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