The field of data analytics has become crucial for any company that’s looking to scale up - from LLCs to global corporations. Businesses are collecting more information about their customers every year, and it takes careful analysis to turn all that information into actionable insights.
To make this career path even more enticing, those who learn to turn raw data into strategy stand to make significant salaries. Analysts have one of the most desirable tech jobs out there, since they enjoy a stable job market full of remote work opportunities. If you enjoyed your Stats class or find yourself planning out your grocery budget in Excel, you could have a long and rewarding career as an analyst.
But if you’re exploring your career options and you’re brand new to the tech scene, that doesn’t really answer your biggest questions. What is data analytics, and why has it become so important for business growth? Here are the terms you need to know, and a big picture overview of how this field is changing our world.
What Is Data Analytics and Why Does It Matter
Data analytics is the practice of collecting and organizing information, looking for patterns, and visualizing those findings in a clear, digestible way. Doesn’t exactly sound glamorous.
But this set of skills has practical applications in every industry. Most organizations large and small are now prioritizing “data-driven decisions:” they strive to make changes based on logical predictions and historical evidence rather than impulse. They need experienced analysts to do that.
When a business is looking for ways to improve their product, introduce a new service or lure in a different customer demographic, they start by collecting as much information as possible about their clients, product usage and competition. To a trained eye, the patterns and gaps that appear in those datasets tell a story about how the company should proceed.
And that story could lead to massive profits.
Skilled data analysts are essential to business growth, and they’re behind many of the products, apps and services we use every day.
Data Analytics Tools
Data analysis has become an incredibly robust field in the last decade. Professional analysts now have specialized software and coding languages at their fingertips to help them do their jobs quickly and accurately. These are the most useful programs that analysts swear by.
Excel: The old standby, Microsoft Excel isn’t going anywhere. It’s still the most frequently used software for databases.
Tableau: A tool used for business intelligence that displays information in easy-to-read visual dashboards.
Structured Query Language (SQL): A programming language that allows analysts to quickly extract specific data from tables.
Python: A coding language that's relatively easy to use, and makes large amounts of information easier to manipulate.
R Programming: A language created by analysts, for analysts: it’s specifically designed for data mining and completing statistical functions.
It takes time and patience to master any one of these tools - much less all of them. That’s one of the reasons why data analysts typically earn high paychecks. (It’s also why they rely on cheat sheets to find the right formulas, commands and lines of code in a pinch).
Data Analytics Terms
As you dig deeper into the world of analytics, you’ll discover a whole new language. Now’s the time to learn the most common terms so you don’t have to reach for Google when you hear them in conversation.
Predictive analytics: A subspecialty of analytics; this is the art of using data mining and statistics to predict future events.
Data visualization: Presenting information graphically, in a way that clearly illustrates trends and patterns. This skill shines when analysts have to present their big takeaways to higher-ups.
Exploratory data analysis: An approach to data interpretation that relies heavily on statistical graphics. This is a popular philosophy that encourages testing and experimenting before making assumptions.
Data model: A way of organizing the elements of a dataset, establishing the relationships between different types of information.
Analytical database: Software that makes it easier to maintain and manipulate data.
Data cleaning/data cleansing: Finding and removing data points that are inaccurate or irrelevant. Data cleaning is usually the first step of an analysis.
What Is Big Data?
Many brands now collect a vast amount of customer information. With that comes more strategic business decisions, but also (sometimes) privacy and ethics concerns. These are a few reasons why the phrase “big data” has become prevalent in mainstream news and media. Even so, the term itself is rarely explained in plain language.
So what's the definition of big data? The popular phrase refers to massive datasets, as well as the practice of collecting and interpreting it all. The type of information included in that dataset is classified as either structured or unstructured. Structured data includes numbers and words that are easily categorized. Unstructured data refers to everything else: pictures, videos, and long-form content.
When a dataset is so large that it exceeds the limits of Excel, specialized tools are brought in to manage it. Programming languages, for example, are used to complete calculations that could take days for someone to achieve with a spreadsheet function. This is why an ability to manipulate big data has become a specialty of its own.
What Is the Difference between Data Analytics and Data Science?
Data analysis and data science are not one and the same, and it’s important to understand the differences as you solidify your career plans.
What Is A Data Analyst?
Data analysis refers to the practice of organizing, interpreting, and visualizing information. A data analyst is someone who manages datasets and draws useful insights in a business context.
What Is A Data Scientist?
Data science encompasses those functions, but takes them to the next level. Data scientists are primarily concerned with creating models and algorithms that conduct complex analyses. Machine learning and deep learning are the most recognizable areas of this advanced discipline. As a data scientist, you'll learn what these terms mean, how they apply to everyday life, and how to actually create the algorithms that lead to artificial intelligence.
Data analytics could be treated as the precursor for learning data science, since it lays the foundation for more advanced studies. But many choose to pursue data analysis as a career in its own right - especially if they’re interested in working closely with the Marketing, Finance, IT or Sales departments.
Types of Data Analysts
If you choose to pursue data analysis as a profession, you may find yourself specializing in a specific area. While some of the basic functions are the same, you’re going to apply your analytics skills in different ways depending on the title you earn, and which department you call home.
This is not an exhaustive list of analyst specialties, but it does give you an idea of the different paths you could pursue with some analytics expertise. This is what it means to be an analyst in a variety of different capacities.
Marketing Analysts: Research which products stand to make the most profit for a business, and advise on pricing based on competition and user demand.
Financial Analysts: Make business recommendations based on market trends; predict outcomes for planned financial deals and contracts.
Business Analysts: Look for efficiencies within internal business operations.
Research Analysts: Conduct market research and determine the best way for a company to stay competitive.
Risk Analysts: Seek to minimize the financial risk associated with big business decisions and investments.
The definition of an "analyst" is someone who gathers quantitative data and uses it to improve the functions, products or services of an organization. And clearly, that skill can be put to use in a variety of different departments.
Data Analysis Skills
There are three important data analysis skills that are essential for anyone serious about a long-term career. If you take pride in your analytical thinking, problem-solving prowess and communication abilities, then you’ll excel in any analytics role. A natural aptitude in these areas makes it much easier to pick up the technical skills, like coding and statistics.
Analytical Thinking: An analytical approach is essential, and that encompasses much more than knowing which Excel function to run. It’s a way of thinking. Great data analysts don’t take the numbers at face value: they continue to ask “why,” and delve deeper into the research to uncover the driving reason behind the patterns they see.
Problem-Solving: Analysts use a variety of business intelligence tools like SAS and Google Data Studio to pull together the fine details. But those tools won’t deliver answers to business challenges. An analyst is responsible for finding the most relevant information, and using it to guide high-level business strategy.
Communication Skills: As a data analyst, you’ll have to deliver your findings to executives or clients who probably are not well-versed in probabilities and statistical analyses. You’ll have to display your key takeaways in user-friendly dashboards and clearly written reports in order to make sure your hard work has the impact it deserves.
Tips for Becoming A Data Analyst
If a career in data sounds appealing to you, there are ways you can get started and build up experience - even if you don’t have an Analyst title on your resume just yet. Here are some tips for flexing your Excel and Tableau muscles on your own to build a portfolio, and ultimately get hired as an entry-level analyst.
Offer up your analysis skills. Seek out a few unpaid projects to work on while you make your transition into data. You could do this by reaching out to others in your company and finding out what their biggest dilemmas are. Figure out how you might be able to solve their problems with some clever Excel or Python analyses. You could even reach out to your personal connections outside the workplace and generate your own volunteer or internship position.
Find your own challenges. If you’re still researching your career options, you can start off with some free self-learning resources. YouTube is full of free webinars and tutorials that can give you a feel for SQL or help you brush up on your statistics knowledge.
You can also roll up your sleeves and create your own project in whatever topic you’re interested in. Use tools like data.gov and dataportals.org to access free datasets; come up with a problem that could be solved with the available information, and get to work on a data experiment. Be sure to preserve your end results in an online portfolio so you can demonstrate your initiative to potential employers.
Take an online course. If you decide this is the right path for you, you’ll need to commit to a structured curriculum. Even the most talented analyst would struggle to learn statistical analysis, data visualization techniques and computer programming solo. Apply to a full-time, online data analytics course for the fastest entry into a new career path, regardless of your previous experience. If you need to keep working while you learn, try a part-time data analytics bootcamp.
If after all your research about data analytics you decide that you’re in it for the long haul - we are too. Our course is structured to give you the skills for ongoing professional growth, and we’ll pair you up with a mentor and career coaches to set you up for success. Learn more about our Data Analytics curriculum, payment options and our job guarantee.
You deserve a stable career path that promises high pay and, more importantly, job satisfaction. Data analytics might be your ideal pathway into tech.