What is Data Analysis? (With Examples)

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Data analysis involves examining, cleaning up, transforming and forming data with the aim of discovering helpful insights, drawing rational conclusions and helping in decision-making.

With a great range of techniques referred to by various names, it is used for multiple purposes in different sectors such as business, science and social sciences.

In today’s corporate world, it plays a major role in making decisions more rational and helping businesses run efficiently. Thus, data analysis is essential for the effective functioning of any organization.

Data analysis is quickly becoming one of the most sought-after skills in the job market. The World Economic Forum’s Future of Jobs Report 2020 cited data analysts and scientists as top emerging professions, with AI and machine learning specialists and big data specialists coming in close second[1].

As a result, anyone interested in honing their knowledge in this field should consider taking courses to learn more.

This article will explore the data analysis process, different types of analysis, and suggested courses to help candidates get started in this fast-growing role.

From techniques such as descriptive analysis for summarizing data neatly, to predictive models for forecasting patterns and trends, data analysis is ripe with possibilities. With the right set of skills, anyone can become a data analysis expert.

The process of data analysis

A data analysis process includes steps such as Data requirements, collection, processing, Data cleaning, Exploratory the data, Modeling and algorithms, Data product, & Communication.

Data requirements

Data requirement gathering is an important step in data analysis. It involves asking questions such as why the analysis needs to be done, what type of data is needed, and what data will be analyzed.

This helps collect all necessary information before beginning the analysis process. Gathering requirements also allows analysts to assess if they have enough data to draw meaningful conclusions from their analysis.

Knowing the answers to these questions will provide a better understanding of the data, which can lead to more accurate and reliable results. Gathering data requirements is essential to any data analysis project and should not be overlooked or taken lightly.

Data collection

Data can be collected from many different sources. Analysts may provide specifications to the people responsible for the data, typically IT staff. Additionally, data can be collected through environmental sensors including traffic cameras, satellites and recording devices. Furthermore, information can be obtained through interviews, downloads of online files or reading documentation.

Data processing

Data must be collected and organized in order to be analyzed. This typically includes placing the data into rows and columns within a table format (known as structured data). Once arranged, this data can then be further examined through the use of spreadsheet or statistical applications.

Data cleaning

Data collection is important, but it’s not enough. In order to make the most use of your data, you need to clean it up. Data cleaning involves getting rid of white spaces, duplicate records, and other errors. This process is necessary before any analysis can take place. Without data cleaning, your results will be unreliable and inaccurate. So do yourself a favor – clean up that data!

Data analysis

Once the data has been cleaned, analysts can begin to analyze it. This process is known as exploratory data analysis and involves using different techniques to uncover any meaningful messages within the data.

Depending on what they find, further cleaning or requests for more data may be necessary. Descriptive statistics such as averages and medians can also be used to aid understanding of the data.

Finally, visualization techniques like graphs are used to get a better look at what the data shows. All these methods allow analysts to gain insights into the information contained in their datasets.

Why is Data Analysis Important?

Here is a why data analysis is important for businesses today.

  • Data analysis can help businesses improve their customer targeting by showing them where to focus their advertising and marketing efforts.
  • It also gives businesses a better idea of their target audience’s spending habits, disposable income, and interests.
  • Moreover, data analysis shows which areas need more resources and money while providing accurate information that can be used to develop future strategies or realign the company’s mission.
  • Ultimately, it enables businesses to make informed decisions that reduce operational costs and lead to successful outcomes.

Types of data analysis

There are majorly 6 types of data analysis. Let’s read them about here.

Descriptive analysis:

Descriptive analysis presents statistics that summarize quantitative data and helps describe what happened. For instance, it can show the sales distribution across employees and the average sales figure per employee.

Diagnostic analysis

Diagnostic analysis helps answer the question of why something happened by identifying patterns in data through statistical analysis. Analysts look for similar past patterns to apply solutions to current problems. More on statistical analysis will be discussed later.

Prescriptive Analysis:

Combining insights from other forms of data analysis results in prescriptive analysis. In some situations, a problem cannot be resolved by relying on a single analysis type and instead necessitates using multiple insights.

Predictive analysis

Predictive analytics involves using data to make projections about future outcomes. For example, if historical sales data reveals that a particular product consistently sells best in September and October each year, you can use predictive analysis to anticipate a similar peak during the coming year.

Statistical Analysis:

Statistical analysis helps to determine what has happened by collecting data, analyzing it, creating models, interpreting the results and presenting them in dashboards.


Where is data analytics used?‎

Data analytics can benefit almost any business or organization by providing valuable insights to guide decision-making and improve performance. Many successful companies, such as Amazon, Netflix, Starbucks, and General Electric, have incorporated data analysis into their business strategies.

What are the top skills for a data analyst?‎

Data analysis requires various tools and technologies. Skills like SQL, data visualization, programming languages such as R and Python, machine learning, and spreadsheets are essential for data analysts.

What is a data analyst job salary?‎

The average salary for a data analyst in the United States is around $95,867 according to Glassdoor. Depending on qualifications, experience, and location, earnings may vary.‎ ‎

Do data analysts need to be good at math?‎

Data analytics requires less math compared to data science. Having a good understanding of basic math and statistical analysis can be useful for success in this field.

What is data analysis with example?

Data analytics looks at what is likely to happen by combining demographic data, purchase data and other information. This can be used for sales analysis to predict customer behavior as the area changes. It helps businesses understand if they will be able to stay in that particular area.

Mark Roberts is a freelance writer and tech enthusiast based in San Diego, specializing in internet security and Ai tools.

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