Data Analysts are highly qualified professionals, responsible for retrieving and gathering data from a variety of sources. They must then organize and analyze the data to extract relevant conclusions. This is a sought after career for new graduates and professionals looking to change careers. Let’s examine further why this role is so important in the current and future economy.
Data is the New Business Currency
Computing advancements have pushed the economy into the age of data. Data is king and is pushing industries and moving markets forward. With the incredibly dramatic increase in the use of computers for business interactions, businesses are now sitting on mountains of data. They desperately need individuals that can extract value from this data.
These mountains of data are invaluable to companies because a highly-trained professional can analyze and draw significant important inferences. With these inferences companies are no longer probing in the dark and can make calculated and educated decisions. For example, a company can develop a highly targeted marketing strategy or make insights into why specific products or services performed poorly.
The workforce is transforming to meet the needs of the economy, providing more data literate graduates than ever before. These graduates are moving into roles like Data Analysts, Data Engineer, Database Manager, Data Architect, and Data Scientists, jobs that vary widely in day to day operations, but involve data at their core.
In this article, we are delving deep into everything about data analysts!
Four Types of Questions Answered by Data Analysts
Data analysis really breaks down into four types, that can each bring specific value to a project. These four types build upon each other, to educate business and help a client make data-driven decisions. A proficient data analyst will regularly use these four types of data analytics when fulfilling their responsibilities to a client.
What happened in the past?
Descriptive Analytics will look at data from the past to spot trends. Examples of data like this include website traffic, quarterly sales, monthly revenue, and weekly expense reports.
Why did something happen?
Diagnostic Analytics will look at the descriptive data and look for patterns that explain the reasons for the positive or negative outcome. 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.
What is going to happen?
Predictive analytics will often combine diagnostic and descriptive analytics to predict the result that is likely based on the data. If monthly revenue is going down every month and diagnostic analytics have determined it to be caused by a severe staffing shortage, then without remedy analytics is likely to predict a further revenue drop.
What is the next plan of action?
Prescriptive analytics will identify what actions and remedies a business or client should take next. This is usually the most significant and valuable type of analytics to a client. The staffing example has a simple remedy that would likely be prescribed by an analyst, but real-world situations are much more complex. Companies aim to stay on top of trends and quickly address any problems before they get worse.
Day to Day Responsibilities of a Data Analyst
A data analyst has four main responsibilities:
- Set up Data Collection Infrastructure and Collect Data
- Analyze the Data
- Create Reports
Setup up Data Collection Infrastructure and Collect Data
Even though companies are constantly collecting data, this data isn’t always easily accessible. The data may be missing a very important and useful metric. A new project can require rethinking and creating a unique data collection infrastructure aimed at specifically collecting the intended data. A data analyst may collaborate with a web developer to optimize data collection.
A data analyst may also be required to go outside the bounds of their companies proprietary database, as outside information may be needed. Gathering these datasets can be time-consuming when there are important missing parameters for the current project. Once a general data collection infrastructure is in place, the data collection becomes much easier and takes up less of the analyst’s day.
Some tasks that fall within data collection include:
- Develop and maintain a data collection system and databases
- Mine data from within internal systems and from secondary sources
- Identify areas of data collection where efficiency can be increased by implementing automation
- Develop and maintain automated processes
Analyze the Data
Once the data is collected, the data analyst is then responsible for cleaning and processing the data.
Cleaning data is the process of reviewing the data the infrastructure collected and making vital decisions about what data is extraneous and what is not. Often when combining multiple data sources, data can be duplicated or incorrectly labeled. The data analyst must watch out for these errors in data and remove them to avoid the bad data affecting the outcomes of the analysis algorithms.
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.
Removing extraneous data is equally important. If the information is not pertinent to the question the data analyst is answering, they will remove the irrelevant data to make analysis efficient. Concise and clear datasets are also best when collaborating with non-data specialists.
Finally when the data collected has been cleaned it can be processed. Data analysts use a variety of tools to aid in analysis. Data analysts will use statistical tools to interpret data sets while looking for trends that are valuable to their project.
A few of the most popular tools are Microsoft Excel, SQL, SAS software, Google AdWords, Tableau, Google Tag Manager, Jupyter Notebooks, Github, AWS S2, and Google Analytics. Each tool has specific strengths and weaknesses depending on the data sets and project at hand.
Some tasks that fall within analyzing data include:
- Identify and implement tools and services that support data validation and cleansing
- Monitor and audit data quality
- Cleanse data of duplicates and extraneous information
- Address any missing data
- Work with internal and external sources to be sure to fully understand data while processing
- Manipulate and interpret large complex data sets
Many of the same tools used by data analysts for interpreting the data have also been integrated with tools to create reports. High-quality, concise, and easy to understand reports are especially important for the data analysts to deliver to executives or clients.
A data analyst may spend much of their data creating and maintaining a variety of internal reports as well as client reports. The data analyst may understand the data inside and out, but it’s their job to translate that into an easily understood narrative so the decision-maker, whether it’s an executive or client, can make well-educated business decisions.
Programs like Tableau, Google Analytics, and SAS perform especially well by helping the data analyst create graphs, dashboards, and visualizations.
Many projects are ongoing and a data analyst will need to review the data and redraw up similar reports regularly. These 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.
A data analyst doesn’t just work isolated with huge data sets all day. Data analysts will find themselves collaborating across departments, to work with salespeople, marketers, executives, and more.
It’s critical that all involved parties understand the complete scope of each project. They may also join forces with other data professionals that were mentioned at the beginning of the article, such as database developers and data architects. All to work on curating the data to answer the questions for the clients.
With the report completed, this is really where the data analysts’ work culminates. The analyst is now able to present their data analyses and recommendations to management or clients. A truly talented analyst will excel in delivering a targeted presentation that is well received and understood by the audience.
A data analyst requires a diverse toolbox of skills when performing their job duties. Below is a breakdown of technical skills, programming capabilities, data analysis tools, and soft skills a data analyst would want to develop when looking for work.
Technical skills of a Data Analyst:
- Excellent numerical and analytical skills
- Develop and document procedures and workflows
- Capacity to perform data quality control, validation, and linkage
- Practice with statistical methodologies and data analysis
- 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 in Data Analytics:
A data analyst should be very proficient in at least one programming language and will 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 in accessing and manipulating SQL databases
- R is a programming language used for statistical computing and graphics
- Scala is an object-oriented programming language that is highly sought after by 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 to share with clientele.
- Oracle Visualize Analyzer
- Microsoft Power BI
Soft skills are non-technical skills that demonstrate data analysts’ ability to solve problems, manage their workload, and interact with colleagues. These skills are also a must-have for prospective data analysts because of the collaborative nature of the job, but can easily be gained in other fields.
- Critical Thinking
- Excellent business acumen
- Strong collaborator and team player
- Intellectual Curiosity
- Outstanding time management
Data Analyst Qualifications
A well-qualified applicant to a data analyst position will have a degree. An applicant that has graduated from a data analysis program and had 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, to educate emerging graduates for these many unfilled roles.
While degrees in associated fields like computer science, information management, mathematics, statistics, business information systems, and economics are particularly valuable, they are not required. Technically any four-year degree can be used, as long as the applicant can demonstrate the technical skills needed to perform the job 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 are looking to secondary education to help them get there.
This can be accomplished with quite a few different types of master’s programs, but online Data Science programs are the most attractive. Returning students want to know they are going to graduate from postgraduate school with all of the tools needed to excel in a data analyst position. Online master’s programs are very popular among busy professionals.
Traditional Masters 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 for the student. A professional hoping to switch careers may need to find a school local to them or have significant savings to move and work closer to the university during their education. The class times will be much less flexible and may interfere with a normal 9-5 work schedule.
These programs are more expensive than online master’s programs, but offer advantages like on-hands instruction and research opportunities. Traditional Master’s programs are more demanding and more likely to award Data Science degrees which are more valuable and command higher salaries.
|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, and hybrid)|
|University of Iowa||MS in Business Analytics|
Online Masters Programs
Online Master’s degree programs can vary in length widely. 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 must take only 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, cost of the program, and admission criteria when creating a list of possible schools. 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.
|School Name||Program Name|
|Georgetown University||MS in Business Analytics|
|John Hopkins University||MS in Data Analytics and Policy|
|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|
There are even boot camps for data science that can provide intense accelerated learning programs for a few months, to teach all the skills a new data analyst would need.
Getting Hired As A Data Analyst
Honestly, entry-level roles are available at companies in virtually every sector. The workforce has yet to catch up to the exploding demand from companies and institutions all over, looking for talented data analysts. Large entities like consulting firms, government, and telecommunications often offer post graduation programs to help funnel new talent into their ranks.
Gaining experience before applying is always recommended. An internship will help an applicant rise to the top of the pile. A data analyst intern will work to support data analysts in their tasks. These are usually data quality control or data mining tasks that aren’t too difficult for the intern but having the intern work on it, it saves the data analyst a lot of time.
Data analyst jobs are highly sought after because they are well-paying 9-5 Monday to Friday positions. This provides an excellent work-life balance. Overtime may be required during especially large projects, but the position can also provide remote-working opportunities as the analyst gains experience.
Industries actively hiring for data analysts:
- Financial service firms
- Consumer retail firms
- Consulting firms
- Marketing firms
- Higher Education Institutions
- Telecommunication companies
- Insurance companies
- Pharmaceutical companies
- Information Technology firms
- Media companies
Data Analyst Salary and Future Outlook
Data analysts are paid well, even at the entry-level, analysts can expect to make a decent wage. According to Indeed, the average annual salary for data analysts in the United States is $70,033 per year. Salaries will vary widely depending on location, skills, experience, education, and industry. Data Analytics is one of the highest paid professions in tech.
North Carolina, New Hampshire, and Maryland are the three states with the highest average salaries for data analysts, coming in at $85,266 per year, $83,324 per year, and $82,692 per year, respectively.
The bottom three states are Alaska, Maine, and Kansas, delivering salaries of $39,473 per year, $44,022 per year, and $48,486 per year respectively.
The U.S. Bureau of Labor Statistics predicts data analyst jobs will grow much faster than average, at 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.
Upon first entering the Data Analyst’s career path, this may not be the salary seen by the analyst. 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. This rapid salary increase during the first few years does not continue indefinitely.
The best skills for a data analyst to increase their 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 entry-level analysts should likely focus on other skills. Some of these skills can increase a data analyst’s salary by up to $15,000.