How to Become a Data Analyst
How Long Does It Take to Become a Data Analyst?
Developing the skills needed to become a Data Analyst can take anywhere between 10 weeks and four years.
This range can be explained by the fact that there are many different paths to a career as a successful Data Analyst. A bachelor’s degree in computer science has traditionally been the starting point for many data professionals (that’s the four-year method), but it’s becoming increasingly common for Data Analysts to land positions directly from data analytics bootcamps and courses, which can be completed in as little as 10 weeks.
How Long Does It Take to Learn Python?
It can take anywhere from five to 10 weeks to learn the basics of Python programming, although this depends on how much experience you have with programming languages and web development.
Generally speaking, though, Python can be considered very beginner-friendly, as it is known for its readability and ease of use. It’s also easy to install the language and run it from anywhere on your machine, which makes it easy to learn it on your own.
Click here to find out more about how long it takes to learn Python.
Are Data Analytics Courses Worth It?
Yes, data analytics courses are an increasingly worthwhile investment and can help you master relevant programming languages like Python. These accelerated courses have many advantages over four-year degrees, as they allow for more hands-on learning and targeted skills development.
What’s more, the demand for data professionals has never been higher and is only expected to keep on growing. In addition to the number of new positions being created in data analytics – which number in the millions – employers also reward up-to-date data training in their existing employees, ensuring that they’re keeping up with the pace of change.
Salaries for data roles already compare favorably to other careers in tech, but even if you’re already working in the data field, boosting your skillset and gaining new specializations could bump your salary further. BrainStation’s data certificate courses were created to help professionals take advantage of these opportunities, allowing them to gain hands-on experience uncovering new insights from data sets, making data-driven predictions, and generating striking data visualization.
It’s worth emphasizing, however, that while Data Analysts enter into the field from a wide range of educational and experiential backgrounds, these positions do require a certain level of technical skill and good working knowledge of various programming languages. Though specialized, it’s still a very technically demanding field. In other words, be ready to embrace lifelong learning, as the industry changes rapidly.
Data Analyst Career Path
Data Analyst can be an entry-level position, with new Data Analysts typically entering the field straight out of school. Many Data Analysts also 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 analysis course or bootcamp.
Regardless of how they got there, new Data Scientists typically start by learning a language like R or SQL. From there, they have to learn how to build databases, perform basic analysis, and generate visualizations using programs like Tableau. Not every Data Analyst will need to know how to do all of these things but you should be able to perform all these tasks if you hope to progress in your career.
The career path for a Data Analyst depends on the industry you’re working in. Depending on the sector and the type of work you’re doing, you may choose to learn Python or R, become a pro at data cleaning, or concentrate on building complex statistical models.
You may also learn a bit of everything, which can help you take on a leadership position and progress toward a Senior Data Analyst title. With broad and deep enough experience, a Senior Data Analyst is can take on a leadership role overseeing a team of other Data Analysts. With additional skills training, Data Analysts can also in a strong position to move into the more advanced position of Data Scientist.
Jobs in Data Analytics
There are three main subfields of data jobs above – Data Analyst, Data Scientist, and Data Engineer – and they are all job titles in themselves, you can also think about them as the three main categories that most data jobs fall into. And there are many permutations of these positions, most of which constitute either an evolution of one of these roles (for example, the advancement from Data Engineer to Data Architect) or a specialization within them, often based on sector (such as the specialization from Data Analyst to Business Intelligence Analyst).
Let's take a closer look at some common data jobs:
As the name suggests, Data Analysts analyze data – but that short title only captures a tiny part of what Data Analysts can actually achieve. For one thing, data seldom starts out in an easy-to-use form, and it’s typically Data Analysts who are responsible for identifying the kind of data needed, gathering and assembling it, and then cleaning and organizing it – converting it into a more useable form, determining what the data set actually contains, removing corrupted data, and evaluating its accuracy. Then there’s the analysis itself – using different techniques to examine and model data, look for patterns, extract meaning from those patterns, and extrapolate or model them. Finally, Data Analysts make their insights available to others by presenting the data in a dashboard or database that other people can access, and communicating their findings to others via presentations, written documents, and charts, graphs, and other visualizations.
Data Architects are responsible for building the ecosystem in which data science actually happens – holistically designing the overarching infrastructure of systems and platforms, databases and applications that administrators and analysts rely on. They also maintain and improve these systems as needed. In brief, a Data Architect manages the ways in which an organization is able to use its data.
Enterprise Architects occupy the intersection between a business’s strategic planning and its technological systems, working to understand the business’s real and abstract goals, translate them into a development strategy, and finally execute a program to ensure that the company’s use of technology furthers those goals. In short, it’s a research-intensive position focused on making sure the company has the technological tools it needs to succeed.
Applications Architects are concerned with the specific applications used within a business or other enterprise’s data systems; they design these applications from infrastructure to interface, build and implement them, and then track how users interact with them in order to make improvements over time. Applications Architects put together the pieces that make up the data system’s architecture as a whole.
Machine Learning Engineer
Working in an area of concentration within data science, Machine Learning Engineers use their strong programming and data analysis skills to design and build machine learning systems, then use them to run tests and experiments. They’re also responsible for monitoring these systems and maintaining their operation.
A highly specialized Data Analyst, a Quantitative Analyst expertly uses mathematical and statistical methods to manage risk. As you’d expect from a risk-management position, most Quantitative Analysts (or “Quants”) work in finance, mining large sets of data – everything from security pricings to hedge fund returns – to yield market insights. Besides strong programming and statistics skills, Quantitative Analysts also need to have a strong footing in business theory.
Business Intelligence Analyst
Like Quantitative Analysts, Business Intelligence Analysts are concerned with business data. Business Intelligence Analysts, though, take a more data-first (as opposed to strategy-first) approach; while Quantitative Analysts may extrapolate and look to the future, Business Intelligence Analysts are more focused on past trends, basing their calculations on what’s hidden in the data already on hand. A Business Intelligence Analyst might ask “What can we learn from the historic trends in this area?” while a Quantitative Analyst asks “What are some of the unforeseen ways we might apply this information?”
Working within the field of data analysis, Statisticians are focused on using statistical methods to understand data; in that sense, they’re simply more focused on one of the fundamental aspects of data science as a whole – but in this, their expertise is extremely wide-ranging. Statisticians may collect, analyze, and interpret data for any one of a vast number of purposes, from scientific inquiry to guiding a business’s decision-making. This makes Statistician a data science job that can be applied to virtually any sector, from business to healthcare to education to physics to government and the public sector.
As their respective titles imply, Business Analysts and Business Intelligence Analysts share quite a bit in common; both comb through a company’s data for actionable insights. What distinguishes them is that, while a Business Intelligence Analyst looks for patterns that show long-term trends with the goal of guiding a company’s strategic decision-making, a Business Analyst is concerned more with the way the business currently operates – helping to improve the processes and protocols of a particular department to improve effectiveness or efficiency, for instance.
The shorthand for their respective domains is how best to run the company internally (Business Analyst) versus what external actions the company should take (Business Intelligence Analyst).
Like a Business Analyst, a Systems Analyst is concerned with how a company operates. A Systems Analyst, however, is more squarely focused on how the company uses information technology. That is, they identify the ways an organization’s IT, communications, and data systems should be set up or improved, then work with internal stakeholders to execute those changes. While much of a Systems Analyst’s time may be focused on hardware or operating systems, their recommendations are guided by data, as they first try to quantify and understand the company’s strengths and weaknesses, to identify trends that will affect their systems’ operation in the future, and to meet the company’s technical requirements and optimize the performance of its IT solutions.
Like a Systems Analyst, an Operations Analyst seeks to improve how a company does what it does – its internal operations. But while the former designs IT systems, the Operations Analyst is focused on the processes by which the company functions, including how information is shared, how protocols are executed, even its organizational structure and managerial hierarchy. The role of Operations Analyst actually predates data science, at least in its modern form, but today, the sociologists observing workplace dynamics with clipboards in hand have largely been replaced by data professionals able to make recommendations for how to streamline a business’s activities by interpreting the reams of data it generates.
Marketing analysis is a subfield of data analysis that’s primarily limited to sales and marketing figures, with an eye to improving the performance of a company’s various marketing strategies. This is applied data analysis in its purest form: correlating the raw numbers representing pricing, sales, distribution, advertising, traffic, conversion, and anything else to do with how a company positions and promotes its products, then identifying patterns among those numbers to generate meaningful insights that can be used to improve the return on marketing investment dollars in the future.
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Recommended Courses for Data Analyst
The part-time Data Analytics course was designed to introduce students to the fundamentals of data analysis.
Taught by data professionals working in the industry, the part-time Data Science course is built on a project-based learning model, which allows students to use data analysis, modeling, Python programming, and more to solve real analytical problems.