How to Become a Data Scientist – An Actionable Guide

posted on

Working as a data scientist may be intellectually stimulating, analytically fulfilling, and place you at the forefront of technological breakthroughs. Data scientists are becoming more prevalent and in demand, as big data becomes more relevant in how corporations make decisions. 

This demand can be well understood from the fact that the average salary for a data scientist in the US is $100,910 per year, and the BLS predicts a 36% job growth rate, which is way above average. Looking at this, one thing is clear, data scientists are needed, and they will be paid well.

Data scientists decide which questions their team should ask and how to respond to those inquiries using data. In addition, they frequently create prediction models to aid in thinking and predicting. Here’s a deeper look at what they are, what they do, and how to become a data scientist.

How to Become a Data Scientist

On a daily basis, one may expect a data scientist to do the following tasks:

  1. Discover patterns and trends in datasets to get insights.
  2. Develop forecasting algorithms and data models.
  3. In data analysis, use data tools such as Python, R, SAS, or SQL.
  4. Improve the standard of data or product offerings by utilizing machine learning techniques.
  5. Keep up with the latest developments in the world of data science.
  6. Distribute suggestions to other teams and top management.

What Skills Are Required to Become a Data Scientist?

If you are wondering how to become a good data scientist to stand out from the crowd, then the first step is to be aware of the skills and competencies that you must possess to become a data scientist. So let us look at them first.

What Skills Are Required to Become a Data Scientist
  1. Foundations of Data Science
  2. Mathematical and Statistical Analysis
  3. Knowledge in programming, data manipulation, and analysis
  4. Machine Learning Data Visualization
  5. Advanced Learning
  6. Deployment of a Big Data Software Engineering Model
  7. Communication Abilities
  8. Storytelling Capabilities
  9. Curiosity Structured Thinking

This list is not exhaustive, but we believe the abilities mentioned above are a must-have for anyone aspiring to become a data scientist.

5 Essential Steps to Take for Becoming a Data Scientist

Let us look in detail at the steps to becoming a data scientist. We shall explore this guide in a continuous approach where the measures, although seen as sequential, can be adjusted based on your different circumstances.

Essential Steps to Take for Becoming a Data Scientist
  1. Education and Training Options
  2. Learn Tools and Technologies for Data Science
  3. Gain experience
  4. Develop Portfolio
  5. Job Market and Career Paths

1. Education and Training Options

An undergraduate degree in a related discipline, such as data science, statistics, or computer science, is an excellent way to get started in data science. It is one of the most prominent factors used by businesses when recruiting data scientists.

Tool and skill-specific courses are excellent methods to demonstrate your knowledge and proficiency in your field. This is why if your undergraduate degree is not in a relevant field, you should not give up your dream of becoming a data scientist.

You can check out several of the top data science bootcamps and enroll in one to continue working on becoming a data scientist.

2. Learn Tools and Technologies for Data Science

While a Bachelor’s degree will provide you with a theoretical foundation of the topic, you will need to brush up on computer languages such as Python, R, SQL, and SAS. These are critical languages for working with massive datasets.

To help in this regard, we suggest checking out the following course offerings to help polish your data science competencies.

You can also check out the best programming language courses to help you gain a broad perspective of different programming languages, not just one.

Additionally, a Data Scientist should be familiar with a few technologies for Data Visualization, Machine Learning, Big Data, and several languages. When working with enormous datasets, it is critical to understand how to clean, sort, and analyze them. You can also look at the best machine learning courses to further enhance your undergraduate learning in machine learning.

3. Gain Experience

Internships are an excellent way to access firms that hire data scientists. Look for positions with keywords like data analyst, business intelligence analyst, statistician, or data engineer. Internships are also an excellent opportunity to understand firsthand what the work entails.

After your internship, you can continue working for the same firm (if they are recruiting) or begin looking for entry-level opportunities for data scientists, analysts, and engineers. As your knowledge and talents improve, you may collect experience and work your way up the ladder.

4. Develop a Portfolio

When you’ve done your initial research, received training, and exercised your new abilities by creating an outstanding collection of projects, the next step is to display those talents by creating the perfected portfolio that will get you your ideal job.

n essence, your portfolio might be the most crucial factor in your job search. For example, the best data science bootcamps listed at KnowItGetIt are designed to provide a project-based experience that allows students to develop an outstanding portfolio of completed real-world projects. That is one of the finest strategies to distinguish yourself in the job market.

5. Job Market and Career Paths

According to Glassdoor, a data scientist made an average income of $126,104 in the United States in March 2023. According to the US Bureau of Labor Statistics (BLS), data scientists and mathematical science jobs are anticipated to rise by 31% and statisticians by 33% between 2020 and 2030. This is significantly faster than the overall job growth rate of 8%.

The emergence of big data and its rising relevance to corporations and other organizations have been connected to the increased demand. Therefore, one can deduce that not only is the job market for Data Scientists open right now, but the future is also bright.

Regarding career paths, data scientists are making their mark in nearly every industry, from education, traveling, and finance to even healthcare. There are multiple titles that are associated with the role of a data scientist and how they can climb the career leader in any organization. We shall discuss these titles ahead.

Careers in Data Science After Becoming a Good Data Scientist

Since Data Scientists’ work crosses so many sectors and fields, the jobs Data Scientists might serve are known by many various titles, including:

  1. Data Scientist/Analyst
  2. Business Analyst 
  3. Data Architect 
  4. Data Engineer 
  5. Manager of Data and Analytics 
  6. Machine Learning Engineer
  7. Quantitative Analyst
  8. Statistician 
  9. Database Administrator

There are several additional types that will continue evolving as data science becomes more common. This is why we recommend that when you’re searching for a role as a data scientist in any organization, you should look out for the above-mentioned job titles and more.

Become a Data Scientist – FAQs

Can You Become a Data Scientist Without a Degree?

Yes, you can become a data scientist without a degree. The simple answer is that formal schooling in this sector is not required to begin your career. Yet, to acquire a data science career, you must have finished a higher education degree or get a relevant certification through a data science course or bootcamp.

How Long Does It Take to Become a Data Scientist?

You may master the skills required to become a Data Scientist in as short as 12 weeks, so new Data Scientists are increasingly attending data science bootcamps, which allow for more significant hands-on learning and focused skill development.

Is It Hard to Become a Data Scientist?

Data Science occupations might be more challenging to learn than other disciplines of technology due to the highly technical needs. In addition, having a solid grasp of such a diverse range of languages and applications is a very high learning curve. But with the right mindset, hard work, and dedication, it is a dream career.

Conclusion

Studying to become a data scientist is difficult, but staying motivated and appreciating your work is critical. In addition, you’ll gain competence and land the data scientist job you want if you continuously create and share projects.

We might not have provided you with a perfect road map to studying data science, but if you stick with it, you’ll get farther than you thought possible. Anyone who is driven enough can become a data scientist and learn more tips along the way on how to become a data scientist.

After becoming dissatisfied with how traditional sites taught data science or predicted the career path, we at KnowItGetIt have laid the best guide on becoming a data scientist, along with the best data science courses and data science bootcamps.

We hope that you find this guide a better method to learn data science online. KnowItGetIt overcomes the challenges associated with online learning, such as needing to know what course to take next and not being inspired by what you’re learning.

Good luck with your endeavors to become a data scientist!