Experts in data organization, analysis, and utilization help shape conclusions and direct corporate choices. To be successful in their positions, these analysts need to possess both technical and interpersonal abilities. Finding out more about the essential competencies for the position might help you decide which of your own skills to hone if you’re a data analyst already or wish to become one.

In this post, we define a data analyst and go over a list of the most critical competencies that are necessary for their success.

A data analyst retrieves, organizes and analyzes information to help a company improve production, efficiency or another valuable aspect of its operations. Data analysts work in all industries, from retail to academia. Often, they use the data they gather to create reports that summarize patterns and offer solutions to stakeholders in the form of updated systems, procedures or protocols. Companies use data analysts specifically to improve outcomes like productivity or profits or to understand employee or customer needs.

Common responsibilities for data analysts include:

• Finding patterns in data
• Writing reports
• Collaborating with stakeholders
• Presenting their findings
• Creating systems

Data analysts benefit from developing certain skills. Here are some of the top skills for data analysts to acquire for success in their field:

Technical skills

These are some technical skills for data analysts:

• 1. SQL

Structured Query Language, or SQL, is a spreadsheet and computing tool capable of handling large sets of data. It can process information much more quickly than more common spreadsheet software. SQL is a valuable tool for data analysts, so knowledge of its functionality is key. Data analysts can also work with NoSQL, which is a similar database that scales horizontally instead of vertically.

• 2. Spreadsheets

While SQL is often the preferred software for data analysts, knowledge and understanding of traditional, widespread spreadsheet tools is necessary. Some companies may prefer reports or data sets presented through traditional spreadsheet tools. Data analysts may use programs like Microsoft Excel, Quip, Zoho Sheet or WPS Spreadsheets.

• 3. Statistical programming languages

Some data analysts choose to use statistical programming languages to analyze large data sets. Data analysts are familiar with a variety of data analysis programs to prepare them for the tools their company has available. These are some languages you can use as a data analyst:

• R: This language is especially helpful for statistical computing.
• Python: Data analysts can use Python to make visualizations of data and build machine learning algorithms.
• Java: Java is an object-oriented language that can help create software that’s compatible with many devices and platforms.
• Scala: This language uses functional programming and object-oriented programming to help create programs that are free of bugs.
• MATLAB: Using MATLAB allows analysts to plot data and implement new algorithms.

• 4. Data visualization

After gathering, analyzing and compiling the data, analysts share their findings with the company. Creating an easy-to-understand data analysis visualization is vital. Often, data analysts use visuals like graphs or charts to help their colleagues understand what the data shows quickly and clearly.

• 5. Machine learning

Some companies that use highly technical machines to complete tasks require their data analysts to have knowledge of how machine learning works. Machine learning, or artificial intelligence, is the use of algorithms and data to imitate human productivity. It’s growing as technology advances, so it’s wise for data analysts to develop this skill.

• 6. Data warehousing

Data warehousing is the creation of virtual storage and organization systems for a company’s data. Often, they’re managed by data analysts whose main job is to monitor the data and maintain safeguards and access. Data warehouses help with management, which allows companies and organizations to collect, organize, protect and use data efficiently.

• 7. Data prep

Data preparation, or data prep, is converting a data set from a large batch of information into a well-sorted and easy-to-understand report. This can include identifying errors or duplicate entries and remedying them prior to date analysis. They may also complete data cleaning to ensure information is streamlined. Data analysts usually complete data prep before data visualization so that the most important information and takeaways are easy to convert into a graph or chart.

Analytical skills

These are some skills that can help with data analytics:

• 8. Critical thinking

Companies often task data analysts with both collecting data and interpreting the data for a specific purpose. Knowing what data to collect and how to process it to obtain the right information is a critical thinking skill that’s vital for data analysts to develop.

• 9. Problem-solving

Data analysts often encounter technical issues while completing their job-related duties. Establishing effective solutions for stakeholders through problem-solving is a necessary skill.

• 10. Attention to detail

Data analysis requires precise focus. Analyzing code or other technical details involves reading and assessing intricate coding or technical structure. Close attention to detail is a vital skill for data analysts to ensure they sort and analyze code correctly.

• 11. Statistics

Data analysts use statistics to assign meaning to large data sets. Statistics help data analysts understand large sets and find useful, relevant information to apply to the issue facing the company.

• 12. Domain knowledge

Domain knowledge for data analysts refers to the domain in which the data they’re assessing lives. Sometimes the data’s domain can offer useful insights into the data and provide analysts with helpful information with which they can more easily deconstruct and make sense of the data.

Communication skills

These are some communication skills data analysts can use:

• 13. Public speaking

Presenting data analysis is an important part of a data analyst’s job. Besides compiling the findings in a clear manner, data analysts also explain both verbally and in writing why the data is important and what the company can do to respond to the findings.

• 14. Verbal communication

Data analysts communicate with a variety of people during the course of their work, including other data analysts, IT representatives and colleagues not directly related to data or technology. They can use verbal communication to explain complex ideas and present information in a way that common viewers can understand.

• 15. Collaboration

Collaboration with internal and external stakeholders is a frequent responsibility for data analysts. Respect and appreciation for teamwork help data analysts and other members of the company’s team complete their work.

Industry-specific skills

These are some business and industry knowledge skills data analysts can use:

• 16. Writing

While data analysts primarily work with numbers, creating written reports is an important part of their job responsibilities. Strong writing skills help data analysts clearly and accurately explain their findings in written reports.

• 17. Project management

Project management involves overseeing and tracking all elements of a project, including the team members and their work. Some data analysts regularly manage a group of other analysts or information technology staffers.

• 18. Research

Sometimes data analysts may conduct research. They may determine solutions for technical problems or evaluate new processes based on data analysis. Both of these tasks require research for information or possible solutions.

How Can I Get Hired as a Data Analyst?

Numerous businesses offer data analytics careers, and there are multiple ways to get your first position in this in-demand industry. Here are some stages to becoming a data analyst, regardless of whether you’re just starting out in the workforce or changing careers.

1. Get a foundational education.

If you’re new to the world of data analysis, you’ll want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.

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It used to be that most entry-level data analyst positions required a bachelor’s degree. While many positions still do require a degree, that’s beginning to change. While you can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field, you can also learn what you need through alternative programs, like professional certificate programs, bootcamps, or self-study courses.

2. Build your technical skills.

Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.

• Statistics
• R or Python programming
• SQL (Structured Query Language)
• Data visualization
• Data cleaning and preparation

Take a look at some job listings for roles you’d like to apply for and focus your learning on the specific programming languages or visualization tools listed as requirements.

In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills—you may be asked to present your findings to those without as much technical knowledge—problem solving ability, and domain knowledge in the industry you’d like to work.

3. Work on projects with real data.

The best way to learn how to find value in data is to work with it in real world settings. Look for degree programs or courses that include hands-on projects using real data sets. You can also find a variety of free public data sets you can use to design your own projects. 

Dig into climate data from the National Centers for Environmental Information, delve deeper into the news with data from BuzzFeed, or come up with solutions to looming challenges on Earth and beyond with NASA open data. These are just a few examples of the data out there. Pick a topic you’re interested in and find some data to practice on.

4. Develop a portfolio of your work.

As you play around with data sets on the internet or complete hands-on assignments in your classes, be sure to save your best work for your portfolio. A portfolio demonstrates your skills to hiring managers. A strong portfolio can go a long way toward getting the job.  

As you start to curate work for your portfolio, choose projects that demonstrate your ability to:

• Scrape data from different sources
• Clean and normalize raw data
• Visualize your findings through graphs, charts, maps, and other visualizations
• Draw actionable insights from data

If you’ve worked on any group projects through the course of your learning, consider including one of those as well. This shows that you’re able to work as part of a team.

If you’re not sure what to include in your portfolio (or need some inspiration for project ideas), spend some time browsing through other people’s portfolios to see what they’ve chosen to include.

5. Practice presenting your findings.

It can be easy to focus only on the technical aspects of data analysis but don’t neglect your communication skills. A significant element of working as a data analyst is presenting your findings to decision-makers and other stakeholders in the company. When you’re able to tell a story with the data, you can help your organization make data-driven decisions.

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability.

As you complete projects for your portfolio, practice presenting your findings. Think about what message you want to convey and what visuals you’ll use to support your message. Practice speaking slowly and making eye contact. Practice in front of the mirror or with your classmates. Try recording yourself as you present so you can watch it back and look for areas to improve.

6. Get an entry-level data analyst job.

After gaining some experience working with data and presenting your findings, it’s time to polish your resume and begin applying for entry-level data analyst jobs. Don’t be afraid to apply for positions you don’t feel 100 percent qualified for. Your skills, portfolio, and enthusiasm for a role can often matter more than if you check every bullet item in the qualifications list.

If you’re still in school, ask your university’s career services office about any internship opportunities. With an internship, you can start gaining real world experience for your resume and apply what you’re learning on the job.

7. Consider certification or an advanced degree.

As you move through your career as a data analyst, consider how you’d like to advance and what other qualifications can help you get there. Certifications, like the Certified Analytics Professional or Cloudera Certified Associate Data Analyst, might help qualify you for more advanced positions at higher pay grades.  

If you’re considering advancing into a role as a data scientist, you may need to earn a master’s degree in data science or a related field. Advanced degrees are not always required, but having one can open up more opportunities.

What Recruiters Look for Data Analysts?

There are four main areas to concentrate on when it comes to the technical side of being a data analyst. These skills are the nuts and bolts that allow your data analyst to navigate the data landscape effectively. 

1. Working knowledge of data analysis tools

Your data analyst should be well-versed in using software and tools specifically designed for data analysis. For instance, your company could be using a Vonage VoIP for a small business system that generates a wealth of data on call volumes, call durations, and customer interactions. 

Familiarity with tools like Excel, Python, R, or specialized software like Tableau is essential to uncovering insights. These insights can go on to drive significant positive results for your business. For example, by adopting Tableau, PepsiCo was able to reduce the time it takes to produce reports by up to 90%. 

Data analysis tools help in cleaning, processing, and transforming raw data into meaningful insights. For instance, when dealing with sales data, proficiency in tools like Excel can help identify trends and patterns in revenue generation.

2. Programming skills

Programming skills are the coding language that data speaks. A competent data analyst should have a working knowledge of programming languages like Python or R. They can perform sophisticated data manipulation and statistical analysis thanks to these languages. 

For instance, when analyzing customer data for an e-commerce business, programming skills enable the automation of repetitive tasks, such as calculating purchase trends.

3. Database management

Databases are like the library of your organization’s data. Data analysts need to be adept at managing and querying data from various databases. Knowledge of SQL (Structured Query Language) is invaluable here, as it helps retrieve specific data from large datasets efficiently. 

For example, when working with customer databases, a data analyst may use SQL to extract information about customer demographics and preferences.

4. Data visualization expertise

Data visualization is the art of turning numbers and statistics into visually appealing and understandable graphics. A proficient data analyst should be skilled in creating charts, graphs, and interactive dashboards. 

Tools like Tableau, Power BI, or even Python libraries like Matplotlib and Seaborn come in handy here. When presenting quarterly sales reports to a team, data visualization expertise makes it easier for everyone to grasp the key insights at a glance.

Note that recruitment tech like applicant tracking systems (ATS) can efficiently source and filter candidates based on specific criteria, including data visualization expertise. These systems can help you find data analysts with relevant skills and experience in this specific area, allowing you to narrow down your pool of candidates to the best ones.

Conclusion

Given the growing importance of big data, it is critical for companies in all industries to collaborate with a qualified data analyst. A comprehensive data analyst ought to exhibit a unique blend of skills, expertise, and background. Make the best decision possible when hiring tech talent for your company by using the above-mentioned advice.

Keep in mind that hiring the best data analysts is an investment in your future success rather than merely filling a position.