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Why You Should Consider a Career in Data Analytics

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Singapore learner on laptop

Alert: approaching maximum storage capacity. 

The world’s data use increases each year, with a forecast of 147. zettabytes created, consumed, and stored in 2024 – which is enough storage for 55 billion 4K movies.

This is a good thing – right? More data means more innovation, which means more advancements for society. 

Not necessarily. 

Think of data the same way you think about a library. There are so many books in one place (which is awesome) but it’s only useful if you know: 

  • How to find the information you need.
  • And how to apply it. 

Businesses have more data than ever before – about their company, their customers, and the world – but no one to tell them what it means. 

That’s where data analysts come in. 

WHAT DO DATA ANALYSTS DO? 

When there’s a problem, data analysts help solve it. 

The first step to addressing business challenges is gathering information (data) and finding answers and insights to guide companies towards better decisions. That’s the role of the data analyst. 

For example, a company may want to know which segment of customers is driving the most revenue from a marketing campaign. 

The data analyst will gather all the data related to the campaign. This may mean exploring customer demographics, marketing acquisition sources, behavioural data, and purchase data. 

They’ll look for notable statistical findings. They’ll form these into insights and create written and/or visual reports to help stakeholders learn and apply the findings to their future campaigns. 

As a distinction from data scientists, data analysts typically work with structured data from a single source and provide historical analysis as opposed to predictive modelling.

WHY SHOULD YOU CONSIDER A DATA ANALYTICS CAREER? 

The most obvious reasons to work in the field of data analytics include these top three reasons: 

  1. You’re dealing with data, numbers, and statistics, but you still get to creatively work to solve problems. 
  2. You’re paid well for this skill.
  3. Data keeps growing and so will the need for data analysts. 

But there are other benefits that may not be quite so apparent: 

  • Most employers are interested in talent with skills. There is not a big focus on degrees and further education. 
  • Many data analyst jobs are remote. No more commuting!
  • The technical skills you learn are easily transferable to other jobs like coding, data science, and more. 

5 TOP JOB TITLES FOR DATA ANALYSTS

What kind of jobs can you get as a data analyst? There are varying specialities and job titles in the field of data analytics. Here are some job titles you may see in this family of jobs:

DATA ANALYST

Related job titles: Junior Data Analyst, Entry-Level Data Analyst, Associate Data Analyst

You can find a data analyst at nearly every company in the world, in every industry imaginable. The average data analyst needs to know some basic programming languages like Python and SQL, and they should be comfortable running statistical analyses and visualising data. 

OPERATIONS ANALYST 

Related job title: Operations Research Analyst 

An operations analyst focuses on the inner workings of a business, helping it run more efficiently. They typically work for larger companies or they work at consulting firms employed by bigger businesses.

MARKETING ANALYST 

Related job title: Market Research Analyst 

One of the biggest parts of any company’s budget is the money they spend on marketing efforts. A marketing analyst looks at market, campaign, and demographic data to ensure companies are executing marketing efforts in the most cost-effective and impactful way possible.

BUSINESS INTELLIGENCE ANALYST 

You’ll spend your days as a BI analyst looking for patterns in your company’s data. You’ll have to make sure you’re good at communicating and that you enjoy visualising data and modelling future scenarios. 

Is a business analyst the same as a data analyst? While the skill sets are similar, there are some differences. Here’s our take on business analyst vs. data analyst. 

LOGISTICS ANALYST

Logistics analysts look at every stage of a production process and product lifecycle. They may analyse supply chain flows and find areas of improvement to increase efficiency and profit for a company. 

DATA ANALYST CAREER OUTLOOK 

Companies, including retailers, investment banks, big tech, and professional services (including accounting and insurance), are all ramping up their data analytics workforce. Other industries hiring for data analysts include logistics, healthcare, government, and sports. 

ARE DATA ANALYST JOBS IN DEMAND? 

The Singapore Economic Development Board (EDB) stated that the data science industry in Singapore contributes an estimated $730 million (USD) to the economy annually. Operations research analysts and market research analysts are also high-growth job categories. 

WHAT’S THE AVERAGE SALARY WORKING IN DATA ANALYTICS? 

In Singapore, the median salary for a data analyst is SGD $99,000, with the middle 50% earning between SGD $75,000 and $137,000. Of course, how much you can earn as a data analyst depends on several factors including education, experience, industry, and geography. For example, the median data analyst salary in the United States is $113,250, with the middle 50% earning between $93,000 and $134,000. In Australia, the typical data analyst salary is in the range of AUD $114,500 and $143,500

Experience and industry can also have an impact on your expected salary. An entry-level data analyst in Singapore’s financial services industry, for example, earns a median salary of SGD $60,000, while a senioranalyst in the same industry earns SGD $74,000 and a director in analytics earns SGD $132,000. 

HOW TO BECOME A DATA ANALYST

Most data analytics jobs require a bachelor’s degree. Degree programs in mathematics, statistics, business, or economics are ideal, but college grads can re-skill for data analytics with any major. 

There have never been more options for individuals to skill up for a career switch, and some employers will even pay for it because of fast-changing business needs. Here are twoways to gain the data analytics skills you need to fast-track a new career in this field:

#1: PART-TIME DATA ANALYTICS COURSE

If you have a full-time job or other responsibilities, a part-time course can be a good option and offers accountability for a set curriculum and timeline. However, the part-time model takes longer to finish and longer to reach the job market than a full-time option. 

#2: FULL-TIME DATA ANALYTICS BOOTCAMP

What is a data analytics bootcamp? Bootcamps provide immersive, intensive training for entry-level professionals in a field. Bootcamps can be in-person or online and are instructor-led, often with multiple speakers and mentors for a course and a cohort. 

So, which option is the best for you? It really depends on your background and learning style.  

If you have transferable skills and experience, you may only need to brush up on a programming language like Python to make the leap. If you’re coming from an unrelated field or from a career break, a more immersive, structured program like a bootcamp may be your best bet to get job-ready. 

GO FROM BEGINNER TO DATA ANALYST IN 12 WEEKS

General Assembly’s Data Analytics Bootcamp is designed for complete beginners. Get hands-on training from actual data analysts working in the field, and graduate in just 12 weeks ready for your first data analyst job. It’s the most direct route to your new data analyst career.

How Technology Powers Today’s Largest Global Events

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Technology is having a significant impact on music festivals, award shows, and sports games, both in terms of enhancing the experience for attendees and making these events more accessible to a wider audience.

In music festivals, technology has enabled organizers to create more immersive and interactive experiences for attendees. Similarly, technology has transformed award shows by allowing audiences to interact with the event in real time. Technology has also significantly impacted sports games, both on and off the field. 

Tech such as AI (artificial intelligence) and VR (virtual reality), amongst many others, is dramatically shaping the world of entertainment, transforming the way we consume and create content, as well as changing the nature of the entertainment industry itself. 

This blog will look at specific examples of how technology has transformed some of the entertainment industry’s biggest events, how this growing industry is increasing the demand for new tech-savvy creatives, and the best job opportunities in 2023. 

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Is a Data Science Career a Good Fit for You? Here’s What You Need to Know.

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So you’re thinking of a career in data science, but you’re not sure if it’s the right fit for you.  Here is your data science guide, where we break down what data science is, day in the life of a data scientist, tips from GA’s data science alumni, career opportunities, and much more. 

WHAT IS DATA SCIENCE? 

According to Berkeley, data science is the ability to take data, understand it, extract value from it, visualize it, and communicate the findings. The term “data science” was coined in 2008 when companies realized the need for data professionals to analyze immense amounts of data. 

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How to Become a Data Analyst in 4 Steps (No Degree Required)

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Making a career change can be scary, especially if self-doubt of “I’m not good enough” starts creeping in. However, there is no point in staying in a job or career that no longer brings you joy or fulfills you professionally. 

If you’re reconsidering your career, you’re not alone — over the last two years, over 50% of employed Americans have considered a total career revamp. Chances are, you know a relative or friend who is going through a similar career dilemma right now.

If you’re considering making a bold move to data analytics, we’ve got you covered. Understand if a career in Data Analytics is right for you in four easy steps.

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A Beginner’s Guide To Tableau

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Estimated reading time: 3 minutes

Featuring Insights From Iun Chen & Vish Srivastava

What is Tableau

Tableau is a powerful data analysis and data visualization tool that anyone can use. It can be used by beginners to create simple charts and by advanced practitioners to solve complex business problems. It is user-friendly, easy to learn quickly, and includes a portfolio of business intelligence tools with the potential to give a wide range of roles the advantage of professionally analyzing data.

Simply put, if you can present data in a clear, compelling format, you gain a competitive advantage in today’s data-driven marketplace.

“Tableau enables you to quickly connect disparate data sources and utilize a drag-and-drop interface to analyze data and create dashboards,” says Vish Srivastava, who leads our Data Visualization & Intro to Tableau workshop. As a product leader at Evidation Health, he relies on Tableau to turn around fast data analysis. “For example, product teams use it to analyze user growth and analytics, BizOps teams use it to analyze operational data, and sales teams use it to analyze customer and revenue data.”

Businesses survive and thrive on data. The amount of data available to businesses today is impressive. To keep organizations on a successful path, analysts need to provide the key insights needed to make important decisions.

Here’s where Tableau comes in.

Tableau takes business intelligence to the next level, making it fast and efficient to analyze large amounts of data and create beautiful, presentation-ready visualizations that generate insights.

Data is the lifeblood of modern teams. Being able to quickly answer ad hoc questions and integrate data analysis into your day-to-day decision-making will make you an MVP. Though not all data analysts use Tableau, they do need some way to quickly create data visualizations.

Tableau is the data viz tool of choice.

Tableau is so popular in part because it is easy and fast to learn. In Iun Chen’s Intro to Data Analytics course, students learn the life-changing basics of Tableau in an afternoon. Aspiring analysts come to understand the power of data and the impact their numbers can have. As more data becomes available, there are more opportunities for data to be misused, a risk that every data scientist soon realizes. To quote the Nobel laureate and economist Ronald Coase, “If you torture the data long enough, it will confess.”

The ethics of data form the foundation of Chen’s syllabus so pitfalls are avoided from the start. “Overanalyzing and manipulating data too deeply can always give you the information you want,” says Chen. “Unfortunately, this is all too common in professional settings, though it’s usually unintentional.”

What features does Tableau offer?

  • Tableau Accelerators
  • Data Stories
  • Predictive Modeling and more

Tableau is a powerful tool.

Business insights are only as good as the data behind them, and the best data analysts understand that the human choices they make matter.

“Data is the perfect example of garbage in, garbage out,” says Srivastava, who defines good data as data that is ethically collected, complete, objective, and thoroughly analyzed. ”The double-edged sword of using powerful data analysis and visualization tools is that beautiful charts can create a false precision and obfuscate data integrity issues.”

To delve deeper into this topic, Chen recommends How Charts Lie, by Alberto Cairo, an exploration of how data can be altered:

“This book details how the use of data and data visualizations in journalism can be distorted and misleading, without the audience even realizing it, due to the urgency to present findings in a timely manner to the public.”

Want to learn more about Iun?
https://www.linkedin.com/in/iunchen 

Want to learn more about Vish? https://www.linkedin.com/in/vishrutps

7 Tips to Learn Tableau Fast

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Featuring Insights From Iun Chen & Vish Srivastava

Read: 2 Minutes

Let’s get it straight: How difficult is it to learn Tableau for a complete beginner? Are there shortcuts to learning Tableau? Any tips, tricks, or time-saving work-arounds? Thankfully, the answer is yes. Try these top tips, approved by our expert instructors, and start data viz now.

“It’s a little overwhelming at first but as soon as you understand the basics, like what are dimensions and measures, everything falls into place pretty quickly,” says Vish Srivastava, product leader at Evidation Health and GA instructor.

“In essence, you need to understand two things: The basics on how data works — for example, what are common formats of data and what is a primary key? And a basic understanding of data visualization in a business setting. Can you answer the question: When is a time series vs. a pie chart valuable for decision making?”

But can you really learn the basics of Tableau in an afternoon?

“The best way to learn is to download a sample dataset and dive right in and start creating data visualizations. To keep going from there, check out various portfolios online to get inspiration, and try to build those.”

According to Iun Chen, who conducts internal Tableau training at LinkedIn, Tableau is easy to learn, but hard to master.

“The basic concepts of charting and color theory are easy to pick up and can take just a few weeks. However, if you are looking to be a subject matter expert, this can take years to perfect,” she says. 

Chen preps students in her Intro to Data Analytics course to achieve close-to-mastery in these key areas.

  1. Can they quickly prep and analyze large volumes of data?
  2. Identify key information and determine the best visual method to present them?
  3. Take business questions and determine which visualizations to use?
  4. Translate raw datasets to storylines with a beginning, middle, and end? 
  5. Format charts, graphs, titles, text, and images for a polished deliverable? 
  6. Articulate best practices on design and visualization techniques?
  7. Provide feedback on ineffective visualizations and how to improve them?

    This checklist is the closest thing to a Tableau cheat sheet you’ll find. Prioritize these skills, and you’ll waste no time learning Tableau. Now that you know what you need to succeed, you can choose whether to take our Data Analytics course fast or slow. Learn Tableau — along with data analytics tools SQL and Excel — in a 1-week accelerated format, or over 10 weeks in the evening.

Chen sums it up perfectly: “As long as you are actively learning, applying your learnings, and ensuring innovation of your work, you will be a data visualization expert in no time.”

Want to learn more about Iun?
https://www.linkedin.com/in/iunchen 

Want to learn more about Vish? https://www.linkedin.com/in/vishrutps

Top 3 Reasons To Learn Tableau

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Featuring Insights From GA instructor Candace Pereira-Roberts

Read: 2 Minutes

Do you communicate data? Do you want to create more effective data visualizations? Tableau is the data analytics tool you’re looking for. Here are the top three reasons why you should learn how to use Tableau, the popular data viz software focused on business intelligence. Read on for the advantages of being a Tableau professional.

#1 Tableau Is Easy

Data can be complicated. Tableau makes it easy. Tableau is a data visualization tool that takes data and presents it in a user-friendly format of charts and graphs. And here’s the rub: There is no code writing required. You’ll easily master the end-to-end cycle of data analytics.


Need to showcase trends or surface findings? Tableau will make you an expert. Proficiency in business intelligence is a transferable skill that is quickly becoming the lifeblood of organizations. 

“I see students who are new to analytics learn Tableau desktop and be able to develop Tableau worksheets, interactive dashboards, and story points in a couple of weeks — essentially a complete data analysis project,” says Candace Pereira-Roberts, FinServ data engineer and one of our Data Analytics course instructors. She adds, “I like to share knowledge and watch people grow. I learn from my students as well.” 

 #2 Tableau Is Tremendously Useful

Would you rather tell visual stories with data? Or present the same old boring reports and tables? Is that even a question?

“Anyone who works in data should learn tools that help tell data stories with quality visual analytics.” Full stop.

The smart data analyst, data scientist, and data engineer were quick to adopt and use Tableau tool by tool, and it has given those roles a key competitive advantage in the recent data-related hiring frenzy. But their secret is out. And the advantages go beyond the usual tech roles. Having a working knowledge of data, and specifically knowing how to use Tableau, can help many more tech professionals become more attractive to recruiters and hiring managers.

Plus, it has a built-in career boost. Tableau’s visualizations are so elegant, you’ll be confident presenting the business intelligence and actionable insights to key stakeholders. Improving your presentation skills is par for the course.

#3 Tableau Data Analysts Are in Demand

As more and more businesses discover the value of data, the demand for analysts is growing. One advantage of Tableau is that it is so visually pleasing and easy for busy executives — and even the tech-averse — to use and understand. Tableau presents complicated and sophisticated data in a simple visualization format. In other words, CEOs love it.

Think of Tableau as your secret weapon. Once you learn it, you can easily surface critical information to stakeholders in a visually compelling format. That will make you a rockstar in any organization. 

“Tableau helps organizations leverage business intelligence to become more data-driven in their decision-making process.” Pereira-Roberts says. She recommends participating in Makeover Monday to take your skills to an even higher level. 

Take Our Free 2-Hour Data Visualization Class

Want to learn more about Candace? Check out her thoughts on how to become a business intelligence analyst, or connect with her on LinkedIn.

What Is Data Visualization?

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An Interview With Iun Chen

Read: 4 Minutes

Data is big, and it’s getting bigger. How do you parse and understand data when the sheer amount of information can be overwhelming? The answer is data visualization. Using concepts of design theory like elements of color and layout, the discipline of data visualization, or data viz, is essentially the graphic representation of data. We called on one of our data viz experts, Iun Chen, to break it down further. 

Let’s start with an introduction and how you came to the world of data viz.

IC: I’m Iun (pronounced ‘yoon’), and I work in the data analytics space focusing on business intelligence tools and building scalable resources for LinkedIn. I also teach the 10-week Intro to Data Analytics course for GA, which includes the professional skills of SQL, Tableau, and Excel.

In college, I was a business major with a specialization in marketing and advertising. I became more interested in how the ad business model worked behind the scenes and in how software and systems worked. As a result, I worked at many major media companies in a quantitative capacity — revenue planning, ad pricing, finance, ad sales strategy. That led me into a formalized analytics route.

How do you define data visualization?

IC: Data visualization is the idea of communicating information graphically. It’s the science of information design, in which you take massive amounts of data in whatever format it comes in and use it to surface high-level insights and findings in a visually compelling way so audiences can easily understand the main points.

How does data visualization differ from data analytics?

IC: Data analytics is the process of cleaning, prepping, analyzing, and presenting data. Data visualization is part of the presenting data step and is defined as the act of visually organizing data through the use of charts, graphs, and dashboards. Concepts of data visualization are closely aligned with concepts of design theory: color, font, scale, layout, organization.

Why is data viz important?

IC: Data visualization is easy to learn but hard to master. In my classes, I heavily emphasize the design element of data visualization. It’s easy to whip together a quick bar or pie chart, but is it the best way to communicate the point you are trying to make? The goal of collecting mass amounts of data is to be able to quickly translate it into insights that can help make smart business decisions. The final form of this translation is often a chart or graph, which is why the ability to design and visualize these mass amounts of data grows as we collect more of it.

What is a data narrative?

IC: People think in stories and narratives, not in black and white figures. Just like you would share a story with a friend using a beginning, middle, and endpoint, you would do the same when sharing details about data analysis. Here’s a simple example.

  1. Beginning: Sales are down year-over-year; identify the symptoms.
  2. Middle: Furniture sales — our largest segment — are doing poorly in the last six months; conduct the analysis to investigate reasons and uncover root causes.
  3. End: Review retail store reports and conduct manufacturer visits; recommend next steps.

The key point to any data narrative is that it should present a compelling business case and surface unrealized insights to the audience. The business challenges, rationale, and next steps should be clearly presented, and people in the room should be able to walk away and know what to action on. 

Which tech roles use data visualization?

Data visualization — like data analytics — is a skill set that can be applied to any job. But if you are looking for a job that has data visualization skills as part of the function and responsibilities, look for roles like business analyst, data analyst, business intelligence analyst, data scientist, and data engineer. Keep in mind that the formal skill of data visualization is still relatively new, so depending on the maturity of the company, those functions may not be fully established yet. However, with the increase of data in the world, there’s a growing need for experts who understand data visualization techniques more and more.           

Check out this Medium post which details how Spotify’s business has evolved with the creation of their data visualization roles.

What’s the future of data visualization?

As we continue to collect more and more data, the need for people with the skills to analyze and present data becomes ever-growing and critical in the workplace environment. More companies will need to generate insights quickly to keep up with advances and competition in their respective industries. The skill of data visualization will become more and more attractive as teams and organizations seek to translate their data into insights more efficiently and effectively. The ability to work with data is increasingly critical to the success of any company in any job function. 

Iun Chen’s Recommended Data Viz Reading List

FlowingData

StorytellingWithData

InformationIsBeautiful

Tableau Public Gallery

New York Times Data Journalism

The WSJ Guide to Information Graphics

Storytelling with Data: A Data Visualization Guide for Business Professionals 

Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations

Edward Tufte’s The Visual Display of Quantitative Information

Want to learn more about Iun?
https://www.linkedin.com/in/iunchen

Business Analytics Vs Data Analytics: What’s the Difference?

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Featuring Insights From Iun Chen & Vish Srivastava

Read: 4 Minutes

Data analytics and business analytics are often confused, understandably, because both data analysts and business analysts work with data. What matters — and differentiates these two roles — is what the data is intended to do.

When comparing the roles of business analyst and data analyst, one must consider the audience. Who will be taking action based on the analyses?

Business analysts use data to improve business metrics.

Business analysts work directly with stakeholders to steer company objectives and keep the business on a successful path. They set and maintain key performance indicators for the organization. A business analyst may recommend strategies or business plans to executives, sometimes when a company is at a critical juncture, say quarterly or during a turnaround. Stakes can be high, but so can the rewards. (Think McKinsey analysts or other coveted consultancy jobs.) Business analysts are more likely to use presentation skills as they’ll need to present findings to executives and give recommendations in high-level meetings. 

Data analysts collect, extract, and analyze data.

Data analysts are more technically focused. They are responsible for getting the data and analyzing it, working with datasets and tables. For example, a data analyst at an eCommerce company may analyze customer information, aggregate email marketing lists, or use data to identify demographics for new customer acquisition plans. Data analysts are more likely to work in teams alongside marketing partners or with other technology roles such as programmers or product managers, depending on the size of the company. They also work with business partners across entire organizations, including business analysts, as needed for tasks and projects. 

Different roles mean different salaries.

Both business analysts and data analysts solve business problems. As such, they are in high demand. According to Glassdoor, the average salary for a data analyst in the U.S. is $72K. Compensation for business analysts is a bit more, averaging $79K. Of course, exact amounts depend on location and will vary from country to country. While a business analyst can command a higher salary, there is wider latitude for data analysts to carve out their niche in practically any industry. Since the function of data is increasingly integral to every enterprise, there is more flexibility for data analysts to dig into areas of the business where they can make the most difference, with more potential for creativity.  

In GA’s Intro to Data Analytics course, Iun Chen teaches SQL, Tableau, and Excel, business intelligence tools she uses in her professional role as a data analyst at LinkedIn.

“My formal job function is to build data tools for internal colleagues so they can successfully grow our business,” she says. “I create dashboards, reports, and anything else to ensure revenue keeps going up and anticipated risks go down for the company. In my experience, the skill set and mindset of the individual can define the role of a data analyst in any organization, large or small. Everyone uses data in their day to day so being able to clean, prep, analyze, and report data — regardless of what your actual job title is — is critical to not only the company’s success but your personal success as well.”

Both business analysts and data analysts are storytellers. 

Whether a business analyst’s more strategic and decision-making role is for you, or the technical, numbers-crunching, team-playing data analyst sounds more your speed, know that the two roles share one crucial skill: They use data to tell stories. Those stories lend insights that factor into decisions that affect the bottom line. Translating raw data into digestible and human narratives can be one of the most challenging skills for analysts to master, according to Vish Srivastava, who’s led multidisciplinary teams across tech sectors. So how does an analyst develop this multifaceted skill and set their career on the path for success?

“My recommendation is twofold,” he says. “One, always start your analysis with a hypothesis that you’re testing. You need to know right out of the gate why your analysis is going to matter. Two, after you’ve spent some time with your data, step away and write down your presentation storyline in three to five bullets. The final bullet should be your recommended next step. Of course, make sure you have the analysis and charts to back up your storyline and fill in the gaps as needed.”

When it comes to storytelling with data, the difference between a boring story and a compelling one can come down to data visualization. The tools at your disposal and your proficiency with them can make or break a presentation. Communicating the insights for business intelligence hinges on clear and impactful data viz, whether we’re talking business analytics or data analytics.

One classic example of data visualization’s power is the cholera map by John Snow, an early pioneer of disease mapping. “This is a beautiful example of how collecting data and visually presenting it can generate amazing insight,” says Srivastava. “In this case, the insight was that the sewer systems were spreading disease. This informed public policy and saved so many lives.”

The future of business intelligence will be determined by the democratization of data.

The prevalence of data and its part in tech careers is changing. To hear Srivastava tell it, future conversations on business intelligence will center less on the specificities of data analysis vs. business analysis and more on how data is creeping into even more roles.

“We’ve come a long way, but there is still far to go for data analysis skills to be deeply embedded in all functions across a company. In the future, I think we will see fewer dedicated teams for business analysis and data analysis; instead, all professionals will have these skills and utilize them daily. This democratization of data analysis will be incredibly powerful. It will create even more emphasis on making high-quality data available across every enterprise.”

Want to learn more about Iun?

https://www.linkedin.com/in/iunchen 
Want to learn more about Vish?
https://www.linkedin.com/in/vishrutps

Tableau vs. Power BI

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Featuring Insights From Matt Brems

Read: 2 Minutes

Tableau and Power BI are powerful tools for business intelligence, with capabilities to take loads of big data and create elegant visualizations that convey key insights to stakeholders in easily digestible presentations. Both help organizations leverage business intelligence to become more data-driven in their decision-making process. So which tool is better? We asked a few industry experts their thoughts on the data analysis tools Tableau and Power BI. Here’s what they had to say.

Candace Pereira-Roberts, Data Engineer & GA Data Analytics Instructor

“Anyone who works in data should learn tools that help tell data stories with quality visualizations. Tableau is a wonderful tool for the technical and nontechnical to build these visualizations. I love how we teach the Tableau unit in the Data Analytics bootcamp. I see students who are new to analytics learn Tableau desktop and be able to develop Tableau worksheets, dashboards, and story points in a couple of weeks to do a complete analysis project.”

Iun Chen, GA Instructor & Data Analyst at LinkedIn 

“In my professional capacity, I lead data visualization workshops to share best practices on charting and design theory, with a focus on Tableau. But with the growth of big data analytics, there are more players in the data viz space. Looker. Qlik, Domo, and Microstrategy are a few with out-of-the-box solutions. Check out other marketplace BI and analytics leaders and their reviews at Gartner.

Alternatively, if you are up for the challenge you can start from scratch and build out completely customized solutions through coding packages, such as with Python plotting libraries Matplotlib, Pandas, and Seaborn.”

Matt Brems, GA Instructor & Data Consultant at BetaVector 

“Most data analyst roles will expect some experience with data visualization. They may prefer your visualization experience be tied to a certain tool like Tableau or Power BI or simply want you to have experience designing graphics or dashboards. As with any platform, the human element is key. A good data analyst is curious and detail-oriented. Diving into the data and spotting anomalies or identifying patterns requires curiosity. Looking at large datasets for long periods of time can invite mistakes, so being detail-oriented ensures you’re interpreting the data correctly.” 

Vish Srivastava, GA Instructor & Product Leader at Evidation Health

 “Most teams I’ve seen are not comparing Tableau and Power BI. Instead, it’s more about whether to adopt a business intelligence tool at all, or whether to use Tableau or Power BI in place of Excel. Tableau is a great option when you need to quickly create data visualizations.Tableau is incredibly powerful because it’s designed for nontechnical users, meaning business users can set up and tweak dashboards and charts without the support of engineering or data science teams.”

When it comes to research, the most common data analytics tool is SQL — no surprise there. But once you get into more niche industries, that can vary, says Brems.

“In academia, R is probably the most prevalent data analysis tool, though Python is quickly gaining popularity. SAS and Stata are often used in specific industries, though their popularity is diminishing. (R and Python are open source tools, which means, among other things, that they are free.)”

Want to learn more about Candace?
https://www.coursereport.com/blog/how-to-become-a-business-intelligence-analyst
https://generalassemb.ly/instructors/candace-roberts/13840
www.linkedin.com/in/candaceproberts

Want to learn more about Iun?
https://www.linkedin.com/in/iunchen 

Want to learn more about Matt?
https://betavector.com/
https://www.linkedin.com/in/matthewbrems

Want to learn more about Vish?
 https://www.linkedin.com/in/vishrutps