If you’re currently searching for a job in data analytics, it may seem overwhelming to know how to navigate thousands of listings and find the job that’s right for you. This article will cover some tips to help with the job search, as well as what employers will be on the lookout for when screening candidates.
Tips & Best Practices for Searching for a Job as a Data Analyst
Organizations around the world are currently working with more data than ever before, meaning they need individuals who can transform these numbers into information and insights. If you’re considering a job in data analytics, now is a great time to apply. Employers are actively seeking candidates who can apply their math, statistics, communication, and database construction skills to unearth trends and other pertinent information from the data to build a more robust business model. Data Analysts are currently in high demand across industries, particularly in financial institutions and market research firms.
Yet even with the need for qualified Data Analysts, it can still be challenging to land a job in this field. The following tips are provided to help with the job search and ensure that once you’ve found a position that’s a good match, you’re as prepared as possible for the job interview:
- Know the core data analytic skills. Most employers want to hire candidates who already know how to use programs, applications, and tools common to the field of data analytics. Here are a few data analytic tools to make sure you know when applying for jobs:
- Programming languages like Python and R
- Zoho Analytics
- Data warehousing
- Network. Even in a strong job market, those who can connect with others in the data analytics industry will likely find employment opportunities that may otherwise not be publicly advertised. Networking takes many forms beyond in-person meetings, such as industry events, coursework in data analytics, and connecting with peer groups on social media platforms such as LinkedIn or Twitter.
- Have a strong portfolio. Employers often ask to see your professional data analytics portfolio, which can be on paper or online. Showcasing your experiences working with big data by selecting specific projects and examples is a great way to demonstrate that you have real-world data analytics experience. Some candidates prefer the convenience and accessibility offered by an online portfolio, which can contain links to pertinent projects, an “About Me” section, and your up-to-date resume.
- Be practical in your approach. While most employers value a solid background in data analytics theory, they are likely to be even more interested in seeing how you have applied theory to real-world data scenarios. It’s essential, therefore, to be ready to describe how a specific theory can be used within a business context to affect real, tangible change. This will indicate that you have the necessary experience to ground abstract concepts into action.
- Consider finding a mentor. If you’re looking to start a career in data analytics but haven’t worked in this field before, connecting with a mentor has many benefits. Those already working in this field have likely been in your position and can offer insights into what you can expect on the path, as well as how best to prepare to be a successful Data Analyst. They also have industry experience and can guide you on what skills you should have, what requirements jobs in this field demand, and even help you avoid mistakes they may have made on their own path. Some people have friends or family members who work as Data Analysts and can serve as mentors. If you don’t, there’s no need to worry. Educational providers such as Noble Desktop offer students one-on-one mentoring in their Data Analytics Certificate program.
- Establish an online presence. Many employers look at an applicant’s presence on social media and other online platforms to better understand their personality and qualifications. An estimated 75% of talent scouts search on LinkedIn to spot candidates who are qualified for the open position. Keep your LinkedIn profile current, with your job experience and contact information readily available. Suppose you use social media platforms such as Twitter solely for professional purposes, such as to post about data analytics-related topics. In that case, this is another good way to showcase your passion for working with data. In addition, creating a Github page is another platform you can use to share your data analytics projects with employers.
- Prepare for your interview. Being ready to speak about your qualifications, training, technical skills, and soft skills at a job interview is crucial to landing a job in data analytics. In addition to being prepared to answer questions about yourself and your qualifications at the interview, also come ready with questions of your own. You want to demonstrate to the hiring committee that you know more than the job listing; show them you’ve done your research and know about the company’s background, its relationship with data, and its corporate culture. You may even consider reaching out to HR to get a copy of job description hierarchies or an organizational chart so that you can ask specific questions about the work environment.
- Also, be prepared to answer questions that extend beyond your technical expertise and work experience. It’s not uncommon for interviewers to also inquire about your work style or behavioral traits. They may, for example, want to know how you navigate disagreements or conflicts or ask how you handled challenges that arose at your last place of employment.
- Don’t get overwhelmed with the process. Searching for a new career can be a stressful, time-intensive undertaking. Balancing a current job, family, and other commitments can be a difficult juggling act when also applying for a new position. That’s why taking it slowly and setting manageable goals is essential. Instead of trying to apply for ten positions in one sitting, you may consider spending a day researching available positions, another day prioritizing which are the best match for you and why, and then applying to these positions first. Having a portfolio, current resume, and basic job interview preparation can help streamline this process when fitting jobs become available.
- Exude confidence. There are many ways to show potential employers that you’re confident in your skills. This can entail researching websites such as Salary.com so that you have a concrete number range for salaries in the industry to which you are applying, as well as pay rates proportional to the location. You can also convey confidence by spending time researching the company you are applying to so that you have thoughtful questions to ask about their operations and culture. The bottom line is that the more genuine confidence you convey to employers, the more likely they will be to feel confident in hiring you.
Learn More About Data Analytics by Enrolling in Hands-On Classes
Data analytics is currently one of the most in-demand professions across the U.S. If you’re interested in learning how to analyze and visualize data, Noble Desktop has several course options available. Noble’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.
If you’re looking to find learning options close to home, you can also search for data analytic courses in-person or live online with the help of Noble’s Classes Near Me tool. Over 340 courses are currently listed by Noble and other top educational providers in topics such as data visualization, business intelligence, and data analytics, among others.