Data analysts are one of the most in-demand jobs in the United States.
A data analyst is a person who specializes in obtaining information, evaluating it for trends, and presenting it in reports. They are a priceless resource for companies that rely on data to acquire insights, including most modern corporations, governments, and organizations.
Data analysts can assist in the resolution of a variety of issues. A data analyst in a healthcare setting, for example, would examine patient data to discover chronic disease in a patient. Analysts for streaming service providers like Hulu, for example, may gain insights that help the provider improve the display of its material.
What is a data analyst’s job description?
Data is generated and consumed at a rapid pace. Data analysts oversee assessing data as it is received to assist the company with which they work in achieving a goal. First, analysts take vast amounts of data and translate it into terms that a company can understand. Next, data analysts examine data to answer a set of questions. The next hunt for trends in the data that could lead to an answer, and they offer the findings in a report.
They’re also in charge of refining the processes that each of these steps entails, whether the established methodology for gathering, evaluating, and reporting data or identifying new data sources to acquire data from.
Skills required for data analysis
As the link between the business side of an organization which requires soft skills like communication and teamwork, and data science which requires hard skills like programming languages and business intelligence software, data analysts require a mix of hard and soft skills to do their jobs effectively. In other words, analysts interpret data for businessmen therefore, they must have both data and people skills.
A data analyst’s resume should include the following basic skills:
- Understanding of business intelligence software
- Knowledge of mathematics and statistics
- Knowledge of computer science and coding
- Solving a problem
- Thinking critically
- Focus on the details
To gain a job as a data analyst, you’ll also need the following eight unique abilities.
- SQL (Structured Query Language)
SQL is a computer language for managing relational databases, and data analysts can use it to conduct queries on the data in those databases. However, other spreadsheet and computer tools, such as Excel, are not as good at processing massive databases like SQL.
The majority of data analyst job postings state that SQL proficiency is required. SQL is a wonderful beginning to learning programming languages and being a necessary skill. SQL (Structured Query Language) is a query language (SQL).
- NoSQL database
As the two database architectures are frequently contrasted, data analysts who understand SQL should also grasp NoSQL. SQL is a programming language. Nonrelational databases, or databases that do not exclusively employ the SQL programming language, are usually referred to as NoSQL. NoSQL databases are frequently used in organizations because of their ease of horizontal scalability and flexibility. They’re also favored over data consistency because of their real-time analysis capabilities and availability.
- Tools for business intelligence
Data visualizations are created using business intelligence technologies such as Tableau, Power BI, Qlik, and Looker. Non-technical company workers can grasp the reports created by analysts thanks to data visualizations.
Data visualization makes data accessible to users who have little to no computer languages or database navigation techniques, just as SQL does for data analysts.
One of the most crucial programming languages for data analysts is Python. Many specialized, open-source libraries are available for Python, which are related to machine learning and artificial intelligence. Though some may consider ML and AI to be more suited to the role of data scientist, they are also beneficial for data analysts, and Python is one approach to apply the capabilities of AI and ML to large data sets to generate insights.
Python was voted the best programming language to learn in an IEEE Spectrum survey in 2020. Despite having a higher learning curve than SQL, Python is still a simple programming help language to pick up.
R is another widely used programming language in data analytics, and whether Python or R is better is frequently debated. R may be used for data wrangling, which is preparing data for use in business intelligence tools. Users can visualize data, execute complex statistical functions, and map data geographically using R’s data-centric add-on packages. R, like Python, is open source and free to use.
Although most data analyst job listings do not require a command of statistics, it is necessary to comprehend some statistical concepts, especially if you work in a data-science-heavy environment.
Significant testing, as well as linear and logistic regression, are some fundamental ideas to grasp. These are some of the statistical concepts that underpin machine learning, which is the engine that drives predictive data analytics. Although a data analyst may not always employ in-depth statistics in their work, it is crucial to know so that the analyst can prepare data to understand how the data scientist on their team might use the data they’ve prepared, processed, and reported on.
- Communication and writing
Data analysts are frequently part of a larger data science team that includes data scientists and engineers. They are also in charge of informing non-technical individuals of their organization about their findings. As a result, analysts must express difficult concepts in a way that is understandable to the public.
If someone doesn’t comprehend a report, for example, an analyst will have to explain it in a new way. This necessitates paying attention to the organization’s leadership and turning basic business needs into statistics.
- Critical thinking and problem-solving
The capacity to problem solves and think critically about all sides of a business situation is another important soft talent. After all, a data analyst’s job is to interpret data and turn it into prospective business solutions. Listening and communication go hand in hand with this. Data can be studied and re-analyzed, but it’s not worth much if it doesn’t have a useful purpose for the organization.
Learn how business executives continue to seek out data analysts’ ideas earlier in the decision-making process by diving deeper into the value of creative problem solving on a data analytics team.
What are the steps to becoming a data analyst?
To become a data analyst, there is no one-size-fits-all approach. Companies are looking for well-rounded applicants to fill these positions, so having various learning experiences is critical.
The following are some examples of possible methods for acquiring the requisite skills:
- Certifications in data science
- University degrees
- Massive open online courses (MOOCs) for self-teaching
- Experience working on a data science or analytics project in the past
Studying something connected to math or statistics for a university degree is a secure bet. This could be a straight degree in statistics or mathematics or an indirectly connected degree, such as sociology, including statistical training. A computer science degree is also a viable option. Analytics and data science are also offered as specialist degrees at some colleges.
It’s critical to maintain consistency during the interview process. Candidates must mention hard skills that they can demonstrate if requested. For example, if a candidate cannot demonstrate how to write a SQL query, they should not list SQL. Candidates for soft skills should have a story or example of how they applied these attributes to a real-world challenge prepared.