Data is revolutionizing the world as we know it. As we produce more and more data every day, businesses are finding new ways to put it to good use. But in order to utilize data, you need to know how to analyze it to find data-based insights. That’s why data analysis is so important — and why Data Analysts and Data Scientists are in such high demand.
Ahead, we’ll take a closer look at the different types of data analysis, how it’s used, how it’s performed, and the different careers that use it. (Or if you’d rather learn how to analyze data yourself, check out our Data Scientist: Analytics Specialist career path).
What is data analysis?
Data analysis is the process of collecting and analyzing data for insights. Many businesses use these insights to improve their systems and products — boosting operational efficiency, improving how services are delivered to customers, and refining products.
A Data Analyst is able to examine data and discover how to use it to boost the bottom line of an organization. Because businesses today generate so much data, a Data Analyst has a lot to work with. As a result, they often play a key role in the operation of an organization.
“We’re seeing data transform our society and everything we do,” says Codecademy Data Science Domain Manager Michelle McSweeney, “whether it’s measuring how well something performed or deciding what we’re going to do next.”
The role of big data
As the name suggests, big data involves large amounts of data, and it’s often used to both improve basic processes and generate machine learning models. Machine learning is a branch of artificial intelligence that enables Data Scientists to predict outcomes and mimic the learning processes of humans in machines. The collection of big data gives Machine Learning Engineers enough information to build very accurate prediction models.
For instance, the artificial intelligence systems that control self-driving cars learn based on huge storehouses of image data. Images are aligned with categories, such as “people,” “vehicle,” “animal,” and “road element,” allowing the vehicle to decide what to do based on the kind of object its cameras are viewing.
Big data also plays a critical role in figuring out how customers use products, their buying habits, and how they may react to the release of services or products. For example, a company with a rewards card program can collect data regarding the kinds of products customers purchase, when they do, and where. They can then use that information to create promotions aimed at giving customers exactly what they want, when they want it.
Real-time data refers to data that is collected as it’s generated. When real-time data is used in a process, it gives the application using it more agility, allowing it to adapt to changing circumstances.
A common example of a source of real-time data is the stock market. As the prices of different stocks rise and fall, the data has to be received, processed, and analyzed in order to ensure investors can make the best decisions possible.
Real-time data also plays a central role in certain medical care environments, allowing computer systems to tell doctors and nurses whether they need to respond to a situation immediately or if a patient’s needs don’t necessarily have to be tended to right away.
Machine data is data generated by machines, but this includes a wide variety of industrial and personal tools. Machine data can be generated by:
- Robots on a production floor
- Handheld devices in a warehouse
- Your smartphone
- Computers and monitoring systems in hospitals
- Applications run by a business
- Cybersecurity systems
Machine data is one of the most diverse categories of data, and analyzing it can improve operations, safety, and the quality of services.
Qualitative vs. quantitative data analysis
Quantitative data analysis involves examining data that can be measured. For example, say a spreadsheet contained a collection of customer ratings for a given product. A Data Analyst could use this data to produce tangible, measurable insights.
Qualitative data analysis is different in that it’s based on data that cannot be easily measured using numbers. For example, if a customer responds to a survey asking them to describe their experience, their responses would be qualitative data. Even though qualitative data analysis involves, in some ways, a very different process than the analysis of quantitative data, the insights produced are just as valuable.
Using machine learning, qualitative data can be transformed into quantitative data when the machine learning algorithm studies patterns in the data sets. For example, if 30% of customers included the word “great” in their reviews in March, and that number progressively decreased over the next six months, a machine learning algorithm could observe the trend and even provide recommendations. A customer service team could start implementing the suggestions in time for the holiday season and significantly boost sales revenue.
Why is data analysis important?
Data analysis is important because it gives decision-makers tangible information on which to base their strategy. This information has a wide range of applications, from improving systems and processes to better understanding clients and even human behavior.
For instance, if a high-end coffee chain were selling a new flavor and customers were buying 35% less of it than the company projected, they could take action to scrap the new flavor in favor of a different one. They could even use customer comments and buying habits to craft new flavors.
In some cases, data analysis can mean the difference between a safe environment and one that threatens people’s health or well-being. For instance, you can use data analysis to study the movement patterns of people on a factory floor.
The distances they get from dangerous machinery can be studied in relation to the person’s speed, the time of day, and even the habits of specific employees. You can then use this information to set up boundaries and guidelines that keep people out of harm’s way.
Data analysis also plays an important role because it provides objective information as opposed to subjective, emotional perspectives. While emotions are a crucial element of the decision-making process, they can sometimes get in the way, especially when somebody is personally invested in an outcome.
For example, if an executive is personally invested in the success of a product they helped conceive, they may fail to see some of its flaws. On the other hand, with data analysis, the reactions of end-users can provide concrete numbers that can produce decisions motivated by facts instead of feelings.
Note that while data analysis can provide objective information, data professionals need to be mindful of biases in the data that can influence analysis and insights and lead to poor outcomes.
Data analysis techniques and tools
There are several techniques you can use to analyze data and a variety of technological tools that can make the process faster.
Data analysis techniques
There are many types of data analysis techniques, but the most popular include regression analysis, Monte Carlo simulation, and cohort analysis.
Regression analysis refers to finding the relationship between different sets of variables. Whenever you do any kind of regression analysis, you’re trying to see a connection between independent and dependent variables. A dependent variable is a factor you’re trying to predict or measure, and an independent variable is one that may affect the dependent variable.
For instance, if you’re studying people’s voting habits in a certain town, one of your dependent variables may be the percentage of registered voters that show up to the polls. Your independent variable could be the temperature outside on voting day. You may discover a correlation between how hot or cold the day was and the number of people that went to the polls.
Monte Carlo simulation
Monte Carlo simulation involves a computer analyzing a set of data and then producing a report that outlines the chance of different outcomes happening. In most situations, the data a computer is analyzing has been organized into a spreadsheet, and the computer figures out the percentage of times a certain outcome occurs and then uses that to predict what may happen in the near future.
For instance, if a city’s traffic light system has been set according to configuration A, and there are 165 accidents over the course of two months, but with configuration B, there are 232 accidents, the computer can use this data to predict which configuration is safest. Configuration B produced about 29% fewer accidents than configuration A, perhaps making it the better choice.
Cohort analysis focuses on separating large data sets into smaller groups that are then examined individually. This helps Data Analysts study the behavior and tendencies of specific subjects.
For example, suppose a university has a freshman class of 2,000 people. You can divide these into several smaller cohorts. For instance, you could group the students according to their high school grade point averages. Data Analysts at the university could then follow students in each grade point average cohort and observe how successful they are in different kinds of classes.
You could use this information to provide tailored support services to students based on their cohort. You could also design support systems around specific types of classes or within certain majors, all using the data gathered from the cohort analysis.
Data analysis tools
Computerized tools make data analysis faster, easier, and more accessible. Some of the most common data analysis tools include Microsoft Excel and the programming languages Python, R, and SQL. Our courses can provide you with a strong foundation to build a career as a Data Analyst. Some of our best courses for data analysis are:
Using these tools, you can create your own databases, complete with rules and configurations that allow you to generate data-based insights. You can also incorporate these kinds of databases into other programs.
For instance, you could design a database using SQL that allows users of an eCommerce clothing app to search through products, specifying elements such as size, color, and style. The end user’s search experience can then be tailored in any way you want, ensuring they get the search options they need and stay engaged with the app and the company’s products.
Who uses data analysis?
In reality, nearly any career that brings you into contact with substantial amounts of data can use data analysis. Even if your role doesn’t specifically require you to analyze data, having some background in it can position you to:
- Be a more effective leader — one that makes decisions based on data.
- Offer better insights to executives and managers.
- Come up with more actionable solutions.
That being said, there are some professions where data analysis is a must. These include:
- Data Scientist: A Data Scientist uses algorithms and scientific methods to derive discoveries from vast amounts of apparently random data.
- Data Analyst: A Data Analyst focuses on exploring and visualizing data, applying statistical methods to generate insights, and communicating their findings.
- Data Engineer: A Data Engineer collects and observes data for a wide range of disciplines, setting up pipelines and data warehouses.
- Business Analyst: A Business Analyst is essentially a Data Analyst focused on improving business outcomes.
- Product Manager: A Product Manager uses insights gained from data analysis to strategize the design of a specific product or a sequence of products. These could be physical or digital products, such as software applications.
- Digital Marketer: A Digital Marketer uses data analysis to choose the most effective marketing channels for a product or service.
Getting started with data analysis
The best way to work with data analysis is to get comfortable using the tools and techniques that power data analytics. You can get started today with our Data Scientist: Analytics Specialist career path. In this course, you’ll learn how to use SQL, Python, and various libraries including pandas and Matplotlib, to analyze and visualize data and share your discoveries.
There’s also our Business Intelligence Data Analyst career path. Business intelligence is the fastest route to a career in data analysis, and the versatility and growth opportunities within the field make it a great choice for new and mid-level professionals alike. This career path will teach you all the skills you’ll need to break into the field and help you prepare for the job-hunting process.
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