Data Analyst Requirements

This article will explore the core requirements and skills necessary to secure a job as a Data Analyst. It will also provide a brief overview of what to include in your data analytics resume, portfolio, and LinkedIn profile.

Experience

Like any other profession, most jobs in data analytics require candidates to have some prior experience working in the field. If you are applying for an entry-level Data Analyst position, you must demonstrate to employers that you have worked for up to two years, either as a volunteer or with paid work such as an internship. If you are applying for more advanced data analytic positions, you likely will need to demonstrate a more extended work history, as well as more specific training in the industry you hope to work in, such as healthcare or finance.

Skills

Aspiring Data Analysts must have a variety of both hard and soft skills. The following are some of the hard skills employers seek out in candidates:

• Programming languages, such as R, Python, or SQL.

• Software skills with programs such as Tableau, Microsoft Power BI, or Oracle Database.

• Technical skills like data cleansing, data modeling, and data warehousing.

• Advanced Excel skills that extend beyond basic spreadsheet proficiency.

• Statistical knowledge in topics such as clustering, association rules, and unstructured data concepts.

In addition to hard skills, most employers also seek candidates who have the following soft skills:

• Clear communication is essential to help Data Analysts share findings with internal and external parties.

• Creativity is a must to help Data Analysts convey their findings in a visually engaging, accessible way.

• Teamwork and the ability to collaborate with those who work in various positions within an organization are vital to being a successful Data Analyst.

• Attention to detail helps Data Analysts notice small clues that may otherwise not be apparent, clues that can indicate a more significant trend or pattern that’s hidden in data.

• Problem-solving is another core requirement for any successful Data Analyst to solve data-driven questions.

Portfolio

When applying for a job in data analytics, your portfolio is the first thing an employer will see along with your resume. This is why having a solid and well-rounded data analytics portfolio is essential to gain their interest and showcase your most impressive and relevant work. In addition, there are many benefits to opting for an online portfolio instead of a physical one. Not only are these websites more accessible to everyone within an organization, which is part of the hiring process, but they also can use visuals, hyperlinks, and other content that may not be as easy to include in the print form.

The following are some elements of a strong data analytics portfolio:

• A compelling narrative. Storytelling is a vital skill in data analytics. This field often relies on a candidate’s ability to weave figures, facts, and findings into a story that’s accessible to audience members who don’t come from a technical background. The same is true for a strong portfolio. A narrative backbone can help aspiring Data Analysts show employers that they have not only a command of the data analytic process, but can also present their findings as a concise story that engages with various stakeholders.

• Visuals. Instead of just telling employers what you do, visuals are a great way to engage an audience and bring your portfolio to life. Because data visualization plays an essential role in most data analytics jobs, if you can showcase this skill in your portfolio, you are demonstrating that you are already ahead of the game.

• Include an “About Me” page. Employers want to see more than just good work; they want to get to know the person they are looking to hire. This is why an “About Me” page is helpful. It provides a way to briefly introduce yourself, explain your interest in data analytics, and conclude with a call-to-action where you can invite prospective employers to reach out via email or phone.

• Give your portfolio a central focus. Strong portfolios tend to include a collection of projects or topics of interest. Each example of work included in a portfolio should demonstrate a relevant work skill the employer will value, such as data wrangling or data visualization.

• Keep it simple. While style contributes to a portfolio’s success, don’t overdo it. Layouts that are busy or cluttered, as well as those that attempt to use too many colors, fonts, or visuals, can distract audience members from your samples rather than highlight them. Optimizing white space to guide the eye and clear, consistent headings help create a cohesive, attention-grabbing portfolio.

• Stay to the point. A strong data analytics portfolio should highlight your skills, the services you can offer, the training you’ve received, and the experience you bring to the table. Often, they focus on common business problems that apply to organizations across industries, such as optimizing sales funnels or creating KPI dashboards.

• Let your personality come through. In addition to showcasing your data analytic skills, a strong portfolio also gives employers a glimpse into your personality. Elements such as word choice, graphics, and other stylistic touches can help you stand out from other candidates by showing a more personal brand that you have created and can bring to the organization.

Resume & LinkedIn Profile

Having a solid resume and an up-to-date LinkedIn page are two essential elements for securing a job as a Data Analyst.

Although there are many ways to structure a data analytics resume, the following are some of the sections you may consider including to highlight your skills and training:

• Technical skills include trend identification, data visualization, and database design/management.

• Research skills, like data mining, focus groups, or data science research methods.

• Computer science skills, such as Python, R, system administration, or advanced Excel.

• Work experience includes any relevant internships, volunteer, or paid positions, as well as a brief description of each.

• Education, such as college attended and degree earned.

• Professional certifications or certificates that further illustrate your commitment to learning data analytics.

Learn More About Data Analytics by Enrolling in Hands-On Classes

If you’re interested in learning more about how to analyze and visualize data, Noble Desktop’s Python for Data Science Bootcamp is a great starting point. This intensive, 30-hour course covers core Python skills that are useful for the data sciences, such as an overview of the various data types and how to create data visualizations. Noble also offers an 18-hour SQL Bootcamp in which students learn how to filter data, write SQL queries, and gather insights from data.

For those looking to learn specifically about data analytics, courses such as the Data Analytics Certificate or Data Analytics Technologies Bootcamp are available in-person in NYC, as well as in the live online format. These rigorous learning options cover core data analysis tools like SQL, Excel, and Tableau, among others.

You can also search for live online and in-person data analytic courses close to home by using Noble’s Classes Near Me tool. More than 340 courses are currently listed by Noble and other educational providers.