level increases. Consequently, they will be able to compete with their peers on an international level.

To understand what it means to build a data science portfolio, here is a definition of a data science portfolio:

Meaning of Data Science Portfolios

They are the extensions of an individual’s currículum vitae. Extensive details about people’s professional lives that are not included on the resume can be found on portfolios. Thus, data science portfolios are the real-life lists of a data scientist’s skills, abilities, work experience, and so on.

Let’s take the example of a data scientist who is also a technical content writer. The scientist has a blog where he writes his technical knowledge of cloud computing, data security, and many more. He also has a website where he uploads weekly tutorials on data science. All these are physical evidence of his content writing skills. They can serve as references in the future if he wants to be a researcher of data science.

He can gather the links to his blogs, websites, and social media pages. He will also list the content of these sites and collect them to build a portfolio.

To put it simply, data science portfolios show the academic world the projects data scientists have done. In addition, their scientific abilities and their research experiences are included in the portfolios.

Content of Data Science Portfolios

Data science portfolios should include:

  • Work experience
  • Sites or blogs links if available
  • Research experience
  • Lists of projects done on data science
  • Your interests, aims, and career objectives.
  • Areas of expertise in other branches of data science. For example, areas like AI, Machine Learning, Robotics, Cloud Computing, cloud security, and many more.

When you have all these, arrange them accordingly. Then go online, look for efficient websites for building portfolios and create an account. Upload your portfolios on your preferred site(s) so that you can access them anytime. Doing this will make it easy for recruiting websites or prospective employers to view your professional abilities.

How to Develop Excellent Portfolios

To develop a wonderful portfolio, you need a plan.

Start by brushing up your skills and knowledge. Technology evolves every day, therefore there are always new skills and knowledge discovered in data science. You don’t need to attend physical classes to learn these skills. You can sign up for virtual classes on the internet. In virtual classes, learners can learn according to their level of comprehension. There are many professional courses you can also register for. Doing this will give you an extensive yet diverse experience and portfolio.

For data science students who wish to further their studies, there are various data science competitions out there. For example, there are hackathons, innovative challenges, and Robotics display competitions for students. When students register for these events, they learn more and also know the areas where they are lacking. This will improve their technical skills in the long run.

Moreover, institutions partner with big tech firms to create these events. This means that student participants are exposed to job opportunities from these firms during the events. Additionally, prizes, in cash or kind, are awarded to outstanding performers at the events. The result is that students get networking and job opportunities, and get rewarded at the same time.

The third step in building portfolios for data scientists is creating projects to solve real-life problems. The type of project a data scientist creates can determine the success of his career.

Look for projects that will enhance your skills, provide solutions to problems, and are relevant to your future career.

You can do data science projects in areas like:

Data clean up: Removing unwanted or redundant data from the database has been a challenge to data scientists. When there is too much data, useless data will encompass useful data. That is, a scientist will find it difficult to find useful data among the cluttered database. Also, the presence of too much useless data can reduce the available storage space. A data scientist can take on projects that are enclosed within cluttered data.

Machine Learning: Learn machine concepts and use machine learning to improve the lifestyle of people. You can use machine learning projects to provide solutions to real-world issues.

Cloud Security: This is a very important aspect of data science due to the increased rate of data breaches. You can create projects to address the issue of third-party data breaches by corporate organizations. According to Mayur Rele, the internet is a tool that makes it easy to hack into cloud storage devices. Therefore, data scientists all over the world welcome useful research on data security.

In conclusion, prepare your portfolio in a neat, concise, and easy-to-read way. Furthermore, diversify your portfolio to attract recruiters or prospective research professors. This will give you an edge over your colleagues in the industry.

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