Data Scientist Requirements

If you are interested in pursuing a career in data science, you have probably wondered what it takes to become a Data Scientist? With average salaries of $130,000 per year, according to the U.S. Bureau of Labor Statistics, and the ability to work for organizations ranging from corporations to government agencies and more, it’s easy to see how becoming a Data Scientist can lead to an interesting, rewarding career. With the need for Data Scientists projected to grow by over 20% through 2031, now is a great time to enter the field of data science. The following sections detail general Data Scientist requirements that apply across different industries and types of organizations.

What is a Data Scientist?

Data Scientists extract meaning from raw data to detect patterns and propose solutions that meet an organization’s needs, especially the needs to compete and grow. A Data Scientist’s responsibilities include finding valuable data from data sources, developing machine learning algorithms, improving data collection procedures, cleansing and validating data integrity to ensure accuracy, and detecting patterns and solutions based on data. Data Scientists build models based on data, create data visualizations that communicate patterns and findings to stakeholders, and automate collection processes. Because data plays a critical role in the success of any organization, Data Scientists can build careers in business, technology, finance, nonprofits, and many other industries. 

Those who wish to become a Data Scientist should develop the analytical, statistical, and programming skills needed to manage and interpret raw data. These skills include understanding statistics, machine learning, and reporting tools. Aspiring Data Scientists also benefit from understanding the programming languages R, SQL, Python, Java, and C++. 

Read more about what a Data Scientist does.

Education

If you are interested in a data science career, you might wonder if you need a degree to become a Data Scientist. Data Scientists have a thorough understanding of mathematical and scientific principles including statistics, linear algebra, calculus, statistics, probability, computer science, and certain programming languages. With the rising use of artificial intelligence (AI), Data Scientists should also understand machine learning algorithms and their uses as this branch of AI plays an increasingly crucial role in gathering, sorting, managing, and analyzing data. While most Data Scientists hold a four-year college degree of some kind, often in a subject such as computer science, there are other ways besides university to learn the necessary skills required of a Data Scientist.

You can learn the skills needed to start a career as a Data Scientist by attending in-person or live online data science classes. Such classes may include training on data science fundamentals, the use of data visualization tools, and the use of machine learning. Data Scientists should also understand the programming languages R, Python, Java, and SQL, so classes that provide hands-on experience with these programming languages will greatly benefit aspiring Data Scientists. Some courses focus on specific skills while others, such as certificate courses, cover a range of skills and topics.

Read more about if you need a degree to become a Data Scientist.

Experience

What kind of experience do you need to secure an entry-level Data Scientist position? 

As with most jobs, your ability to perform the tasks and handle the responsibilities of the role matters far more to recruiters than the number of years of experience on your resume. That said, it never hurts to have prior experience on your resume in the form of an internship. You may also wish to highlight aspects of your former career that directly relate to those of a Data Scientist, such as your problem-solving skills, ability to work independently, and your history of accountability.

Internships can do more than simply bolster your resume. They provide first-hand experience applying your data science knowledge to real-world scenarios. Some internships act as a precursor to earning an entry-level position with that employer while others still provide you with a reference and useful industry connections for the future. If you have difficulty finding a remote or local internship opportunity, you can also seek volunteer opportunities to sharpen your skills and contribute to your professional portfolio. 

Skills

The first step to becoming a Data Scientist is to learn the necessary skills for gathering, managing, and interpreting raw data used by organizations to achieve their goals. Data Scientists must develop several mathematical and scientific skills, including a thorough understanding of machine learning and programming languages used in data collection, management, and analysis. Such skills include understanding: 

• SQL
• R programming
• Python
• File management
• Machine learning and algorithms
• Natural language processing
• Linear algebra
• Calculus
• Statistics
• Probability

Data Scientists often create data visualizations to communicate findings to stakeholders, so they must also understand how to use data visualization tools such as Tableau. A successful Data Scientist knows how to work closely with others, focus on details, recognize patterns, and develop solutions to problems. A Data Scientist must also develop the communications skills necessary to relay recommendations and findings to stakeholders.

Read more about what skills you need to become a Data Scientist.

Portfolio

A professional portfolio shows recruiters and prospective employers the evidence of your skills. You can demonstrate your abilities through the work included in your portfolio and even discuss these projects during an interview. This portfolio should highlight your best work and demonstrate your skills as a Data Scientist. You will want to include examples of your code, documentation, and ways you communicate your findings to stakeholders (for example, data visualizations in the forms of graphs and charts).

You want your professional portfolio to highlight your knowledge of: 

• Deep learning
• Computer science
• Programming languages
• Exploratory data analysis

Start by providing a brief statement about who you are, your experience, and what you do. Use this piece to highlight your communication skills, accomplishments, and experience relevant to the position. You may wish to tailor this statement to the position you are applying for.

Next include a few of your best projects that illustrate the range of your skills and your strengths. Avoid overwhelming a recruiter with too much information. Simply select a few pieces that illustrate your abilities. Aim for a balance of code-based and content-based projects. You will also want to ensure these projects stand out from the competition whenever possible.

Finally, make sure your portfolio is easy to navigate and has a professional and appealing design. You will also need to include your contact information to make it easy for recruiters and prospective employers to reach out. 

Resume & LinkedIn Profile

LinkedIn serves as a way to network with industry professionals you have either met in person or who you wish to connect to virtually. LinkedIn can also serve as a virtual resume and portfolio, highlighting your skills, experience, and projects to recruiters and employers. To make the most of your LinkedIn profile, you should spotlight work directly relevant to the job you’re applying for. While a printed resume should not exceed more than 2 pages, LinkedIn has no such limitation and can account for the entirety of your professional career. Review the best practices for creating a LinkedIn data science profile to help you stand out from other applicants.

To land the job you want, it helps to tailor your resume to the position you are applying for. This means tweaking your standard resume to emphasize skills and experience outlined in the job posting. You will also want to do some research on the company or organization you apply to so that you can tailor your resume and cover letter. Also watch for any specific instructions outlined in the job posting, such as a statement or phrase you should include in your cover letter to be considered for the position. If you are enrolled in a data science course or program that includes job search assistance or mentoring, ask a trusted professional to review your resume to ensure it is polished and will stand out from the competition. It is always wise to review tips and best practices for creating a stand-out data science resume before submission.

References

References are professional connections that can vouch for your work ethic positively and at length. Your references need not be professionals who work in data science, though it can help if the reference is connected to the industry you are applying to work in. If you are applying for your first data science job, you may wish to use your data science teacher or mentor as a reference. You can also use past supervisors you had a positive experience with who can speak to your positive qualities as an employee. Coworkers can also serve as references if needed, but only if the coworker has a thorough understanding of the type of work you did and worked closely with you. You should never use friends or family as references as these are not unbiased or professional connections.

Most recruiters or prospective employees will request two or three professional references. You will need to provide their contact information, typically in the form of a phone number and email address. If you are unsure whether the contact information you have for your reference is up to date, try to reach out to them through another means, such as LinkedIn, to confirm that the contact information you have is accurate and up to date. This also allows you to let your reference know a recruiter may reach out to them, helping to ensure your reference connects with the recruiter.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.

Key Takeaways

• Data Scientists extract valuable information from data sets and improve data-collection processes.
• Landing a job as a Data Scientist requires certain skills and expertise.
• Some entry-level Data Scientist positions require no prior experience, but all will require examples of your skills. This is where a professional portfolio is key.
• Your data science portfolio should include a mix of code-based and content-based projects. Make sure to showcase your best work as evidence of your abilities.
• Creating a LinkedIn profile and reviewing your resume will help you stand out from the competition when applying for jobs.
• References are professional connections that can speak to your abilities and experience. References include former supervisors, coworkers, and teachers or mentors, but should never include friends or family.
• The number of years needed to become a Data Scientist depends in part on the learning method you choose.
• You can receive comprehensive training to become a Data Scientist through an in-person or live online course with Noble Desktop.