Businesses across all industries want experts who can gather and evaluate data, translate it into actionable insights, and use those detailed insights to address critical business issues. Therefore, you could think about pursuing the data analyst job for several reasons.
Let’s take a look into its responsibilities and salaries.
In this article:
What Is A Data Analyst?
A data analyst is an expert who gathers, arranges, and evaluates data from several sources to assist a company in achieving its objectives. Because data analysts are so adaptable, they are sometimes referred to by different job titles, such as:
- Business analyst
- Business intelligence analyst
- Market intelligence consultant
- Operations research analyst
- Competitive intelligence analyst
- Data strategist
Whatever the specific title, the data analyst job benefits businesses in a variety of industries, from marketing and healthcare to biology and logistics. This is a major contributing factor to the increased need for data analysts.
A Data Analyst’s Responsibilities
A data analyst’s job is to transform unorganized, raw data into useful, understandable data. After that, they must respond to certain queries or work out issues using the knowledge gleaned from the data.
Key stakeholders in the firm are then given access to this information to aid in the planning and execution of changes. In addition, experienced data analysts oversee the procedures for gathering, storing, and establishing criteria for the caliber of data.
Almost every business that uses data to structure its operations can employ data analysts, including manufacturing, government, finance, education, and healthcare. Regardless of the sector, they deliver their results after data preparation and purification to assist businesses in making strategic decisions.
They collaborate with teams in business and engineering and may share their results with IT, data science, product management, sales, finance, and marketing, among other areas.
A Data Analyst Job Descriptions
- Construct and run collecting systems and databases.
- Compile information from both main and secondary sources.
- Collaborate with the management team to identify critical KPIs and measurements.
- Clean the data.
- Utilize technologies to recognize, evaluate, and pinpoint intricate data trends and patterns.
- Construct and modify data reports.
- Create and keep up reports on infrastructure and statistical models.
- Analyze national and international industry trends that might impact the business and the industry.
- Plan data governance strategies and identify areas for improvement in close collaboration with engineers, managers, and programmers.
- Correct coding mistakes and eliminate tainted data.
Differences Between Data Analysts Vs. Data Scientists
It’s possible that you’ve previously read some material about data science and conducted some studies on the job of a data analyst. Although these two names are sometimes synonymous, they refer to two distinct professional pathways with different goals and key skill requirements.
Data analysts utilize an organization’s data and provide interpretations for business decision-makers. They hunt for patterns in data and utilize those to create dashboards and visualizations that can be used by a wider audience to answer questions and provide answers.
Meanwhile, a data scientist will explore the data further to find patterns using machine learning and data mining. After setting up studies, they create models and tests to support or refute their findings. Based on their findings, they will then recommend how a firm should proceed.
The Average Salary Of A Data Analyst
According to Glassdoor, as of May 24, 2024, data analysts may expect to make between $86,000 and $136,000 a year in total compensation, with base pay ranging from $68,000 to $103,000. These are competitive compensation packages.
Extra compensation, which can include commissions, gratuities, cash incentives, and profit sharing, is offered on top of the base salary, ranging from $18,000 to $34,000 annually. In the United States, a data analyst can make a total of about $107,663 a year, with an average salary of $83,457.
6 Steps To Become A Data Analyst
Step 1: Obtain A Basic Education
If you’re a newcomer in the data analysis field, start by learning the fundamentals of the subject. Gaining a general understanding of data analytics will prepare you for the workforce and assist you in determining if this is a profession that suits you.
Most entry-level data analyst jobs used to require a bachelor’s degree. Although a degree is still necessary for many occupations, the landscape is changing. A degree in computer science, math, or a similar discipline can help you build your core knowledge and strengthen your resume. You can get the required insights in many ways, such as self-study courses, boot camps, and professional certificate programs.
Step 2: Build Your Technical Abilities
Possessing certain technical abilities and analytical skills is crucial to land an entry-level position in data analysis. Some fundamental abilities are required, regardless of whether you’re learning through a professional certificate program, degree program, or self-studying.
- Descriptive statistics
- Python or R programming
- SQL (Structured Query Language)
- Data visualization
- Data cleaning & preparation
A tip to determine which aspect you need to focus on is to look through job postings for positions you would like to apply for and check their prerequisites. After that, you can spend more time studying the required programming languages and visualization tools accordingly.
Learning some soft skills, in addition to technical skills, is also recommended. These include workplace skills such as problem-solving ability, strong communication skills, and domain knowledge of the sector you’d like to work in.
Step 3: Take On Tasks With Actual Data
You’ll have many chances to practice your newly acquired abilities by working on different exercises and finishing the data analysis assignments recommended by your program. You will lay a strong foundation for future job experience by honing your abilities and finding solutions to simulated or actual situations.
At this point, it will be easier to focus on learning and avoid distractions like further research or brainstorming if you have access to some actual, clean datasets and pre-selected topics to investigate. To help you put your abilities to the test, have a look at our selection of data science projects.
Step 4: Create Your Portfolio
Save your finest work and hands-on projects for your portfolio when you finish practical homework in your classroom or experiment with online data sets. HR managers tend to scrutinize the ability section of your portfolio, so a solid portfolio or resume can be your ticket to a new job in this field.
However, you should select what to include in the portfolio strategically, especially those that prove your capabilities to:
- Gather information from a wide variety of sources
- Purify and standardize unprocessed data
- Make use of maps, graphs, charts, and other visualizations to illustrate your results
- Analyze data to get useful insights
Think about including one of your group projects if you’ve completed any during your education, which demonstrates your collaboration skills. In case you’re unsure of what to put in your own or need ideas for projects, browse through other people’s portfolios to get an idea of what they’ve decided to include.
Step 5: Apply For Jobs As An Entry-Level Data Analyst
Now that you have some data analysis and presentation experience under your belt, it’s time to update your CV and start applying to positions as an entry-level analyst.
Applicants should not be scared to apply for jobs for which they do not believe they are fully qualified. Although you may be able to check off every item on the prerequisites list, your talents, portfolio, and excitement for the position may frequently be more important.
Suppose you’re still enrolled in classes; ask the career services office at your institution about internship options. With an internship, you may begin applying what you’re learning in the workplace and build real-world experience for your CV.
Step 6 (Optional): Take Into Account Earning A Degree Or Qualification
Consider your professional goals and what additional credentials will help you reach them as you progress as a successful data analyst. You might need to obtain a data science degree or a comparable discipline if you’re thinking about moving up into a position as a data scientist. Although they are not always necessary, advanced degrees can lead to greater options.
Frequently Asked Questions
Is A Data Analyst A Stressful Job?
Most of the time, yes. Stress levels might rise due to the excessive amount of data that has to be examined. The strain is increased by the requirement to keep up with ever-evolving tools and technology.
Is Data Analyst A Lot Of Math?
No. For the most part, data analysis only entails following a set of logical steps, but it also requires strong numerical skills and can benefit from a foundational grasp of arithmetic and statistics. Long story short, it’s not necessary to be very mathematically proficient for success in this sector.
Can I Become A Data Analyst If I Hate Coding?
Yes. Although coding can be useful, there are analysis tools and platforms that offer low- or no-code solutions. Thus, it is feasible to enter the area without having a lot of coding experience.
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The Bottom Line
Data analysts are in charge of gathering, processing, analyzing, and displaying data, as we have seen. Numerous talents are needed for these activities, and employers from almost every industry are searching for data experts who can use data to inform smarter business decisions.
The data analyst role is arguably the most accessible if you’re thinking about getting into data science since, unlike data scientists, it doesn’t require a strong background in math and computers.
