7 Data Analysis Methods to Learn

Data analysis is a process for evaluating data using statistical or logical methods and techniques. It allows you to better understand past events, including why they happened and their relationship, and make predictions for the future. Many business processes benefit from data analysis methods, including sales, marketing, human resources, accounting, and information technology. 

Data supporting business processes and decisions helps you make informed, insight-driven decisions. Even industries outside the business world, such as medicine and sports, use data analysis to help make decisions. Depending on the type of data you have access to and the insights you’re looking to gain, you can choose from several different types of data analysis methods. 

Use this guide to learn more about data analysis, the different methods, and when to choose each. 

What is data, and why analyze it?

While data may seem complex, it’s simply a collection of factual information presented in a format such as words, numbers, measurements, or observations. Data alone, however, lacks meaning. To interpret data and give it meaning, you first have to analyze it, which gives you statistics. By analyzing data, you give the factual information context so that you can use it to help in decision-making processes. 

Data comes in two different forms: qualitative and quantitative. Qualitative data is generally nonnumerical and describes attributes and characteristics. Examples of qualitative data include the color of an object or someone’s opinion. You collect qualitative data using various methods, including focus groups and interviews. 

Quantitative data is expressed numerically and includes information that can be counted or measured. You can collect data for quantitative analysis through surveys, experiments, and database reports. If you collect your data through surveys and want to analyze your variables quantitatively, you will need to make sure your variables are designed to be counted or represented numerically.

How does data analysis work?

You can break down the data analysis process into a few steps. First, you need to identify your objective. Next, you need to collect, clean, analyze, and interpret your information. 

• Identify your goal: During this stage, you will use data to explore the questions you want to answer. 

• Collect your data: To have data to analyze, you need to collect it. Your chosen collection method will depend on the type of data you’re gathering, the timeline, and your resources. 

• Clean your data: Cleaning data is necessary to prepare it for analysis. When collecting large amounts of data, you'll likely come across either incorrectly formatted or duplicated information. You don’t want to include this data in your analysis since it will negatively impact the quality of your results.

• Analyze your data: Using techniques like those we will discuss in the next section, you can develop insights from data, including patterns, trends, and correlations. 

• Interpret and present your findings: Interpreting the findings from your analysis allows you to draw conclusions and establish how to use the results. Once you have these insights, you’ll need to present them to the relevant stakeholders in your organization. 

7 data analysis methods

Implementing data analysis methods is important for you to help your organization get the most out of its data. Depending on your industry and goals, you might use certain types of analysis more than others. As you start learning different types of methods, seven different analysis types you can start with include the following.

1. Regression analysis

Regression analysis is great for establishing trends and making predictions for the future. Using regression analysis, you can measure the relationship between variables by testing how different factors, known as independent variables, impact the dependent variable. Accountants can use regression analysis to help organizations make informed business decisions, while marketers and business owners can use this method to determine the factors influencing customer buying decisions.

2. Discourse analysis

Discourse analysis is a qualitative method used to explore how language is used in real-world social contexts. You can better understand how cultural values, beliefs, and conventions influence communication by performing discourse analysis. This helps clarify misunderstandings and establish the meaning behind verbal and nonverbal communication.

3. Hypothesis analysis

During a hypothesis analysis, you will develop two different hypotheses: Null and alternative. The null hypothesis states that no difference exists between the two groups, while the alternative hypothesis usually states the opposite. The goal of a hypothesis analysis, also called hypothesis testing, is to disprove the null hypothesis by demonstrating the difference between the two groups, thus validating the alternative hypothesis. 

4. Content analysis

Content analysis can be used when working with qualitative data, such as different forms of communication. This type of data analysis allows you to quantify relationships and meanings found within qualitative data, such as using certain words or concepts. 

5. Data mining

Data mining is the process of using computers to sort through large amounts of data to establish patterns or trends. With this method, you can automate the process of analyzing information and make predictions based on future probabilities and other useful insights. 

6. Cluster analysis

The cluster analysis method sorts data into clusters based on their similarity. Cluster analysis is an unsupervised learning method, which means the model does the sorting instead of you having to sort data into clusters yourself. Because of this, you don’t know what the clusters are or how many exist before the cluster analysis. Cluster analysis can be particularly helpful in market segmentation, machine learning, pattern recognition, bioinformatics, and image analysis.

7. Factor analysis

Using factor analysis, you can take many variables and reduce them to a smaller number of factors to determine the amount of variance between the different variables and assign them a score. This method is especially helpful when working with complex data that has a high number of interconnected variables. 

Advantages and challenges of using data analysis methods

Using data analysis can be advantageous. It can help you make informed decisions, improve efficiency in your organization, and learn how to improve products and services for your customers.

Data analysis does have its challenges, too. Not only do you have to spend time collecting and organizing data, but you must also ensure you’re implementing the right data analysis strategies. Once you’ve performed the analysis, you might find it difficult to express your results to audiences less familiar with data analysis techniques. Taking time to break down the findings into key results and simple visualizations can help you overcome this obstacle.

Why learn data analysis methods?

Data analysis is an increasingly valuable skill. Organizations use data analysis to effectively market their products, build a personalized customer experience, and optimize their business operations. By learning data analysis, you can expand your professional capabilities and create more informed business strategies to meet your organizational objectives. A strong data analysis foundation can help you make data-driven decisions in any industry, making it a versatile skill that can benefit you throughout your professional development.

Job titles that use data analysis

Data analysis is relevant across industries, which makes it applicable to a diverse range of careers. You can find several different roles where data analysis is a valuable skill, including:

• Marketing analyst

• Data scientist

• Systems analyst

• Data analyst

• Data architect

• Business intelligence analyst

• Financial analyst

• SQL developer

• Database administrator

• Software engineer

Learning more with Coursera

By learning foundational data analysis methods, you can develop the ability to assess and analyze your data accurately, leading to informed insights within your industry. As you begin, start by learning key techniques such as regression, hypothesis, and cluster analysis.  On Coursera, you can find highly rated courses to help you learn more and develop your data analysis skills. Data Analysis with Python from IBM is a great course if you want to learn how to write code to clean and prepare data for analysis and build regression models. 

If you’re interested in a career where you can perform data analysis, you can earn a Google Data Analytics Professional Certificate to learn how to organize data, complete analyses, and other in-demand job skills. You can also take Duke University’s Data Analysis with R Specialization to learn how to perform hypothesis tests, utilize regression models, and visualize data using the R programming language.