First and foremost, it’s critical to understand the difference between data analytics and data science. To do so, let’s take a look at a definition of both. According to Northeastern University, data analysis involves answering questions generated for better business decision-making. It uses existing information to uncover actionable data and focuses on specific areas with specific goals.
On the other hand, data science focuses on discovering new questions that you might not have realized needed answering to drive business innovation. Keep reading for an in-depth overview of both disciplines to decide which career path would better suit your career aspirations.
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.
Data analysis is a driving force for businesses taking the challenge of digital transformation head-on. Data analytics skills are some of the most sought after in candidates today. There’s a wealth of data available for companies to access, but without trained talent, they can’t leverage this data to make smarter, faster decisions.
Since it was created in 1985, Excel has practically become synonymous with data itself, and still is many years later. Spend a few minutes with our expert instructor in the videos below to learn the kinds of Excel tools that can help you be your own analyst—and make smarter decisions with data.
How to Create an Excel Bar Chart
Bar charts are an important visual tool that can help express your data over time and tell a story in a visually appealing and digestible way. Learn more in our 2-minute lesson below:
How To Create an Excel Pivot Table
Pivot tables allow you to effectively summarize and highlight the importance of your data sets. They are an important presentation tool and can help you simplify your data. Learn more in our 3-minute lesson below:
How To Create a Histogram in Excel
Histograms provide a visual representation of variations within your data and can help display degrees of difference in an impactful way. Learn more in our 2.5-minute lesson below:
How To Create a Pie Chart in Excel
Pie charts can express percentages of a whole and represents a set period of time and can be helpful to show differences among a handful of categories. Unlike bar charts, it does not express changes over time. Learn more in our 2.5-minute lesson below:
How To Create a VLookup in Excel
A VLookup (vertical lookup) can help you lookup data that is organized vertically. It is useful in helping you spot trends and find important pieces of data that can be difficult to locate in large data sets. Learn more in our 2.5-minute lesson below:
Featuring Insights From Iun Chen & Vish Srivastava
Read: 2 Minutes
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.”
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.”
“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.”
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?”
“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.
Can they quickly prep and analyze large volumes of data?
Identify key information and determine the best visual method to present them?
Take business questions and determine which visualizations to use?
Translate raw datasets to storylines with a beginning, middle, and end?
Format charts, graphs, titles, text, and images for a polished deliverable?
Articulate best practices on design and visualization techniques?
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.”
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.
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.
Beginning: Sales are down year-over-year; identify the symptoms.
Middle: Furniture sales — our largest segment — are doing poorly in the last six months; conduct the analysis to investigate reasons and uncover root causes.
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.
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.
“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.”