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Career Guides

Data Analyst

BrainStation’s Data Analyst career guide is intended to help you take the first steps toward a lucrative career in data analysis. The guide provides an in-depth overview of what Data Analysts and data analytics are, how a Data Analyst compares to a Data Scientist, what a Data Analyst does on the job, and more.

Data Analyst
Ready to start your career in Data? Find out more about BrainStation's Data Analytics Course

What Is a Data Analyst?

Data Analysts collect and analyze large amounts of data and information to find trends and actionable insights.

Know Your Audience

Understand your users' needs and identify opportunities.

Evaluate Products and Processes

Measure and maximize efficiency and effectiveness.

Eliminate Guesswork

Make informed decisions to set realistic targets and objectives.

Optimize Marketing ROI

Track and analyze marketing campaigns to improve performance.

Data Analysts vs Data Scientists

Data Analysts typically:

  • Are more junior
  • Have a focus on business intelligence
  • Use Excel instead of programming languages
  • Look for meaningful insights from data
  • Optimize existing products and processes

Data Scientists typically:

  • Are more senior
  • Aim to predict the future
  • Use Python and other programming languages
  • Develop new ideas and products
  • Are involved with strategic planning

Find out more about the differences between Data Analysts and Data Scientists.

What Does a Data Analyst Do?

Data analysts collect, organize, and interpret data and information to create actionable insights for companies. To accomplish this, Data Analysts must collect large amounts of data, sift through it, and assemble key sets of data based on the organization’s desired metrics or goals. Analysts then often transform those key datasets into dashboards for different departments within the organization, presenting their insights in ways that can be used to inform activities and decision-making.

Data Analysts work in everything from political campaign management and finance to mining and epidemiology. But to give an example: imagine a corporate website that uses content marketing for lead generation. Tracking the conversion rates of visitors into customers yields data that lets a Digital Marketer follow a potential customer from their arrival at a blog post or other landing page all the way through to their signing up for a newsletter or even purchasing a product. Seeing what happens at each step helps the Marketer understand what content is working, why it’s working, and hopefully expand on that success.

Data Analysts’ specific tasks vary wildly from industry to industry, company to company. Generally speaking, though, as a Data Analyst, you can expect to perform some or all of the following tasks and responsibilities:

Research

As a Data Analyst, you will be responsible for researching your company and your industry to identify opportunities for growth and areas for improved efficiency and productivity.

Data Gathering

Data Analysts must gather data requirements, beginning with determining what you hope to accomplish and arriving at a clear sense of what information you need.

Data Collection

Data Analysts must collect usable data and information, either from existing sources or by developing new channels for information gathering.

Data Cleaning

Data Analysts must reformat data for consistency, removing duplicate entries and null sets, and so on. In very large datasets, this task is too onerous to complete by hand and requires the use of purpose-built tools and software.

Algorithm Creation

You will have to create and apply algorithms to run automation tools, allowing you to understand, interpret, and reach solid conclusions about what the data shows.

Data Modeling

Another requirement for Data Analysts is to model and analyze data to identify significant patterns and trends and interpret their meaning.

Presenting Your Findings

Once data analysis is complete, Data Analysts present their findings to other members of the organization, digested and packaged in a way they can easily grasp. This can include creating visualizations or dashboards for other members of the organization to refer to.

This diverse range of actions can be generalized by four fundamental categories: understanding the data, analyzing the data, building and managing databases, and communicating the data to others.

In the most recent BrainStation Digital Skills Survey, most Data Analyst respondents said they spend the largest amount of time wrangling raw data and cleaning it up. The primary use for this data? Optimizing existing platforms and products, as well as the development of new ideas, products, and services.

When BrainStation further correlated these responses to major job titles, an interesting discrepancy between Data Analysts and Data Scientists emerged: the majority of Business Analyst and Data Analyst respondents indicated that they tend to focus more on the former (optimizing existing platforms and products). Data Scientists, on the other hand, hew primarily toward the development of new ideas, products, and services, where strategic planning comes to the fore—possibly a result of differences in experience, knowledge levels, or degree of specialization.

Types of Data Analysis

These are a few of the most prominent data analysis types:

Text Analysis

Looks for patterns in large sets of written information (e.g. customer feedback surveys or social media posts).

Statistical Analysis

Examines characteristics of a numerical data set to find trends and correlations between data points.

Diagnostic Analysis

Drills deeper into insights from statistical analysis to determine causes and why correlations exist.

Predictive Analysis

Extrapolates or projects figures beyond the parameters of the existing dataset to forecast future outcomes.

Prescriptive Analysis

Draws on insights gained and uses them to determine the best course of action in a given situation.

Benefits of Data Analysis

Data analysis is important in business to help your organization identify and define problems, and to organize and interpret data sets to provide actionable insights and solutions. In addition, the relative speed and ease with which data can now be leveraged means that virtually every organization can optimize their operations and investments, allowing them to:

  • Make informed decisions
  • Set realistic targets
  • Predict consumer behavior

There are a wide range of enterprise-level analytics tools available to businesses at virtually every scale. Google Analytics is a great example. The basic tools are free, and their insights can be used to recalibrate and dramatically improve the performance of your business website.

How Does Data Analytics Help Businesses Make Decisions?

Data analytics can help businesses make decisions by compiling data from across an organization, giving it insight into the performance of sales, marketing, product development, and more. This gives organizations the opportunity to track the performance of various initiatives and investments in context, allowing it to make better business decisions.

Data analysis can also allow your organization to dig deeper into each business function and department, ensuring you’re maximizing ROI. Let’s take a closer look at the impact data analysis can have on digital marketing initiatives:

Social Media Marketing

What is your target audience talking about? How do they interact with your company? Do they share or like the content your business is posting? Coupling your social media presence with good data analysis can extract more value from your online profile.

Email Marketing

Using analytics, email can double as an information-gathering tool by helping you determine which subject lines get the most opens, and what kinds of messages see the highest rates of success. You can also test which times of day or days of the week your audience is most likely to see and open emails. Data analytics ensures that you aren’t just sending your messages out into the universe and hoping for the best; it gives you the information you need to continuously improve.

Target Demographics

Data can help define your target audience, including understanding where they are and how to reach them. Rather than wasting advertising dollars to reach a broad audience, data analytics allows you to more precisely target your ad spending to tailor your content to the right people for maximum impact on a leaner budget.

The benefits of analytics aren’t limited to marketing. Tracking expenditures over time, for example, can provide insight into where your organization’s biggest expenses are—and provide clues on how to run a more efficient operation. Anywhere data can be gathered, analyzed, and compared against key performance indicators, it can be used to effect change and improve outcomes.

Data Analyst Salary Ranges

While salaries for Data Analysts can vary greatly by industry and region, the average salary for a Data Analyst in the U.S. is $73,673.

Range of average salaries for Data Analysts:

  • Entry level: $69,827
  • Intermediate level: $78,026
  • Senior Data Analysts: $87, 211

Demand for Data Analysts

Demand for Data Analysts and for data skill is surging. According to a report from McKinsey, the United States faces a shortage of up to 190,000 people with Data Analyst skills.

What’s more, the World Economic Forum (WEF) found that by 2022, 85% of companies will have adopted big data and analytics technologies. WEF also found that 96% of companies were planning or likely to plan to hire new permanent staff with relevant skills to fill roles related to data analytics.

Unsurprisingly, the role has been called one of the most in-demand jobs by LinkedIn, Glassdoor, the US Bureau of Labor, and Robert Half, among others.

What Tools Do Data Analysts Use?

Data Analysts use a number of different tools to collect and analyze data, and then to visualize and present insights for non-data professionals to understand.

Excel is the most widely used analysis tool, although that is changing. According to the BrainStation Digital Skills Survey, 66 percent of data professionals cited it as their most-used tool in 2020, down from 81 percent in 2019.

There are a number of potential reasons for the reduction in the number of Excel users, including the potential for accidental data loss in spreadsheets, as well as the inability to share data and information in real-time.

A close runner-up in terms of use by Data Analysts is SQL, the industry-standard query language used in database management, which is routinely used by 48 percent of respondents. Big-data number-cruncher Python is the most widely used tool for statistical programming, with 45 percent of respondents relying on it on a regular basis (coming in next at 15 percent, is R, another important player in this category).

We’ve also reached the point where more data professionals work in the cloud than don’t. Only 36 percent of respondents said they work without a cloud computing platform. Amazon Web Services is the most popular cloud platform here, used by 43 percent of survey respondents, with Google Cloud following at 24 percent.

Spark is the dominant processing framework—used to write applications across a range of operators for large-scale data processing—with 43 percent of professionals naming it their framework of choice; Hadoop, in second, claims a further 24 percent.

Tableau is by far the most widely used visualization tools, with 60 percent of Data Analysts using it. Other popular visualization tools include Matplotlib and ggplot2—Python and R’s respective visualization packages.

Overview of most popular tools for Data Analysts:

Excel
According to the BrainStation Digital Skills Survey, 66 percent of data professionals cited it as their most-used tool.

SQL
The industry-standard query language used in database management, which is routinely used by 48 percent of respondents.

Python
The most widely used tool for statistical programming.

Spark
The most dominant processing framework, used to write applications across a range of operators for large-scale data processing.

Tableau
The most widely used data visualization tool.

Data Analyst Career Path

Data Analyst is an excellent entry point into the world of Data Science; it can be an entry-level position, depending on the level of expertise required. New Data Analysts typically enter the field straight out of school – with a degree in statistics, mathematics, computer science, or similar – or transition into data analysis from a related field like business, economics, or even the social sciences, typically by upgrading their skills mid-career through a data analytics course.

Common job data analytics jobs:

  • Data Analyst
  • Business Analyst
  • Systems Analyst
  • Research Analyst
  • Operations Analyst
  • Marketing Analyst
  • Researcher
  • Statistician

What Skills Do You Need to Be a Data Analyst?

There are a number of skills all aspiring Data Analysts should develop, including:

SQL
The industry-standard query language used in database management, which is routinely used by 48 percent of respondents.

Excel
According to the BrainStation Digital Skills Survey, 66 percent of data professionals cited Excel as their most-used tool.

Statistical Programming
R or Python are used to run statistical functions, including regression analysis, linear and nonlinear modeling, statistical tests, and more.

Data Visualization
Data Analysts using programs like Tableau, PowerBI, Bokeh, and more to produce easy-to-read chord diagrams, heat maps, scatter plots, and more.

Find out more about what skills you need to become a Data Analyst.

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