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How to Become a Data Analyst

What is a Data Analyst?

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Data Analysts are data professionals who gather, order, and examine data and information to find trends and make actionable reports for companies or other organizations. Data Analysts must sift through massive amounts of data and – while considering the specific needs of the company that they’re working for – determine key data sets. Data Analysts then use different tools to create visualizations that make these insights easy for non-data professionals to understand and act upon as they make major decisions on the company’s direction.

Data Analysts are employed in virtually all industries and all types of companies, from healthcare providers to banks to retail stores to restaurant chains. Data Analysts bring valuable insights to any employer who wants to know more about the needs of their consumer or end-user, to evaluate how their current products or processes are faring, and to find opportunities to expand on what’s working well.

No matter the industry they work in, Data Analysts should expect to spend considerable time developing systems for collecting data, organizing that data into data sets, and compiling their findings into visually compelling reports that can help improve the way a company is operating.

Potentially, Data Analysts could be included in any part of the analysis process. In a Data Analyst role, you could be involved in everything from setting up an analytics system to presenting insights based on the data you collect. Otherwise, the specific job duties of a Data Analyst will vary considerably based on the industry and the company. Although we’ve been hearing more and more about the importance of data over recent decades, businesses have actually been using some form of Data Analytics as far back as the 1800s, when Frederick Winslow Taylor began running time management exercises.

Another 19th-century example relates to the U.S. census. To collect the necessary data, analyze said data, and create a report in 1880 took seven years. To correct that, Herman Hollerith in 1890 invented the “tabulating machine,” which took punch cards and systematically processed their data. Thanks to this innovation, the 1890 census was complete in just 18 months for far less money.

Still, the real rise of data analysis occurred in the late 1970s and early ‘80s with the development of relational database management systems (RDBMS) that gave users the ability to write Sequel (SQL) to retrieve data from a database. That empowered Data Analysts to analyze on-demand and the convenience made database use more and more popular. Ultimately, cheaper, faster data collection and cheaper, faster data storage/retrieval technology led to the explosion of big data.

Data Analyst Skills

A successful Data Analyst needs to possess a mix of technical skills and more intangible qualities.

On the more technical end of the spectrum, Data Analysts do need to master SQL – the formal language used to query a set of structured data – and they should be familiar with statistical programming tools like R and Python, and of course, Excel is a must. A good Data Analyst should be able to use a program like Tableau to produce clear, esthetically impressive visualizations. And it’s hard to excel as a Data Analyst if you don’t have a head for math, including concepts like statistical regression and mathematical modeling.

However, it’s perhaps the “soft skills” or intangible qualities that really separate a good Data Analyst from a great one. For instance, a Data Analyst needs to have great business sense. A Data Analyst’s job is to understand a company’s needs and challenges and conjure smart solutions. To put it more simply, a top-tier Data Analyst sees things others don’t.

Communication is also crucial to flourish as a Data Analyst. If you can’t effectively present your insights to others in a compelling, accessible way, it will be hard to build a compelling business case that what you’ve unearthed has merit. To that same point, a good Data Analyst hasn’t just mastered the technical tools required to create beautiful visualizations -- they must also possess an eye for design. Understanding how to present your information in a clean but eye-catching way isn’t necessarily something that comes easily to everyone, but it’s crucial to thrive as a Data Analyst.

Data Analyst Education

When it comes to the educational background of a Data Analyst, there seems to be a wide degree of variance. Many people who want to become a Data Analyst are also coming into the field after completing a bootcamp, online course, or certification program to quickly immerse themselves in the field.

If they have a bachelor’s degree or master’s degree, it tends to be that – unlike Data Scientists – Data Analysts often have a background in business, economics, and the social sciences, where it’s rarer to see them come from a mathematics, statistics, or computer science background. In fact, a Data Scientist is almost 10 times as likely to possess a Ph.D. than a Data Analyst, and Data Scientists are twice as likely to hold a graduate degree. There are still people getting into the data science field after attending a data science bootcamp, as well.

That’s just one difference between the oft-confused professions. Let's take a look at related data jobs and how they differ.

Here are some related data professions and how these roles differ from what you might see in a Data Analyst job description.

  • Data Scientist. Data Scientists estimate the unknown by writing algorithms, asking questions, and building statistical models. While both are responsible for analyzing data, a Data Scientist will have to do some coding (and know some programming languages). Data Scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks. Where Data Analysts examine large data sets to identify trends and patterns, develop charts, and use data visualization to help companies make more informed, strategic decisions, Data Scientists design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

  • Data Engineer. Data Engineers typically are people who have a master’s degree in a field relating to statistics, mathematics, or computer science, or they’ve accumulated significant experience as a Data Analyst. Data Engineers need a rock-solid technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.

  • Business Analyst. Business Analysts do use data to make strategic decisions, but their duties tend to be more focused on assessing business processes for efficiency and cost, communicating what they’ve learned to high-level executives and key stakeholders, and finally offering strategic recommendations to improve processes, procedures, and performance. In simpler terms, their responsibility is to analyze data to develop recommendations that address clear business needs.

  • Product Manager. Product Managers oversee a product from ideation through to the entire product lifecycle. Although Product Managers are also expected to have a solid understanding of data analysis, once the product has matured to a certain level, that’s typically where a Data Analyst would come in to provide insights that would continue to grow and improve the product. They might make recommendations to the Product Manager, who have to consider a range of factors that a Data Analyst might not. Some Data Analysts might look to become Product Managers after gaining a few years of experience.

  • Quantitative Analyst. Quantitative Analysts are in high demand, especially in financial firms. Quantitative Analysts use data analytical skills to seek out potential financial investment opportunities or risk management problems. They often also venture out on their own, creating trading models to predict the prices of stocks, exchange rates, and commodities. Some even go on to open their own firms.

  • Operations Analyst. Usually found in large companies, Operations Analysts focus on the internal processes of a business. The scope of their job could cover internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations. Business savvy and technical knowledge of the systems they’re working with are both must-have qualities for Operation Analysts, who work in virtually every type of business, from grocery chains to the military.

  • Marketing Analyst. Data analytics plays a crucial role in digital marketing – without a proper system of analytics in place, it would be easy for companies to waste a lot of time, money, and resources on campaigns that won’t actually drive traffic. Marketing Analysts often use tools like Google Analytics, custom reporting tools, and other third-party sites to analyze traffic from websites and social media and spot trends. Performing these functions only requires a basic understanding of data analytics, but those professionals with a higher-level understanding could be set up for a very nice career.

One reason Data Analysts need to be effective communicators is that they work with a diverse cross-section of people. The wide variety of Data Analyst roles and responsibilities means you’ll be collaborating with people across many different departments, including executives, salespeople, marketers, managers, clients, and even users. You’ll also collaborate closely with people in more technical roles, including Data Scientists, Data Architects, Database Developers, and Machine Learning Engineers.

To communicate clearly and efficiently with that diverse bunch, Data Analysts have to be adept at both handling highly technical discussions and also understanding when and how to simplify their message for non-technical audiences – after all, not everyone is well-versed in data analysis. Since Data Analysts need to persuade stakeholders that their insights are worth acting upon, they should be confident and compelling speakers with a knack for presenting information.

Although it certainly does require an elusive mix of technical skills and communication skills to secure a great career in data, there are many reasons why it’s worth it to become a Data Analyst.

Reasons to Become a Data Analyst

First, let’s consider the growth of big data. About 2.5 quintillion bytes of data are created daily, and as companies are ever-more diligent about collecting and utilizing that data, they have come calling for Data Analysts.

In fact, there’s more demand for Data Analysts than there are qualified candidates to meet that demand.

That’s because companies are wisely investing big in data. According to one a recent study by Fortune Business Insights, the global big data technology market size is expected to soar from $41.33 billion in 2019 to $116 billion by the end of 2027. It’s predicted that the increasing integration of AI and machine learning will lead that market growth, with industries including retail, manufacturing, IT and telecom, government, healthcare, and utility all expected to dramatically ramp up their investments in data.

Indeed, a Dresner study found big-data analytics was being adopted in huge numbers in telecommunications (95 percent adoption), insurance (83 percent), and advertising (77 percent), especially. It’s likely that’s still not enough – a McKinsey study, for example, found that if large retailers were maximizing the potential of data, they could increase their operating margin by more than 60 percent.

The widespread demand for data analysis talent has created a situation where it’s not only easier to get a Data Analyst job, but you’ll likely have a healthy salary as well. Many Data Analysts bring home well over $70,000 – it’s even possible in an entry-level role at the right company. If you’ve got significant experience under your belt, you might fetch more than $100,000.

It’s also a position that could be considered upwardly mobile. Data Analysts have the opportunity to make high-level business decisions and work closely with senior decision-makers, and it’s not uncommon to see Data Analysts move into managerial or more specialized positions.

There’s also the fact that many Data Analyst responsibilities could actually be met working from home if a company offers that kind of flexibility.

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