This ability can help organizations make informed decisions and implement effective solutions. Ultimately, this process leads to improved business operations and outcomes. This guide will explore the key data analyst skills you should look for when hiring for your next data project.
Roles and Responsibilities of a Data Analyst
Data Cleaning and Preparation: The Foundation of Data Analysis
Data cleaning and preparation involves filtering the data, handling missing values, and preparing the dataset for analysis to ensure accuracy and relevance. This process eliminates duplicate entries and corrects inconsistencies to create a stable dataset for analysis.
Data Exploration and Analysis: Unearthing Valuable Insights
Data exploration and analysis involves using statistical tools and techniques to explore and analyze the cleaned dataset. Analysts identify patterns, relationships, and trends in the data that uncover valuable insights.
Data Visualization: Making Sense of Complex Data
Data visualization is creating visual representations of data findings through charts, graphs, and dashboards. These visual aids make the data understandable for stakeholders, who may not have the technical background to interpret complex data.
Reporting: Communicating Findings to Decision Makers
Data analysts prepare reports and presentations to communicate the insights and findings from the data to stakeholders. These reports can influence policy and decision-making processes.
Collaboration: Working With Others to Achieve Business Goals
Data analysts often work with other departments to understand their data needs and help them make informed decisions based on data insights.
20 Data Analyst Skills Needed for Hiring
1. SQL: The Essential Tool for Data Analysts
Structured Query Language, or SQL, is a pivotal tool for data analysts. It is a computing language designed to help users manage and organize information stored in a relational database. SQL can quickly sift through large data sets to retrieve specific information, saving analysts valuable time.
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 conventional spreadsheet tools. Data analysts may use programs like Microsoft Excel, Quip, Zoho Sheet, or WPS Spreadsheets.
2. Programming Languages: Python, R, and SQL
Programming languages like Python, R, and SQL are indispensable in data analytics. These languages allow you to manipulate data, perform statistical analyses, and create data visualizations. Python. Widely used for data manipulation and analysis, Python boasts a rich ecosystem of libraries like Pandas and NumPy. R. Specialized for statistical analysis, R is another powerful tool often used in academic research and data visualization. SQL. The go-to language for database management, SQL allows you to query, update, and manipulate structured data.
3. Spreadsheets: The Old Reliable for Data Analysts
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 conventional spreadsheet tools. Data analysts may use programs like Microsoft Excel, Quip, Zoho Sheet, or WPS Spreadsheets.
4. Data Visualization: A Top Skill for Data Analysts
As the term suggests, data visualization is a person’s ability to present data findings via graphics or other illustrations. The purpose of this is simple: It facilitates a better understanding of data-driven insights, even for those who aren’t trained in data analysis.
With data visualization, data analysts can help a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance. This capability allows you — the data analyst — to better understand a company’s situation, convey valuable insights to team leaders, and even shape company decision-making for the better.
Data visualization allows you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”
Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” The number one skill reported by participants? You guessed it: data visualization.
5. Data Cleaning: An Essential Task for Data Analysts
As any Marie Kondo aficionado will tell you, cleaning is invaluable to achieving success — and data cleaning is no different! It’s one of the most critical steps in assembling a functional machine-learning model and often comprises a significant chunk of any data analyst’s day.
“Although we often think of data scientists as spending most of their time tinkering with ML algorithms and models, the reality is somewhat different,” tech writer Ajay Sarangam notes for Analytics Training. “Most data scientists spend around 80 percent of their time cleaning data. Why? Because of a simple truth in ML: Better data beats fancier algorithms.”
With an adequately cleaned dataset, even simple algorithms can generate remarkable insights. Conversely, uncleaned data can produce misleading patterns and lead a business toward mistaken conclusions. By necessity, data analyst qualifications require proper data cleaning skills — and there are no two ways around that.
6. MATLAB: The Big Data Tool for Data Analysts
MATLAB is a programming language and multi-paradigm numerical computing environment that supports algorithm implementation, matrix manipulations, and data plotting, among other functions. Businesses interested in big data have begun turning to MATLAB because it allows analysts to drastically cut down on the time they usually spend pre-processing data and facilitates quick data cleaning, organization, and visualization. Most notably, MATLAB can execute any machine learning model built in its environment across multiple platforms.
Understanding MATLAB isn’t required for data analysts; however, given its wide-reaching applications and usefulness, a working understanding of the environment can boost your marketability to employers.
7. Machine Learning: An Emerging Skill for Data Analysts
Some companies that use highly technical machines to complete tasks require their data analysts to know how machine learning works. Machine learning, or artificial intelligence, uses algorithms and data to imitate human productivity. It’s growing as technology advances, so it’s wise for data analysts to develop this skill.
8. Statistical Analysis: The Foundation of Data Analytics
Statistical analysis is the backbone of data analytics, providing the methodologies for making inferences from data. Understanding statistical methods allows you to use the following:
Descriptive statistics
Summarize and interpret data to provide a clear overview of what the data shows.
Inferential statistics
Make predictions and inferences about a population based on a sample.
Hypothesis testing
Evaluate theories or hypotheses to solve practical problems.
9. Linear Algebra and Calculus: Essential Math Skills for Data Analysts
When it comes to data analytics, having advanced mathematical skills is non-negotiable. Some data analysts even major in mathematics or statistics during their undergraduate years just to better understand the theory underpinning real-world analytical practice!
Two specific fields of mathematical study rise to the forefront of analytics: linear algebra and calculus. Linear algebra has machine and deep learning applications that support vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.
However, you may not need a robust theoretical background before pursuing real-world applications. Some in tech suggest taking the opposite track. For example, in the 2019 article “Mathematics for Data Science,” Data Science writer and data analyst Ibrahim Sharaf El Den advised taking a top-down approach.
“Learn how to code, learn how to use the PyData stack (Pandas, sklearn, Keras, etc..), get your hands dirty building real-world projects, use library documentation and YouTube or Medium tutorials,” he explains. “You’ll start to see the bigger picture, notice your lack of theoretical background, to actually understand how those algorithms work […] studying math will make much more sense to you!”
That said, there is no correct way to become a data scientist. Explore and find an educational route that works for you!
10. Microsoft Excel: The Surprisingly Important Tool for Data Analysts
Stressing the importance of Microsoft Excel skills is laughable when considering the significantly more advanced technology data analysts have at their disposal. To borrow a quote from Irish business writer Anne Walsh, “Mention Excel to techies, and it’s often dismissed with a sniff.”
And it’s true — Excel is clunky in comparison to other platforms. Yet Microsoft’s workhorse spreadsheet platform is used by an estimated 750 million people worldwide. “Excel skills” frequently appear under the qualifications section for jobs posted on hiring services like Indeed or Monster. For all its apparent low-fi capabilities, Excel is well-used among businesses.
Moreover, Excel well, excels is automating certain features and commands for better data analysis. Excel has its own programming language, VBA, which it uses to create macros or pre-recorded commands. When deployed correctly, VBA can save human analysts a lot of time on frequently performed, repetitive projects such as accounting, payroll, or project management.
Microsoft also developed its Analysis ToolPak with statistical modeling and data analysis in mind. As the company’s help center explains, “You provide the data and parameters for each analysis, and the tool uses the appropriate statistical or engineering macro functions to calculate and display the results in an output table. Some tools generate charts in addition to output tables.”
11. Data Warehousing: An Important Skill for Data Analysts
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.
12. Data Prep: The First Step in Data Analysis
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.
13. Critical Thinking: An Important Skill for Data Analysts
It’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights and information from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.
Becoming a critical thinker can be difficult, but challenging yourself can hone such skills. The next time you face an analytical task or exercise, try to think — what is the meaning behind the pattern I see? What does the data say about what has been accomplished? What shortfalls does it indicate? Don’t overlook the importance of honing your critical thinking skills when you prepare for a career in data analytics.
14. Communication: Explaining Data Analysis to Others
You need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.
Being a good data analyst effectively means becoming “bilingual.” You should be able to address highly technical points with your trained peers and provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.
15. Problem Solving: An Important Skill for Data Analysts
Data analysts often encounter technical issues while completing their job-related duties. Establishing effective solutions for stakeholders through problem-solving is a necessary skill.
16. Attention to Detail: Why Data Analysts Can’t Overlook Errors
According to a Gartner report, poor data quality can cost organizations an average of $12.9 million annually, highlighting the importance of attention to detail.
In data analytics, even a tiny error can lead to incorrect conclusions. Attention to detail is critical for:
Data cleaning
Ensuring the data you work with is accurate and free from errors.
Quality assurance
Double-check your analyses and visualizations for accuracy.
Documentation
Keeping thorough records of your data sources, methodologies, and code.
17. Domain Knowledge: Understanding the Industry Relevant to Your Data
Domain knowledge for data analysts refers to the domain in which the data they assess lives. Sometimes, the data’s domain can offer valuable insights into the data and provide analysts with helpful information that they can use to deconstruct and make sense of the data more efficiently.
18. Public Speaking: Presenting Data Analysis Findings
Presenting data analysis is an integral part of a data analyst’s job. Besides clearly compiling the findings, data analysts also explain verbally and in writing why the data is essential and what the company can do to respond to the findings.
19. Big Data Technologies: An Emerging Skill for Data Analysts
Data from Statista shows that the big data market was expected to grow from $42 billion in 2018 to $103 billion by 2027, emphasizing the increasing need for skills in big data technologies.
Big data technologies like Hadoop and Spark are becoming increasingly important as data grows in volume and complexity. These technologies allow you to work on the following:
Data storage
Handle large datasets that are beyond the capacity of traditional databases.
Data processing
Perform complex computations and analyses on big data.
Real-time analytics
Analyze data in real-time to make immediate business decisions.
20. Collaboration: Working Well with Others in Data Analytics
Collaboration with internal and external stakeholders is a frequent responsibility for data analysts. Respect and appreciation for teamwork help data analysts and other company team members complete their work.
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How to Hire a Data Analyst in 6 Simple Steps
1. Use Azulity
Azulity specializes in healthcare master data management and provider credentialing services. They have a track record of implementing data solutions and credentialing across the US. Their platform helps organizations eliminate the costly problems of fragmented data systems. Key features of their services include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. Azulity serves healthcare technology leaders — from CIOs and CDOs to VPs of data platforms and credentialing.
2. Create your data-analyst job profile
Prior to writing your data analyst job description, create a job profile. This will help you identify what the right candidate will look like and streamline your hiring process.
Sit down with the hiring team and other relevant stakeholders to determine what objectives hiring a data analyst will help the business achieve, which projects or areas they will work in, and the impact you expect them to make.
Since data analysts have varied backgrounds and expertise in different sectors/industries, try to be specific when creating your data analyst profile.
Based on this profile, you can define the requirements, experience, and skills your prospective employee will need.
Finally, set a salary level and include benefits, bonuses, and company perks.
3. Draft and publish your job description
Once you’ve created a data analyst profile, it’s time to make a job description and publish it. Remember to include:
- An informative job title
- An overview of the role
- An introduction to your company
- Details about working conditions
- Information about the relevant skills, competencies, and experience you require for the role
- Information about compensation, company, benefits, and perks
- Details about the hiring process
When you’re ready to publish, remember to post the job description on LinkedIn, GitHub, TopTal, and Dataspace. You’ll also want to post the job to your employee referral program and careers website.
4. Evaluate applicants with a data-analyst skills assessment
Soon after you publish your job description, you’ll begin receiving applications. From here, you’ll need to start evaluating applicants’ skill sets.
However, evaluating every applicant’s resume manually – or even using resume-screening tools – is time-consuming and can lead to biased candidate selection.
Innovative businesses implement skills-based hiring practices to find candidates and minimize time-to-hire metrics. These involve defining a role’s specific skills and screening candidates based on whether they have those skills.
This will help you identify the right data-analyst candidates from the outset and reduce the time you spend considering applicants who don’t meet your requirements.
To support skills-based hiring practices, you’ll need a robust tool that can help you evaluate candidates’ technical and soft skills. With TestGorilla, you’ll find it easy to start creating skills assessments for your data-analyst candidates to ensure they fit your profile.
5. Analyze assessment results
After all your candidates have completed the assessment, you can begin analyzing the results and identifying which candidates to interview.
TestGorilla ranks your candidates from high to low to make this process more efficient based on their average scores.
Additionally, since an assessment comprises several tests, our analysis provides granular insights to help you judge which elements or areas are most important.
Finally, you can summarize each candidate’s evaluation by assigning them a final star rating and recording your notes. From here, you’re ready to narrow the list of candidates who will move on to the interview process.
6. Develop solid interview questions and schedule interviews
Once you’ve analyzed the skills assessment results and identified a shortlist of applicants best suited for your data analyst role, you’ll need to prepare interview questions to help you glean more information about each candidate.
To help you further evaluate candidates’ knowledge, experience, and skills, here are six data-analyst interview questions you could ask during interviews.
What are the different types of hypothesis testing?
Candidates should be able to relay the two main types of hypothesis testing that can accept or reject statistical hypotheses.
The first is a null hypothesis, meaning there is no relation or statistical significance between data sets or variables. The second is an alternative hypothesis, which means that there is some relation between sets of data or variables.
What are the different types of sampling techniques used by data analysts?
Candidates should be able to name the sampling techniques used to select, manipulate, and analyze a subset of data points to reveal meaningful insights.
Candidates should mention the following sampling methods:
- Simple random sampling
- Cluster sampling
- Stratified sampling
- Systematic sampling
- Judgmental or purposive sampling
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Where to Find Data Analysts for Hiring
1. Job Boards and Platforms: Where to Look for Data Analyst Candidates
Job boards are a traditional yet effective way to find data analysts. Most job boards allow you to post a job listing and search for data analyst candidates actively seeking roles.
General Job Boards
Platforms like LinkedIn, Indeed, and Glassdoor are popular options for hiring data analysts.
Tech-Specific Job Boards
Sites like Dice, AngelList (for startups), and Turing are better for finding tech talent, including data analysts.
2. Freelance Platforms: Find Short-Term Help for Data Projects
If you need to hire a data analyst for a short-term or project-based assignment, consider freelance marketplaces:
- Upwork (upwork.com)
- Toptal (toptal.com) (for pre-vetted high-quality freelancers)
- Fiverr (fiverr.com)
3. Networking and Referrals: Tap into Your Professional Network
When looking for data analysts, networking can help you find qualified candidates who may not be actively looking for a job.
Professional Networks
Tap into professional associations like the International Association for Statistical Education (IASE) or local meetups.
Referrals
Ask within your network or company for recommendations. Employees can often refer strong candidates.
4. Academic Institutions: Hire Entry-Level Talent or Interns
Universities are great places to find data analysts who are just starting and can bring fresh skills and ideas to your organization.
University Career Centers
Partner with universities that offer data science, statistics, or analytics programs.
Internship Programs
Offer internships or entry-level positions to fresh graduates.
5. Social Media and Online Communities: Explore Groups for Data Analysts
You can also find data analysts on social media platforms and online communities.
LinkedIn Groups
Join and post in groups like “Data Science Central” or “Analytics Vidhya.”
Reddit Communities
Post in subreddits like r/datascience or r/forhire to find candidates.
Slack/Discord Communities
Engage with communities like DataTalks.Club or local data science groups.
6. Recruitment Agencies: Use Specialized Hiring Firms
Recruitment agencies can help you find data analyst candidates quickly. Use specialized agencies like Robert Half, Hays, or Michael Page with expertise in tech talent acquisition.
7. Hackathons and Competitions: Find Skilled Analysts
Platforms like Kaggle and Topcoder host competitions for data analysis, which can help identify skilled analysts.
8. Internal Talent Development: Hire from Within
Instead of hiring a new data analyst, consider upskilling your current employees with training in data analysis tools like Python, R, SQL, and Tableau.
Azulity specializes in healthcare master data management and provider credentialing services, bringing proven expertise in implementing healthcare data solutions and credentialing across the US. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments.
Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!
Book a Call to Learn More About Our Provider Credentialing Services
Azulity specializes in healthcare master data management and provider credentialing services. We bring proven expertise in implementing healthcare data solutions and credentialing across the US. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments.
Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!
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