Nearly every industry utilizes data in some capacity, which is why there’s such a high demand for data science professionals. With mind-boggling amounts of data at our disposal, the world needs folks who can make sense of all the stats and figures.
One in-demand career in data science is a Data Analyst, someone who uncovers what’s happening behind the numbers, and answers questions with data. Data Analysts use programming languages and other tools to work with large amounts of data and communicate their findings — and you might have exactly the skills and traits you need to enter the field.
Codecademy’s data science career paths are an awesome place to start if your ultimate goal is to get a job. We’ll guide you through all of the technical skills that specialists in these professions use, plus give you practice projects that you can use to build a professional portfolio.
The newest career path, Business Intelligence Data Analyst, is the quickest way to learn the data analytics skills necessary to get a job in the field. It’s tailored for beginners, and you’ll learn how to code like a Data Analyst with SQL and Python. So if you haven’t taken a math class in years or have never written a line of code, we’ve got you covered.
The role of a Data Analyst
You can’t always tell what a person does all day based on their job title alone — and that’s definitely true in the data science domain. For example, a Data Scientist and a Data Analyst are technically different jobs, though the terms are often used interchangeably. While a Data Scientist is an umbrella term that could refer to careers like a Machine Learning Specialist or Inference Specialist, a Data Analyst focuses on answering questions using data. (BTW, if you’re interested in pursuing those other data science specialties, we have career paths that will walk you through step-by-step how to become one.)
A Business Intelligence Data Analyst, for example, uses statistics and analytics to write reports and build dashboards so that organizations can turn data into actionable insights. In today’s data-driven world, every kind of business needs BI Data Analysts who are experts at taking huge amounts of data, investigating it for trends, and telling a meaningful story that informs business decisions.
Data analytics tools to learn
Compared to other data science roles, BI Data Analysts typically don’t need as much programming experience. But there are still some key coding and technical skills that you need to have in order to collect, manipulate, analyze, and interpret data:
- SQL: Short for “Structured Query Language,” SQL is a standard database management language that’s used to query and manipulate data in relational databases like spreadsheets.
- Python: With Python’s built-in modules and libraries (like Pandas and MatPlotLib), BI Data Analysts can use the general-purpose programming language to analyze and visualize data.
- Microsoft Excel: Business and spreadsheets go together like PB&J. As a BI Data Analyst, you’ll need to know how to seamlessly load data into Microsoft Excel and use it to answer numerical questions.
- Tableau: “The business world is run on dashboards,” Michelle says. You should know how to use Tableau, the data visualization software that’s standard in business intelligence. Knowing how to compose a Tableau dashboard that tells a clear, coherent story is a must-have for BI Data Analysts, she says.
You’ll learn how (and when) to use all of these data analytics tools in the BI Data Analyst Career Path. If you’re starting from scratch, or have never been super comfortable with math and numbers, don’t stress: We’ll guide you through everything you need to know to use these programming languages and tools.
3 traits that make successful Data Analysts
Here are some personality traits and soft skills that will help you shine as a Data Analyst.
Good news for career-switchers: Your past work experience and domain knowledge is your secret weapon when you’re trying to land a BI Data Analyst job, because you’ll be able to contextualize even further what data means to a business, Michelle says.
For example, if you worked in restaurants for 15 years, you know how a restaurant operates, what makes restaurants successful, and what different data points represent in food service. Your domain knowledge would give you an edge if you were applying to a job as a BI Data Analyst in restaurants, because you “understand the relationship between the numbers on the page and the real-world values,” Michelle says.
So whether you have experience in the arts, medicine, engineering, food service, education — you name it, your past jobs or passions could serve you in a BI Data Analyst role.
Being able to translate complicated data topics into a clear story that makes sense to a wide range of audiences is a huge asset for BI Data Analysts. For example, a BI Data Analyst might have to share their findings with non-technical departments that may not be fluent in data vocab and concepts. On top of that, you have to know what the most relevant information is for a particular group; you wouldn’t talk to the company’s leadership team about data the same way you would your peers, for instance, Michelle says.
Most of us have practice tailoring our communication to different audiences within the workplace, Michelle says. “Those soft skills are immediately transferable,” she says.
The most important soft skill for a BI Data Analyst to have is curiosity and enthusiasm to ask questions, Michelle says. It’s one thing for a BI Data Analyst to create a data summary or pull performance numbers for a company — that’s all part of the job. But a really successful BI Data Analyst will take it a step further by segmenting data in different ways, asking questions about what the data really means, and considering alternative data stories that could come out of data.
Curiosity is also what makes the job exciting, Michelle says. “You’re always asking and answering questions, and that feels empowering,” she says. “The ability to take a bunch of observations [and] a dataset and turn it into something that’s actionable — that feels good.”
What to expect when you apply to a Data Analyst job
Ready to start job-hunting? Before you apply, you’ll need to prepare a Data Analyst resume that includes your technical skills, past work experience, relevant courses you’ve taken, and data science projects you’ve completed. Don’t forget to read the job description closely and see what the required technologies and tools are so you can list the ones you know on your resume. It also helps to take the extra time to write a Data Analyst cover letter — not only will a cover letter showcase your communication and storytelling abilities, but it also is your chance to express why you’re the best candidate for the role.
Along with your resume and cover letter, you’ll be expected to provide a portfolio of Data Analyst projects you’ve completed. Your portfolio is your opportunity to provide tangible proof of all of your skills and accomplishments. If this is your first time applying to a Data Analyst job, you can absolutely use Codecademy projects in your portfolio. In Codecademy’s BI Data Analyst Career Path, you’ll get to complete four projects that you can use in your Data Analyst portfolio, and we also have lots of other data analysis projects you can complete and add to your portfolio.
Making it to the interview is a super exciting, but sometimes intimidating, stage of a job application. As part of a Data Analyst job interview, you’ll probably have to complete a technical interview where you’ll be asked to solve a data problem using code. It sounds nerve-wracking, but the most important thing to remember is that interviewers want to see how you’d approach solving the problem, not necessarily the exact right answer. Hot tip: Start practicing for your technical interviews by reviewing these common interview questions for Python and SQL, and complete these Python and SQL code challenges.
Beyond that, you might be asked to answer open-ended questions about common data concepts and how you’ve applied them in the past. The hiring manager will also ask you behavioral questions about why you want to become a Data Analyst, how you handle problem-solving, and what interests you about the organization. Be sure to read this blog about common data scientist job interview questions to get familiar with the relevant behavioral and technical questions you might face.
Start your journey to becoming a Data Analyst
Feeling inspired about the career possibilities within data science? Get started today with Codecademy’s new BI Data Analyst Career Path — it’s the fastest way to jumpstart a career in data analytics. You’ll learn the coding concepts and tools that BI Data Analysts use in their jobs, and get hands-on practice creating portfolio-ready projects with Python, Excel, and Tableau.
As you’re thinking about your next career move, be sure to check out these blogs for tips on what to include on your Data Analyst resume, how to build a Data Analyst portfolio, and common Data Analyst questions you’ll be asked in a job interview.