What Is Data Analysis? (With Examples) (2024)

Written by Coursera Staff • Updated on

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

What Is Data Analysis? (With Examples) (1)

"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.

This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.

Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.

The World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists [1].In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.

Read more: How to Become a Data Analyst (with or Without a Degree)

Beginner-friendly data analysis courses

Interested in building your knowledge of data analysis today? Consider enrolling in one of these popular courses on Coursera:

In Google's Foundations: Data, Data, Everywhere course, you'll explore key data analysis concepts, tools, and jobs.

In Duke University's Data Analysis and Visualization course, you'll learn how to identify key components for data analytics projects, explore data visualization, and find out how to create a compelling data story.

What Is Data Analysis? (With Examples) (2)

Data analysis process

As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.

  • Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it?

  • Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).

  • Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.

  • Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.

  • Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions?

You can complete hands-on projects for your portfolio while practicing statistical analysis, data management, and programming with Meta's beginner-friendly Data Analyst Professional Certificate. Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.

Or, Learn more about data analysis in this lecture by Kevin, Director of Data Analytics at Google, from Google's Data Analytics Professional Certificate:

Read more: What Does a Data Analyst Do? A Career Guide

Types of data analysis (with examples)

Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.

In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.

Descriptive analysis

Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.

Descriptive analysis answers the question, “what happened?”

Diagnostic analysis

If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.

Diagnostic analysis answers the question, “why did it happen?”

Predictive analysis

So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.

Predictive analysis answers the question, “what might happen in the future?”

Prescriptive analysis

Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.

Prescriptive analysis answers the question, “what should we do about it?”

This last type is where the concept of data-driven decision-making comes into play.

Read more: Advanced Analytics: Definition, Benefits, and Use Cases

What is data-driven decision-making (DDDM)?

Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.

This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [2].

Get started with Coursera

If you’re interested in a career in the high-growth field of data analytics, consider these top-rated courses on Coursera:

Begin building job-ready skills with the Google Data Analytics Professional Certificate. Prepare for an entry-level job as you learn from Google employees—no experience or degree required.

Practice working with data with Macquarie University's Excel Skills for Business Specialization. Learn how to use Microsoft Excel to analyze data and make data-informed business decisions.

Deepen your skill set with Google's Advanced Data Analytics Professional Certificate. In this advanced program, you'll continue exploring the concepts introduced in the beginner-level courses, plus learn Python, statistics, and Machine Learning concepts.

Frequently asked questions (FAQ)

Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance.‎

Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python), machine learning, and spreadsheets.

Read: 7 In-Demand Data Analyst Skills to Get Hired in 2022

Data from Glassdoor indicates that the average base salary for a data analyst in the United States is $75,349 as of March 2024 [3]. How much you make will depend on factors like your qualifications, experience, and location.‎

Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.

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

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

What Is Data Analysis? (With Examples) (2024)

FAQs

What Is Data Analysis? (With Examples)? ›

Data analysis is used to make purposeful discoveries, suggest conclusions, support decision-making, and support or debunk previous studies. For example, a researcher wants to understand the relationship between classrooms that use hands-on activities in mathematics.

What is data analysis in simple words? ›

Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.

What are the 3 most common data analysis? ›

The four types of data analysis are:
  • Descriptive Analysis.
  • Diagnostic Analysis.
  • Predictive Analysis.
  • Prescriptive Analysis.

What are the three 3 kinds of data analysis? ›

There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.

What are the basic skills required for a data analyst? ›

  • Programming languages (Python, R, SQL)
  • Data Visualization Tools (Tableau, Power BI)
  • Statistical Analysis.
  • Data Wrangling and Cleaning. Essential Data Analyst Skills: Soft Skills.
  • Communication skills.
  • Problem-solving abilities.
  • Attention to detail. ...
  • Machine learning.

What is the main purpose of data analysis? ›

The main purpose of data analysis is to draw conclusions on specific data. Researchers use these results to draw conclusions on their study.

What does a data analyst do? ›

Data analysts gather, cleanse, analyze historical data, and uncover business insights. They tell us what happened and why it happened. Whereas data scientists utilize advanced machine learning algorithms like neural networks to identify future trends, predict outcomes, and prescribe solutions.

What are the three C's of data analysis? ›

Three C's of Data Analysis: Codes, Categories, Concepts.

What is an example of data analysis? ›

This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee. Descriptive analysis answers the question, “what happened?”

What is the key objective of data analysis? ›

It involves a variety of techniques and methods, ranging from basic statistical measures to sophisticated machine learning algorithms. The primary objective of data analysis is to extract actionable insights from raw data, enabling organizations to make informed choices and predictions.

What are the 3 steps you follow when you Analyse data? ›

Three Steps to Data Analysis
  • Data Acquisition.
  • Data Sorting and Calculation.
  • Data Visualisation and Information Output.
Aug 26, 2021

Can I teach myself data analysis? ›

Yes, it is possible to learn data analytics on your own. Many online resources are available for learning data analytics, including tutorials, courses, and online communities.

What is the easiest way to analyze data? ›

Best Ways to Analyze Data Effectively
  1. Look for Patterns and Trends.
  2. Compare Current Data against Historical Trends.
  3. Look For Any Data That Goes Against Your Expectations.
  4. Pull Data from Various Sources.
  5. Determine the Next Steps.
Nov 28, 2023

How to use Excel to analyze data? ›

Simply select a cell in a data range > select the Analyze Data button on the Home tab. Analyze Data in Excel will analyze your data, and return interesting visuals about it in a task pane.

What is a simple example of data analysis? ›

For example, a dating app company might use measures of central tendency to determine the average age of its users. Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field.

What is the main idea of data analysis? ›

Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses, or disprove theories.

How do you explain data analysis method? ›

Data analysis is the systematic process of investigating, through varied techniques, facts and figures to make conclusions about a specific question or topic. Examples include analyzing data gathered from customer surveys, conducting interviews, or reviewing case files.

What is another word for data analysis? ›

What is another word for data analysis?
analyticsdata analytics
anatomybreakdown
analyzationresearch
deconstructiondissection
evaluationinterpretation
6 more rows

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