Data Analytics for Beginners: Tips for Getting Started

Data scientists and data analysts are in high demand, and the field is poised to grow even more. Between 2021 and 2031, the U.S. Bureau of Labor Statistics (BLS) projects:

• 36% job growth for data scientists

• 23% job growth for operations research analysts

• 19% job growth for market research analysts

These massive job growth projections far exceed the projected overall job growth of 5% due to an increasing number of organizations recognizing the power of data. Gathering, analyzing, and interpreting information can produce detailed insights on a range of crucial areas, from consumer behavior to operational efficiency.

Because it is so highly prized, people skilled in data analysis and application are increasingly valuable in the workforce. Below is a quick summary of data analytics for beginners who aren’t familiar with data analytics but are interested in the field.

What Is Data Analytics?

Data analytics is the practice of analyzing data to draw actionable conclusions, encompassing data gathering, analysis, interpretation, and presentation. The source data can be anything from raw numerical data from online transactions to details mined from text and web searches. In the hands of skilled professionals, this data can provide insights businesses can use to make informed strategic decisions that help them reach their goals.

Analysts use sophisticated tools such as software and programming languages like Python to gather, manipulate, and examine huge quantities of data—then interpret the results and draw conclusions based on what the data reveals. Then, analysts present the insights gleaned from the data to key decision-makers and others using techniques such as data visualization.

Data analytics can offer a range of monetary and resource benefits to organizations of every size and type, including more effective business decisions regarding growth and stability, marketing strategies that target new customers and keep existing customers happy, and operations improvements that mitigate risk while maximizing efficiency.

Different Types of Data Analytics

A key part of any summary of data analytics for beginners is a description of the types of analytics. These can be applied to the same data sets, but each offers different insights.

Descriptive Analytics

Descriptive analytics details the changes that occurred between one set point and another. For example, it could be used to define exactly how much a product’s sales increased or decreased during a set period of time, or how many phone calls a customer service representative took. This type of analytics involves historical data and provides a company with information regarding a specific metric.

Diagnostic Analytics

Diagnostic analytics uses data to explore why something happened rather than simply reporting what happened. This usually involves more complex data sets that can provide greater context for the results, such as why the data deviated from a trend or how a unique event disrupted an otherwise steady pattern. For instance, diagnostic analytics could be used to determine whether a new advertising campaign led to more sales.

Predictive Analytics

Predictive analytics uses past information to determine what is most likely to happen in the future. This can help companies anticipate market shifts before they occur. For instance, a brewery could analyze the previous year’s sales of a seasonal pumpkin spice beer to anticipate market demand for the same product in the current year.

Prescriptive Analytics

Prescriptive analytics helps organizations develop strategies to improve and correct negative trends in the future. In the pumpkin spice example, prescriptive analytics could be used to help the brewery adjust its production schedule or move up its release date if the data suggests that increased demand for the product is likely to occur.

Tips for Data Analytics Beginners

The data analytics field offers a wide range of careers to choose from. As a beginner, you need to develop and refine the right knowledge and skills to be qualified to pursue any of these positions. Fortunately, there are a number of ways you can do this.

One of the best ways to develop data analytics skills is through a dedicated tech bootcamp. These programs, such as Fullstack Academy’s online Data Analytics Bootcamp, provide a space to learn the basics of data analysis. They offer a controlled environment to help you develop skills in Excel and SQL, data visualization, and the basics of Python.

You can also learn the basics of data analytics independently. A number of online tutorials and tests are available on the Internet, and after developing some confidence in your basic skills, you can try your hand at competitions. These tools can be ideal for data analytics beginners, but they can also be useful once you become a veteran data analyst and need to learn a new programming language.

Build a Bright Future

Data analytics is a field full of opportunity, even if you’re a beginner. With the right training and tools, today’s data analytics beginners can become tomorrow’s leaders.

Enrolling in Fullstack Academy’s online Data Analytics Bootcamp allows you to take the first step toward a rewarding career. The program’s signature live online classes offer a foundation in the basics of data analytics, and can prepare you for certification exams in Tableau and Amazon Web Services. Take charge of your future with Fullstack Academy.

Recommended Readings

Business Analyst vs. Data Analyst: How Are They Different?

Data Scientist vs. Data Analyst: What’s the Difference?

What Is Database Management? Career and Salary Outcomes

Sources:

Alooba, Free Data Analytics Test

Forbes, “Four Types of Business Analytics”

Indeed, “FAQ: Why Are Data Science Jobs in High Demand?”

Investopedia, “Data Analytics: What It Is, How It’s Used, and 4 Basic Techniques”

TechTarget, “Data Analytics (DA)”

U.S. Bureau of Labor Statistics, Data Scientists

U.S. Bureau of Labor Statistics, Operations Research Analysts

U.S. Bureau of Labor Statistics, Market Research Analysts