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How to Stand out from the Competition in Data Science as a Fresher?

Introduction

The rise of advanced information technology at the turn of the 21st century signalled a coming paradigm shift in how human society might work in the future. With the increase in automation, machine learning, and 3D printing, many careers that were once historically respectable professions face obsolescence, being replaced with faster and more efficient technological solutions.

Data science, one of the new up and coming technology fields of the modern era, looks to be an attractive alternative career path for those in the job market, with plenty of online training resources and material and certifications provided by various institutions.

Data Science refers to the study of any vast volumes of data in multiple sources and formats using tools such as machine learning algorithms and techniques such as predictive modelling to extract patterns and derive meaningful information that could be used to make sound business decisions.

As an interdisciplinary field, data science as a domain unifies several concepts such as statistics, data analysis, informatics, data mining, and big data, and uses techniques and theories drawn from many domains such as mathematics, statistics, computer science, information science, and case-by-case domain knowledge for each application.

The knowledge and insights gained from data could solve problems in a wide range of application domains. Data science enables superior decision making through pattern discovery and improved predictive analysis. Some applications of data science are:

  • Finding the most important cause of a problem by discovering the right questions to focus on.
  • Performing exploratory studies and analysis of raw data to determine how best to approach the problem.
  • Data modelling using machine learning algorithms for improved accuracy.
  • Communication and visualization of results through the necessary mediums, such as graphs or dashboards.

An example of how data science principles can benefit businesses is the airline industry, where data science is used in route planning, flight scheduling and forecasting delays and disruptions. Data science is also used to decide which planes to purchase for best overall performance and in determining personalized promotional offers based on customer booking patterns.

As companies across various industries and government agencies all seek to empower their decision making through data science, there has been an understandably steep increase in the number of aspirants seeking to enter the job market. While there is certainly no shortage of job opportunities in data science, here are a few things that might help increase one’s employability and stand out from the rest of the competition in the data science industry:

Tips to Become a Successful Data Scientist

Critical Thinking: Critical thinking is a useful skill in everyday life and one that most employers look for, but even more so in data science hires. Applicants would be expected to look at problems from different perspectives to understand how to best approach and analyze them.

Data scientists are expected to know how to frame a question and not just find an answer and demonstrate a diverse variety of problem-solving techniques. A strong portfolio that showcases the applicant’s critical thinking in various projects would charm potential employers.

Communication: Data science as a field is not communication-intensive; with the bulk of the work involving the querying and analysis of data, there is a not-insignificant amount of professional communication involved in conveying the necessary results party.

Data scientists do not work in an isolated bubble and might have to collaborate with or report to people in other fields, and will therefore be expected to have good oratory and written communication skills to explain and discuss problems, questions, and ideas.

Studies claim that improper communication costs large organizations up to USD 62 million a year, so applicants should develop their interpersonal skills and technical knowledge by participating in group projects to stay ahead of the competition in the data science industry.

Intellectual curiosity: Any good data scientist should be able to look for solutions to the problems they are given – but great data scientists are the ones that actively seek out situations that they can fix. Being part of a disruptive new field of information science, Data scientists are expected to be able to think outside of the traditional framework of problem-solving and to implement creative solutions by examining under-the-radar issues.

Employers look for data scientists that are passionately driven by curiosity. They possess a problem-solving mindset that can help the company scale and grow. Applicants can demonstrate their intellectual curiosity through individual projects, showcasing an initiative taking attitude.

Domain knowledge: Data science, as previously mentioned, is a disruptive technology transforming the operations of entire industries and sectors of the economy – However, like any tool, the applications of data science are limited by the knowledge and capabilities of the user.

While data scientists may be skilled at processing and analyzing all types of data, they would not have an above-average understanding of the subject knowledge in most fields. Freshers would require additional training before their skills can be adequately used. Companies, therefore, tend to look for data science applicants that have a work history in the same domain, so the new hire can hit the ground running. 

Adaptability: Data scientists are expected to be highly adaptable and capable of picking up new skills as and when demanded by changing job requirements. Given the various potential uses of data science in practically every aspect of business, data scientists are expected to apply themselves to different situations as part of their daily work.

Working in a tech-centric, rapidly evolving field, Data scientists will have to constantly adapt to keep up with the most recent developments to keep pace with the competition in the data science industry. Applicants can highlight their adaptability by covering the diversity in the nature of the work in their previous projects. 

Time management: Data scientists should have reliable time management skills, as their fast-paced job may at times be highly demanding. Applicants will be expected to develop their time-management strategies to meet the employer’s rigorous demands. Good time management skills are useful not just in data science but to improve productivity and reduce stress in all aspects of life.

Conclusion

In conclusion, the core technical knowledge of data science itself is only the foremost quality employers look for in a sea of aspirants; in order to stand out from the crowd one must further cultivate and hone their soft skills and personality traits. 

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

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