Trying to wrap your head around the difference between data science vs. computer science?
Many of the luxuries that we have today — a favorite streaming service that recommends new movies, the ability to unlock our phones with facial recognition technology, or virtual home assistants that let us play our favorite music just by speaking — are made possible by computer science and made better by data science.
Today, these two fields complement each other to further applications of artificial intelligence, machine learning, and business forecasting. Read on to learn a definition of each, their histories and applications, and career paths in both computer science and data science.
Whether you’re making the leap into a career change, or leveling up your current skill set to land that next promotion, you might be wondering if an online coding bootcamp is right for you. The good news is that General Assembly offers a range of beginner-friendly class formats (from full-time immersives to flexible, part-time schedules), and we have a course that will fit your specific career path and interests.
Without data, humans make decisions based on intuition. However, we don’t make very good decisions with our gut. That’s because as humans we have our emotions, unconscious biases, and gaps in information to contend with.
Everyone makes better decisions with data. For a business, poor leadership decisions can be incredibly costly. But companies don’t just need to collect data—they need professionals who can analyze and interpret it. That’s where data analysts and data scientists come in.
Glassdoor and U.S. News & World Report have both named data scientist among their best jobs based on salary, job satisfaction, and career opportunities. Data analysts earn a median base salary of $66,370 in the U.S., while data scientists earn $103,525 on average. If you’re ready to jump into your first data analyst entry-level job, read on for how to break in and the top industries hiring data analysts and data scientists.
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.
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.
The world’s data reached an all-time high in 2021. 79 zettabytes of data – which is enough storage for 30 billion 4K movies – was generated last year alone.
This is a good thing – right? More data means more innovation, which means more advancements for society.
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.”
