Data analysts form a crucial bridge between the process of data collection and the making of a product that provides valuable insights to reach a business or research goal. A data analyst is like a Swiss Army knife of the information world and is expected to know the ins and outs of a company or organization’s data with a goal of guiding informed decisions.
If you are looking for more info on how to start a career as a data analyst — you have come to the right place. This guid is all about what you will need to become a data analyst, including data analyst degree options, info about what data analysts do all day, and what some potential career paths might look like.
Data Analyst Degree
There are several routes to becoming a data analyst. The first route is to obtain a bachelor’s or a master’s degree in domain such as finance, economics, information, mathematics, computer science, bioinformatics, statistics.
A knowledge of the data analyst domain acquired through education helps in understanding industry specific information metrics used to derive key insights. A degree in STEM builds a strong foundation in the qualitative skills needed to become a data analyst.
But a data analyst degree isn’t the only way to launch a career. If you decide to change your career trajectory to become a data analyst and come from a non-traditional background, there are several resources to assist you in finding a job in the field.
Online learning platforms such as Coursera, Udemy, edx can be used to assist you in developing the skill sets needed to become a data analyst. There are platforms like Kaggle that help you with a dataset which can be used to get practical hands-on knowledge in using necessary tools and tackling the challenges while working with datasets to come up with solutions. Data science bootcamp training or data analyst certifications are another way you could get into a data analyst role without a formal degree.
Regardless of how you prepare for a career as a data analyst, it is important to keep current on evolving tools and platforms to analyze the data. The career path of a data analyst has diverse forks depending on the industry of employment. It has become common for a data analyst to move into a data scientist role, although this depends on the industry if the option is available.
There is an overlap between the roles and responsibilities of a data analyst and a data scientist, a data scientist is expected to frame the right questions that will help with business growth and development while a data analyst is provided with questions that can be answered through data management and manipulation. Data scientists need to have a greater understanding of mathematical models, machine learning and technical skills in comparison with a data analyst to deploy their work in a production environment.
How To Become a Data Analyst
As technology changes, it is expected that a data analyst keeps in touch with the latest software and tools that helps in fetching, data wrangling and describing the datasets.
Traditionally, an analyst’s best friends have been Excel spreadsheets, R and Python programming languages and SQL. Evolving data systems might also require them to fetch data from streaming outputs from NoSQL databases, file storage objects, continuous streaming data from Apache Spark, Hadoop(Mapreduce), Hive or cloud servers.
As much as technical skills, the importance of strong domain knowledge cannot be underestimated for this role. An analyst is able to perform his or her tasks more efficiently if they understand the domain. This will equip them with asking the right questions while collaborating with the stakeholders and providing a detailed answer to their questions.
Depending on the industry, a data analyst may be provided with titles such as bioinformatics analyst, healthcare analyst, fraud analyst, business operations analyst. A data analyst might be expected to have an educational background or work experience in the respective field of interest.
What is a Data Analyst?
In general, a data analyst is known to wear multiple hats. The roles and responsibilities for data analysts may differ slightly based on the industry sector they work for. On a usual day, a data analyst will perform the following tasks:
Incoming data collected may not always be perfect. Quantitative data might be incoming into the data system as characters or in string format causing an issue to calculate basic statistics like mean or standard deviation. It may have a lot of missing information rendering it infeasible to be considered in developing key strategies insights. It may also have noise or information that may not be useful to answer the questions needed for business requirements.
Data munging is one of tasks that takes a majority chunk of an analyst’s time. It is a process of taking in raw data and cleaning it to make it conducive to research. This process involves many tasks not limited to formatting and filtering variables irrelevant for analysis, coming up with data imputation strategies to work with partial data or merging data from two different data sources based on key variables or timestamps and so on.
Data Exploration, Visualization, and Reporting
Once the data is cleaned, the next task is to perform exploratory analysis on the cleaned data by measuring preliminary statistics and describing the data.
An analytical mind is needed for the process of data exploration. This process helps in describing the key features that are present in the dataset. An example of data exploration would be to describe a demographic variable in a survey population like age categorized by gender and using descriptive statistics to look at mean, median and outliers.
Creating reports on a regular basis is also one of the responsibilities a data analyst works on. This reporting leads to track growth of the business or underlying trends or patterns that can help business understand their customers and offer insights and areas of improvement.
A data analyst needs to be creative to present his or her understanding of data that can be easily grasped by others. The data analyst needs to be equipped with data visualization skills to plot outcomes and develop dashboards that help in reporting insight from the data.
Data Governance and Management
While this is a broad scope, a data analyst may be involved in some of tasks but not all of the tasks related to data governance and management. Some examples of tasks involved in these areas may include developing strategies for metadata and data quality checks.
This requires an analyst to be detail-oriented and organized. During the exploratory phase, a data analyst may find issues with incoming data and may have to collaborate with data engineers and database administrators in the data modeling process in order to make the data capturing process more robust and convenient to use. They may be needed to perform data validation by developing edge cases and collaborating with the developers to check for correctness of data.
Decision Making and Predictive Analytics
The ability to collaborate with different teams to understand their requirements and clear communications with the collaborating team is appreciated and valued across the industry where a data analyst is employed. This also requires reasoning skills on the part of the data analyst to come up with information relevant in making decisions.
Occasionally or within a specific industry, an analyst may be required to have statistical skills to perform predictive analytics and projections for business decisions.
Data Analyst Career Paths
Any organization that makes data-driven decisions needs a data analyst on their team. Industries that hire data analysts include finance, insurance, credit bureaus, market research, public health and other healthcare research, sports management, social media, technology firms, non-profits, government, security systems. The list continues to grow.
Research by Andrew McAfee and Erik Brynjolfsson from MIT reveals, “companies in the top third of their industry in the use of data-driven decision-making were, on average, five percent more productive and six percent more profitable than their competitors.” In 2011, McKinsey predicted that by 2018 there would be 2.8 million workers with either deep analytical talent or data-savvy skill sets. By 2015, however, there were already over 2,350,000 job listings for core data science and analytics (DSA) jobs in the United States. By 2020 the number of DSA job listings was projected to grow by nearly 364,000 listings, to about 2,720,000 openings.
Data Analyst Job Outlook and Salary Outcomes
The Bureau of Labor and Statistics projects that employment for operations research analysts is expected to grow 25 percent between 2019 to 2029. Similar upward trends have been observed in other industries where data analysts are employed. This growth is much faster than the average for all occupations.
As technology advances and companies seek efficiency and cost savings, demand for operations research analysis should continue to grow. Salaries of data analysts vary depending on industry type, educational background, years of experience and location. According to ZipRecruiter, “As of Mar 7, 2021, the average hourly pay for a data analyst in the United States is $32.35 an hour or $67,294 annually.
The annual salary for a data analyst in the United States as per Glassdoor is $62,453 with high confidence. LinkedIn closely matches Glassdoor to record the average salary of a data analyst at $62,400 while Indeed has recorded the average as $74,115.