With data analytics being relied upon by so many companies across industries, data analysts are now an in-demand role on tech teams. But what does data analytics encompass? And how do you become a data analyst today? BrainStation Founder & CEO, Jason Field, answers all of our questions about the typical responsibilities of today’s data analysts, which technical skills (and soft skills!) a data analyst needs to learn, and trends to expect in 2023!
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At a high level, data analytics is the process of collecting, organizing, and interpreting groups of information or data points to discover trends and areas of opportunity, and then create actionable insights.
How does a data analyst operate within a technical team?
We’re at a point now where all organizations can benefit from having someone trained in data analytics because the skillset can help you better understand your business and audience. The types of responsibilities and projects a data analyst could work on, therefore, are endless. Responsibilities can vary by industry, by current business objectives, or simply by trying to find the answer to a question.
For example, retail brands need data analysts to keep track of inventory sold, forecast what types of SKUs they should be making more of, and which ones to retire.
For Saas (software-as-a-service) companies, data analytics is a pillar of their business. Data analytics gives them the ability to organize data and help their clients leverage these insights to a competitive advantage. The responsibilities and daily tasks can vary, but the importance is consistent and growing very rapidly.
What makes a good data analyst? What would you say are the most important data analyst soft skills?
Data analysis is ultimately about both retrieving and expressing the information contained in data, and that second part sometimes gets forgotten. In addition to the more technical skills like SQL, Excel, and some statistical programming languages to help sort the data, knowing how to communicate your findings is just as important.
Visualization tools like Tableau are great for presenting seemingly complex datasets in a more intuitive way such as charts, graphs, and maps. You don’t need to be an artist to make a great-looking graph to enhance your presentation to your team.
Analysts also need to have good communication skills to make their conclusions understandable and compelling. We make sure in our Data Analytics course to highlight the importance of these soft skills (in addition to the technical skills), as these are often what sets a good data analyst apart from a great one.
What are the most common data analyst tools used by professionals in the industry?
I’ve mentioned a few like Tableau and mySQL, but there are many well-loved tools by experts that help them excel at their jobs, like PowerBI, Bokeh, Plotly, and Infogram. Programming languages like R and Python are also commonly used by data professionals as these are great for modeling and testing.
Do data analysts need to code?
You don’t need to have a coding background to be a data analyst. Skills like creative thinking, communication, and attention to detail are great starting points and help to craft the right questions. The technical skills and programming languages that will help you excel at your job as a data analyst can be taught. Several of our students have come to us without coding experience and have moved on to work for world-leading companies as part of a data team.
What is a typical data analyst career path? Do you need a college degree to become a data analyst?
Traditionally, most entry-level data analyst jobs have required at least a Bachelor’s degree, but that is changing quickly. We’re now seeing a shift where employers are looking for proven skills and experience with industry tools. I would actually argue that the right hard and soft skills are even more important than a degree when it comes to landing a role in data analysis today. This is partly why the demand for our Data Analytics course has been so overwhelming; it allows aspiring data analysts a chance to build job-ready skills and get the experience they need to start working on real-world projects.
What is the difference between data analytics and data science?
A Data Scientist is generally a more senior position involving more technical expertise. They are typically focused on building models to try and predict the future.
A Data Analyst, on the other hand, can be considered a more entry-level field and is generally more focused on analyzing trends of the past to help with the future. Data analytics roles are excellent for people with the right skills interested in advancing their career in data but who don’t have a degree.
Our Data Analytics course was carefully designed to allow professionals to develop essential data skills and knowledge, including the fundamentals of data analysis, and how to work with industry tools and complex datasets. With more data being generated every day, businesses need data analysts to make sense of this information and then effectively communicate their findings. The course will help you develop these increasingly indispensable skills.
Our teams are constantly talking with Instructors and other industry leaders to make sure we’re teaching tools and software that are currently being used in the workplace. We want to empower our learners to go from our classroom to a boardroom, knowing that they have the tools and experience to tackle the tasks that will be asked of them.
Do students work on projects in the Data Analytics course?
Each week in our data analytics classes, students learn and implement skills required to complete a final project. The goal is for them to have implemented each step of the data analysis process from start to finish, leveraging MySQL and data visualization tools, and presenting their findings just like you’ll be expected to do in a data role. By the end of the Data Analytics course, learners have a polished data analyst portfolio of real-world work and completed projects.
Is there an ideal candidate for the Data Analytics course?
Our certificate courses are geared towards people looking to upskill, whether that’s to learn the basics for the first time, to refresh previous learning, or to see if a career in data would be a good fit for their next move. If someone has an interest and is motivated to learn, we welcome them into our classroom.
For those who are interested in a more intensive learning experience, I’d recommend our Part-Time Data Science Bootcamp or our Full-Time Data Science Bootcamp, which were created to be immersive, career transformation experiences.
Can students complete the Data Analytics course while working a job?
Yes, our courses are designed so professionals can learn and build new skills while working. As part of our mission to increase accessibility to digital skills training, we offer a range of different schedules and learning formats, including weekday and weekend courses.
Who teaches the Data Analytics course? Do they have industry experience?
Our Instructors are the best of the best. We have an immersive interviewing process that ensures our learners are only exposed to the most elite industry experts that also excel at teaching and are passionate about helping others. Students can expect to see subject matter experts from global brands such as Google, Amazon, Shopify, and Meta in our classrooms.
The adoption of AI and machine learning is a growing trend in the data sphere, but digital transformation remains the biggest opportunity and challenge for organizations around the world – and this in turn means demand for data professionals and data analytics will continue to soar.
Recent reports had more than 70% of organizations developing or implementing a digital transformation strategy, including adopting digital processes and channels, but also investing in areas like UX design, digital marketing, and product management, among others. The common thread in all of these initiatives is data. You need to understand your users and their needs to be able to create a great experience or product, and to then build your brand and audience. In fact, the organizations that best navigated the challenges brought on by COVID-19, were those that understood this and ensured their workforce had the data skills needed to thrive in a digital-first model.
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