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How to start a career in Data Science?

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As per the present scenario, jobs in data science are the well-liked trend. There has been frequent growth in the ecosystem of data analytics and data science. India is contributing 6% of job openings of it. Below, we are going to jot down pre-requisites of Data Science which will help you to reach out to great heights in the career of Data Science.

What is Data Science?

Data Science is a meadow that makes use of scientific methods and algorithms to extract knowledge and gain insights from structured and unstructured (raw) data. With the understanding of Science, Data Science is valueless, and trying to achieve something with it may be a fruitless effort; because Data Science is 90% of Science and only 10% of the management of data.

What is Data Scientist?

Data Scientist is a professional with the capabilities to collect a large amount of data to analyze and synthesize the information into the actionable plans for the organizations. Data scientists may present the data in a visualized manner. They often create highly advanced algorithms that are used to determine patterns and take the data from a muddle of number and stats to something that can be useful for business. In this profession, technical skills are not being counted. On the other hand, Data Scientists often exist in business settings and are charged with making complex data-driven organizational decisions.

Responsibilities of a Data Scientist

  • Extract an enormous volume of data from multiple internal and external sources
  • Conduct undirected research and shape open-ended industry questions
  • Compose data-driven solutions to the most pressing challenges
  • Communicate predictions and findings to management and IT departments through effective data visualization and reports
  • Advocate cost-effective changes to existing procedures and strategies
  • Clean and prune the data thoroughly to discard irrelevant information

Above mentioned are the basic responsibilities of a Data Scientist. But remember, every company will have a different take on job tasks. As Data Scientists achieve new levels of experience or change jobs, their responsibilities invariably change.

Data Science Requirements/Pre-requisites

Technical Skills

  • Mathematics, Computer Science, Statistics, and Engineering
  • Programming languages like Python, Julia, and Scala
  • SQL and NoSQL Databases (ability to interact with database)
  • Machine Learning Techniques and Algorithms
  • Experience with Big Data and the related tools
  • Analytical tools like SAS and/or R
  • Experimentation
  • Coding
  • Quantitative Problem Solving
  • Handling a large set of data, etc.

Non-Technical Skills

  • Peculiar Communication Skills
  • Exceptional Curiosity
  • Business acumen and domain knowledge

Bonus: Join Data Science Training and get started for your dream career

Go for Data Science Certifications

  • Principal Data Scientist – DASCA
  • SAS Certified Data Scientist
  • Cloudera Certified Professional (CCP) Data Science Certificate
  • IBM Data Science Professional Certificate
  • EMC Proven Professional Data Scientist Associate (EMCDSA)
  • Microsoft Professional Program Certificate in Data Science

Academic background to become Data Scientist

Data Science degree is the most conspicuous career path. The academic level degrees available to launch your career in Data Science are-

  • Degree in Computer Science
  • Degree in Statistics
  • Degree in Physics
  • Degree in Social Science
  • Degree in Mathematics
  • Degree in Applied Mathematics
  • Degree in Economics

Future of Data Science

Data Science is required by nearly every business, organization, and agency across the globe. So, there is certainly the chance for growth in the future of Data Science. Many Data Scientists are heavily specialized in business; others are in marketing or pricing, and many more. There are Data Scientists who work for the defence department who specializes in the analysis of threat levels, while others specialize in helping small startup businesses to find and retain customers.

Pros and Cons

The pros consist of- the profile is unique and yet challenging that offers a wide variety of daily tasks. So, you’ll be learning new things more frequently. As a Data Scientist, you may work for a wide range of companies coming for business solutions.

This career has some drawbacks too. You’ll have to face new challenges which mean that you never get to fully dive into a specific topic. The technology you specialize in may constantly be evolving, so you need to upgrade yourself from time to time.

Bonus Tips

Look up for the internships in Data Science

Now is an excellent time to try getting internships in Data Science. Many of the people view internships as paths to their future jobs, and indeed, they can be if they impress employers enough. Anyone who is working towards a career in Data Science must realize that most internships are valuable even when participants do not get job offers from employers. Depending upon the span of the internship and the requirements of the company, you might get to build data visualizations and reports, among other tasks. Performing these things might feel overwhelming at times, but the experience gets individuals ready for their future. Wherever you stuck, your co-workers will give you multiple pieces of advice from their own experiences which will help you step ahead in your career.

Do not stop learning and practicing

It is really important to keep on the track of learning with the growing technology to be in the rat race. There are many ways for upskilling yourself such as online courses in Data Science, MOOC’s, youtube videos, conferences, and so on. To learn how to apply Data Science in problems, you should get used to of problem-solving and coding as much as you can.

Get or Git yourself

Having a good portfolio is essential to be hired as a Data Scientist. While building a portfolio, select and complete projects that qualify you for the jobs in Data Science in which you are most interested in. Use your portfolio to promote your strengths and innate abilities by sharing projects you have completed on your own. Along with your portfolio, create and continually build a strong online presence of yours which can be done either through a website or a blog. Be sure to add your GitHub profiles to showcase your passion and proficiency in Data Science. Organizations may also reach out to you through freelance projects, interviews, and other opportunities.

End Notes

Your journey towards Data Science has just begun! There is so much to learn in this field of Data Science that it would take more than a lifetime to master. But don’t worry. You don’t have to master it all to launch your career in Data Science; you just have to get started!

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