How can an Average Student become a Data Scientist?

According to a U.S. Bureau of Labor Statistics report, 11.5 million new jobs will be created in the data science field by 2026. Many individuals want to become data scientists but are clueless about how to get started, especially if they are average students. Most of them think they can’t become one because they are not among the top scores or hold an advanced degree or a specialization in a data science subject in their formal education.

However, there is a huge demand for professionals with even basic data scientist skills. Data science is a dynamic field that adopts statistical, programming, and domain fact information to extract valuable information from big data to transform decision-making across many sectors. Hence, data science career opportunities are becoming more appealing to potential aspirants.

Ways an Average Student can become a Data Scientist

Let’s understand an outline of the essential aspects of getting into this field and starting to work as a data scientist, even though you are an average student.

• Self-Assessment

This is the initial and crucial aspect to consider when starting the data science journey as an average student. Ask yourself questions like why an organization should hire you. What do you know about the data science field? What more do you require to know about the data science field? What data scientist skills and certifications help stand out from the crowd?

Along with the skills and certifications, it is essential for an individual who aspires to be a data scientist should possess a keen interest in the latest industry trends and good knowledge of the current job roles that are on-demand, such as the latest programming languages. Along with the knowledge and skills you should have, the insights related to the latest industry trends — how an industry works, the available job roles in high demand, programming languages used, and many more. Keep a note of all the essential things and start working on them.

• Polish Math Skills

Mathematics is considered one of the crucial subjects when it comes to data science. It covers many concepts related to data science, such as statistics and probability theory, for algorithm application. You might be an average student if you put effort into sharpening your mathematical skills, as it will help you write efficient code and get accurate conclusions.

A few highly recommended math concepts used are: 

• Statistical methods and probability theory
• Probability distributions
• Linear algebra
• Hypothesis testing
• Statistical modelling and fitting
• Multivariable calculus
• Data summaries and descriptive statistics
• Regression analysis
• Bayesian thinking and modelling
• Markov chains

• Learn Programming Languages

Knowledge of programming languages is essential to build a strong foundation in any programming language. Keep your complete focus on them. You can also enhance your programming skills by doing a data scientist certification.

The interview process for data science jobs is skill-based, so master the following programming languages: 

• Python
• R
• Java

• SQL
• TensorFlow
• Julia

• Machine learning, deep Learning, and AI

Knowledge learning and AI are among the core data skills for data scientists. ML is used to build predictive models and practice problem-solving. Deep Learning is a high-growth vertical in the strLearningI. You can excel by mastering your programming skills, mainly in Python, and having sound mathematics and linear algebra knowledge.

Knowledge of the following libraries is helpful: 

• PyTorch
• Keras
• TensorFlow

• Communication Skills

Communication is the most critical skill. A data scientist’s quality is formulating a problem statement. For this to happen, it is important to communicate correctly to business executives about the business perks of data, computational, and technology resources.

• Do an Internship

Internships are the best way to gain knowledge of this field. During the job-hunting process, holding an internship will offer real-world experience with the help of real-world data. Internships are helpful when there is no previous experience or advanced degree. During the internships, one can interact with others with many years of experience in data science. One can learn, understand and implement the best solutions to positively impact one’s career and increase the chances of becoming a data scientist.

• Pursue Data Scientist Certifications

Doing a data scientist certification enhances an individual’s skill set. Certifications are helpful for getting a better job and standing out from the ongoing competition. They also add a significant amount of value to one’s career, improve knowledge of the latest industry standards, tools, and technologies along with practical exposure, boost chances to get higher salaries, and provide more and better opportunities across various industries to exhibit skill and solve industry problems effectively.

Several hiring managers consider recruiting a certified candidate because they consider certifications a benchmark for reviewing an individual. In the current competitive world, an accredited candidate will have a fulfilling career.

Conclusion

Even if you are an average student, you can pursue a career in the data science field. Stay focused on your goal, enhance your skillset, gain expertise in mathematics and statistics, and consider doing some real-time projects, certification programs, and courses. Be patient and determined enough so that you can achieve success.