Data analysts are highly qualified professionals who gather and organize data from a variety of sources to extract relevant conclusions. This is a sought-after role for new graduates and for professionals looking to change their career path. Let’s examine why this role is so important in the current and future economy.
Data is the New Business Currency
Computing pushed the economy into the age of data. Data is king and it’s propelling industries and markets forward. Now that computers are an integral part of business interactions, companies are sitting on mountains of data which they desperately need analysts to interpret for them.
A data analyst can draw important conclusions from a data set and, as a result, companies are given the opportunity to make better-informed decisions. For example, a company can use analysts’ conclusions to develop a highly targeted marketing strategy or to receive insights into why specific products or services are performing poorly.
Education is transforming to meet the demands of the data economy, providing more data-literate graduates than ever before. These graduates are becoming data analysts, data engineers, database managers, data architects, and data scientists – jobs which vary in their day-to-day operations but which all revolve around data.
In this article, we’re delving into everything about the role of a data analyst!
Four Types of Questions Answered by Data Analysts
Data analysis can be broken down into four key questions. These questions and the answers they bring up are used to fully equip a business for data-driven decisions.
Question 1: What happened in the past?
Descriptive analytics will look at data from the past in order to spot trends. Examples of data like this include website traffic, quarterly sales, monthly revenue, and weekly expense reports.
Question 2: Why did something happen?
Diagnostic analytics will look for patterns in the descriptive data to make sense of why positive or negative outcomes occurred. For example, if monthly revenue has been going down for the last six months, diagnostic analytics will look at other metrics to help identify why.
Question 3: What is going to happen?
Predictive analytics will often combine diagnostic and descriptive analytics to predict likely future results based on past data. If monthly revenue is going down every month and diagnostic analytics point to a severe staffing shortage as the cause, then predictive analytics will likely predict a further revenue drop unless this issue is set to be resolved.
Question 4: What is the next plan of action?
Prescriptive analytics will identify the actions a business or client should take next. This is usually the most significant and valuable type of analytics for a client. A severe staffing shortage, for example, has a direct solution that would likely be prescribed by an analyst. However, real-world situations are usually more complex. Companies aim to stay on top of trends and quickly address any problems before they get worse.
The Day-to-day Responsibilities of a Data Analyst
In order to learn how to become a data analyst, let’s explore the four main responsibilities of the job:
- Setting up data collection infrastructures and collecting data
- Analyzing the data
- Creating reports
- Collaborating with other departments and roles
Setting Up Data Collection Infrastructures and Collecting Data
Even though companies are constantly collecting data, it isn’t always easily accessible. The available data may be missing a very important or useful metric. A new project can require the creation of a unique data collection infrastructure in order to meet a project’s specific needs. A data analyst may collaborate with a web developer at this stage to help optimize the data collection.
A data analyst may also be required to collect data beyond their company’s proprietary database, as outside information may be relevant. Gathering these datasets can be time-consuming. However, once a general data collection infrastructure is in place, this process takes up less of the analyst’s time.
Some tasks that fall within data collection include:
- Developing and maintaining a data collection system and databases
- Mining data from within internal systems and from secondary sources
- Identifying areas of data collection where efficiency can be increased by implementing automation
- Developing and maintaining automated processes
Analyzing the Data
Once the data is collected, the data analyst is then responsible for cleaning and processing the data.
Cleaning data involves reviewing the data collected by the infrastructure and making vital decisions about which pieces of data are extraneous and which are not. When combining multiple data sources, data can often be duplicated or incorrectly labeled. The data analyst must look out for these errors and remove them to avoid bad data affecting the outcomes of the analysis.
When cleaning data, duplication is usually the biggest part of the process. For example, a data analyst working in healthcare may combine multiple data sets from different hospital departments. These departments may have much of the same data.
As well as cutting any duplicated data, removing anything extraneous is equally important. If some of the information collected isn’t pertinent to the question the data analyst is answering, they’ll remove this irrelevant data to make their analysis more efficient. It is also worth considering that concise and clear datasets are also easier to use when collaborating with non-data specialists.
Finally, once the collected data has been cleaned, it is ready to be processed. Data analysts use a variety of statistical tools to interpret data sets, looking for trends that are valuable to their project.
A few of the most popular tools for interpreting data sets:
Each tool has specific strengths and weaknesses depending on the data sets and the project’s requirements.
- Microsoft Excel
- SQL
- SAS software
- Google AdWords
- Tableau
- Google Tag Manager
- Jupyter Notebooks
- Github
- AWS S2
- Google Analytics
Some of the tasks involved in analyzing data include:
- Identifying and implementing tools and services which support data validation and cleansing
- Monitoring and auditing data quality
- Cleansing data of duplicates and extraneous information
- Addressing any missing data
- Working with internal and external sources to fully understand the data during its processing
- Manipulating and interpreting large complex data sets
Creating Reports
Many of the same tools used by data analysts to interpret data have also been integrated with tools to create reports. High-quality, concise, and easy-to-understand reports are especially important in order to effectively communicate the project’s findings to executives or clients who are not data specialists.
A data analyst may spend much of their time creating and maintaining a variety of internal reports, as well as client reports. Although they may understand the data inside and out, it’s the data analyst’s job to translate their findings into easily understood narratives. This means a business’s decision-makers can then make smart choices on the basis of analysts’ conclusions.
Programs like Tableau, Google Analytics, and SAS help data analysts create graphs, dashboards, and visualizations of data.
Many projects are ongoing and a data analyst will need to regularly review the data and revisit their reports. Reports can be delivered weekly, monthly, or quarterly, depending on the needs of the client. This also helps the analyst recognize useful long-term trends.
Collaborating with Other Departments and Roles
A data analyst doesn’t just work in isolation. They will find themselves collaborating across departments, working with salespeople, marketers, executives, and more. It’s critical that all involved parties understand the complete scope of a project.
Data analysts may also join forces with other data professionals, such as database developers and data architects, in order to curate data.
The data analyst’s work culminates with the report of their work. They also present their analyses and recommendations to management or clients. A truly talented analyst will excel in delivering a targeted presentation that’s well understood by its audience.
Skills
A data analyst requires a diverse set of skills when performing their duties. Below is a breakdown of the technical skills, programming capabilities, data analysis tools, and soft skills a data analyst should develop.
Technical Skills of a Data Analyst:
- Excellent numerical and analytical skills
- Ability to develop and document procedures and workflows
- Capacity to perform data quality control, validation, and linkage
- Experience with statistical methodologies and data analysis
- Familiarity with Hadoop open-source data analytics
- Exceptional understanding and use of graphical representations and data visualizations
- Familiarity with relational databases
- Understanding of data protection issues that may be encountered
- Advanced Microsoft Excel skills
Programming Languages for Data Analytics:
A data analyst should be very proficient in at least one programming language and they’ll likely have beginner skills in multiple languages.
- Python is the most widely used language in data science, used in up to 83% of projects
- SQL is a programming language used when accessing and manipulating SQL databases
- R is a programming language used for statistical computing and graphics
- Scala is an object-oriented programming language that’s highly desirable for employers
A data analyst needs to be able to build queries to extract specific data from large data sets.
Data Analysis Tools:
A data analyst will need to master at least one of these tools to create visually powerful reports.
- SAS
- Tableau
- Cognos
- Oracle Visualize Analyzer
- Microsoft Power BI
Soft Skills:
Soft skills are non-technical skills that demonstrate a data analyst’s ability to solve problems, manage their workload, and collaborate with colleagues. These skills are also a must-have for prospective data analysts because of the collaborative nature of their job, and they can easily be acquired in other fields.
- Critical thinking
- Excellent business acumen
- Being a strong collaborator and team player
- Intellectual curiosity
- Outstanding time management
Qualifications to Become a Data Analyst
A well-qualified applicant will usually have a degree. An applicant who’s graduated from a data analysis program and who has a strong grade point average will likely have no issues finding an entry-level position. Universities are increasingly moving to include machine learning and data analysis in their computer science programs in order to prepare emerging graduates for the roles currently in demand.
While degrees in associated fields – including computer science, information management, mathematics, statistics, business information systems, and economics – are particularly valuable, they’re not required. Technically any degree can be used, as long as the applicant can demonstrate the technical skills needed to perform the job’s responsibilities.
Despite this, postgraduate degrees in data science are becoming increasingly popular. Many people are wanting to transition out of their current careers and into the technology sector, and they’re looking to postgraduate courses as a helpful redirect.
Online master’s programs are often the most attractive option, as they give busy professionals the flexibility to fit their learning around their pre-existing work life.
Traditional Master’s Programs
A traditional master’s program will require the student to attend classes on campus at least part-time. This requires additional planning and investment from the student. A professional hoping to switch careers may need to find a local school or have significant savings to move and work closer to the university during their course. The class times will be less flexible and may interfere with a normal 9-to-5 work schedule.
These programs are also more expensive than online master’s programs, but they offer advantages like hands-on instruction and research opportunities. They’re also more demanding and are therefore more likely to be considered of higher value to an employer and have a greater impact on a student’s earning potential.
Traditional Master’s Programs for Data Analytics Roles in the US
| School Name | Program Name |
| Purdue University | MS in Business Analytics & Information Management |
| DePaul University | MS in Data Science (mix of in-person and online) |
| The University of Rochester | MS in Data Science |
| North Carolina State University | MS in Analytics |
| Georgia Institute of Technology | MS in Analytics |
| University of Oklahoma | MS in Data Science and Analytics (in person, online, or hybrid) |
| University of Iowa | MS in Business Analytics |
Online Master’s Programs
Online master’s degree programs vary in length. There are accelerated programs that can be completed in as little as 12 months, but most programs take about 36 months to complete full time. If an aspiring data analyst can only take one or two courses a semester because of other commitments, the degree may take significantly longer. Overall, these programs are very flexible and will work to accommodate students from all walks of life.
It’s important to consider the curriculum, the cost of the program, and the admission criteria when choosing which schools to apply to. Prestigious schools will generally have more admission requirements, including GRE scores, GMAT scores, and professional or academic recommendations. Prestigious schools will also generally charge the most per credit hour.
Online Master’s Programs for Data Analytics Roles in the US
| School Name | Program Name |
| Georgetown University | MS in Business Analytics |
| Johns Hopkins University | MSE in Data Science |
| Northern Illinois University | MS in Data Analytics |
| Saint Mary’s University of Minnesota | MS in Business Intelligence and Analytics |
| Drake University | MS in Business Analytics |
| Utica College | MS in Data Science |
| George Mason University | MS in Data Analytics Engineering |
Besides these traditional degree programs, there are also boot camps on data science which offer intense accelerated-learning programs over a few months. These aim to teach all the skills a new data analyst would need.
Getting Hired as a Data Analyst
Entry-level roles are available at companies in virtually every sector. The workforce has yet to catch up with the exploding demand from companies and institutions looking for talented data analysts. Large sectors – like consulting, government, and telecommunications – often offer post-graduation programs to help funnel new talent into their ranks.
Gaining experience before applying is always recommended. A relevant internship will help an applicant rise to the top of the pile. A data analyst intern works to support data analysts in tasks such as data quality control or data mining. These areas of the job aren’t too difficult and the intern will be helping to save the data analyst a lot of time.
Data analyst jobs are highly sought after because they are well-paying 9-to-5 Monday-to-Friday positions. This can support an excellent work-life balance. Overtime may be required during especially large projects, but remote-working opportunities are usually also an option as the analyst gains experience.
Industries Actively Hiring Data Analysts:
- Financial services
- Consumer retail
- Consulting
- Marketing
- Higher education
- Telecommunications
- Insurance
- Pharmaceuticals
- Information technology
- Media
- Government
A Data Analyst’s Salary and Future Outlook
Data analysts are well paid. Even at entry level, analysts can expect to command a decent wage. According to Indeed, the average annual salary for data analysts in the United States is $65,812 per year in 2022. Salaries vary widely depending on the industry and the location of the role, as well as an applicant’s skills, experience, and education. Data analytics is one of the highest paid sectors in tech.
In 2022, Washington, DC, Chicago, IL, and Denver, CO, are the three cities with the highest average salaries for data analysts, coming in at $76,350 per year, $76,224 per year, and $71,487 per year, respectively. By contrast, states including Idaho, North Dakota, New Mexico, Mississippi have the lowest average salaries for the position.
The U.S. Bureau of Labor Statistics predicts the demand for data analyst jobs is growing much faster than average, anticipating 20% growth between 2018 and 2028. Companies across many industries have realized the value of using data analysts to curate data and provide data-based market research. The positions are not easily filled though, as the job requires specific technical and analytical skills.
The average salary for an analyst with two years of experience and a bachelor’s degree is about $54,000. After about two years on the job, the salary can be expected to increase to $70,000 per year. A senior analyst with at least six years of experience will likely have a salary in the $90,000 range, but certain industries can see that surpass $100,000. However, this rapid salary increase during the first few years does not continue indefinitely.
Specialist skills can increase a data analyst’s salary by up to $15,000. Skills with the potential to increase a data analyst’s salary include learning the Scala programming language, learning the analytics engine Apache Spark, Data Warehouse experience, and learning Java. These are high-level skills for expert-level data analysts and shouldn’t be the focus of entry-level analysts until they build up experience and their central skillset.