What is data analytics?

In today’s world, data is everywhere. However, most data starts out as “raw,” meaning it may be disorganized, incomplete, or otherwise unable to provide meaningful insights for a business. Raw data only becomes useful when someone with data analytics skills gathers, processes, and manipulates the data to get it ready for analysis. Then, the analyst can use the refined data to draw conclusions and make predictions, which help drive more informed decision-making and better outcomes for the business.

For example, an e-commerce company might analyze customer purchasing behavior to personalize recommendations and boost sales by improving the user experience. Businesses that effectively leverage data analytics can turn data into a competitive advantage, making data analysis a valuable function for any business and a crucial skill set for many professionals.

A data analyst is a professional who collects, manipulates, and analyzes data to help their business or organization make data-supported decisions. An analyst’s work involves breaking down complex data sets, identifying key trends, and communicating findings. Essential traits of a data analyst include:

• Analytical thinking, such as breaking down complex problems into actionable solutions.
  
• Technical expertise, including proficiency in data analysis tools.
  
• Statistical knowledge, from basic concepts like averages and medians, through more advanced concepts like correlation, regression, and variance.
  
• Communication skills, such as presenting findings through visualizations and reports.
  

Many data analysts specialize in a field. Data analysts in marketing, for example, might focus on campaign optimization, identifying key drivers of success and failure. Data analysts in finance, on the other hand, might use their skills to evaluate investment opportunities and identify emerging trends.

If you’re looking around at data-related job listings, you may see titles like “data analyst” and “data scientist.” While these titles are similar in that they both work with data, their roles and focus areas differ significantly.

Data analysts mostly interpret existing data to address current business challenges. They use data analysis tools to create reports, generate insights, and provide recommendations. Their work is often rooted in descriptive and diagnostic analysis, explaining what happened and why.

Data scientists, on the other hand, tend to work on more advanced data analytics practices. They may build complex algorithms and use advanced methods like machine learning to predict future trends or model potential outcomes. Their work often involves more programming languages and requires a strong foundation in mathematics and computer science.

For example, a data analyst might evaluate sales data to identify top-performing products or friction points within the sales funnel, while a data scientist might develop a predictive model to help forecast future demand.

Breaking into the field of data analytics can seem daunting, especially if you’ve already worked or studied in seemingly unrelated fields. You might be worried that you don’t have the right experience, but the reality is that successful data analysts come from diverse fields and may often have non-traditional backgrounds.

To become a data analyst, you’ll need a combination of training, familiarity with analytical tools, and, ideally, some hands-on experience with real or fake data. While there are many paths into the field, most involve these steps:

1. Structured education: Training and certificate courses in data analytics can teach you job-ready skills like understanding data structures, manipulating data, and interpreting results. In many cases, these training programs may be all you need to land your first data role, but they can also be a supplement to any existing related or unrelated education you may have received.
   
2. Learning data analysis tools: You’ll also need proficiency in the current software programs used for data organization and visualization, such as standard spreadsheet software like Google Sheets or Excel, and programming languages like SQL or Python.
   
3. Building a portfolio: You can showcase your abilities by working on personal projects, analyzing public datasets or contributing to existing businesses and non-profits. This practical work helps you establish a tangible record of your skills.
   
4. Networking and gaining experience: Networking helps you build connections in the field and may open doors to internships or entry-level roles. Attend industry events, join online communities, and seek opportunities to apply your skills in real-world settings.

If you’ve ever wondered how to become a data analyst, the Google Data Analytics Certificate can be your springboard to a new career in data. Our certificate programs are created by subject matter experts at Google and designed to teach you the skills you’ll need to stand out among employers. Through readings, quizzes, and hands-on projects, you'll learn to organize and analyze large datasets, create compelling data visualizations, and solve real-world business challenges.

Alternatively, if you’ve already mastered the basics of data analytics, you might want to consider some of our advanced content, such as Google Advanced Data Analytics Certificate or Google Business Intelligence Certificate.