Businesses are fighting to keep up with changes caused by the digital revolution. That’s why they’re hiring data analysts at an incredible rate. According to the US Bureau of Labor Statistics, the field of market research data analysis will grow 18% in the next ten years, and operations research analysis will grow by more than 25%.
There’s never been a better time to learn how to work with raw data. Here are 10 easy-to-learn data analysis methods to help you kickstart your analytics career.
What Is Data Analysis?
The data analysis process extracts meaning from a collection of raw data to make better decisions. With so much data/information available to us every day, it’s impossible to learn from it without the right data analytics tools. A data analyst will develop a hypothesis, collect quantitative data, and produce models and graphs to spot correlations and prove or disprove their assumptions.
How Is Data Analytics Used in Business?
Data analytics is helping take the guesswork out of business. With a robust data set and the correct method, data analysts can help business leaders make data-driven decisions instead of relying on customer feedback.
Market research is one of the most common subjects of data analysis. The rise of the internet and digital transformation has made it easy to track how customers react to different products daily. Collecting this data en masse allows data analysts to look for actionable insights that lead to better products, marketing campaigns, and customer service.
Similarly, data analytics can help businesses track when and where their customers are most engaged. Using data like ad clicks and website visits, analysts can find opportunities to increase conversions across platforms.
10 Easy Data Analysis Methods
You don’t need to be a data analyst with a Ph.D. to analyze and draw conclusions from data. In fact, there are a host of practical data analysis techniques that are relatively simple to employ.
Anyone can use these ten types of business data analysis to improve their understanding of a data set.
1. Cluster analysis
Also known as discriminant analysis, this technique collects similar data objects into “clusters,” or groups where each member is more alike than different. Cluster data visualizations can help you find trends in your customer base by collecting similar customers and describing what they have in common.
2. Cohort analysis
This method analyzes the data generated over a specific period by a “cohort” or a particular group of related customers. You can track cohorts who made their first purchase after clicking a specific ad, and learn how they behave over the next month or year.
3. Descriptive analysis
If you already have a collection of data, descriptive analysis is a type of statistical data analysis that describes what that data means. You might use descriptive analysis to examine past revenue information to discuss trends and problems.
4. Dispersion analysis
This diagnostic analysis technique identifies the size of your standard deviation, or how different parts of your data set are. It helps judge whether you’ve collected relevant data. If you collect information about a market segment and then use dispersion analysis, you can find outliers and perform better evaluations.
5. Factor analysis
This is a specific type of regression analysis that looks for hidden “factors” that may affect variables. Suppose you notice that ten groups demonstrate just three main purchasing patterns. In that case, factor analysis can help you spot the three fundamental factors that drive the behavior.
6. Monte Carlo simulation
A Monte Carlo simulation is a computer-run predictive analysis that models how likely different possible outcomes might be. For example, a Monte Carlo simulation can help you determine whether a new marketing campaign is likely to raise sales.
7. Neural network analysis
A neural network is a machine learning program that looks for patterns in data the way a brain might. These algorithms are excellent for finding trends in “noisy” data from various sources.
8. Regression analysis
This analytic technique looks for connections between an independent variable and a dependent variable. You might use regression analysis to find relationships between different product prices and the number of products sold.
9. Text analytics
Social media platforms like Facebook have given data analysts a whole new world to explore. Text analysis, or sentiment analysis, looks for trends in phrasing used in written text. It can help you learn when customers feel positive or negative and filter for market opportunities before your competitors.
10. Time series analysis
Time series analysis lets you model and explains how something changes over time. For example, you can use a time series analysis of past sales data for the holidays to predict upcoming holiday demand.
Is data analytics the right career for you?
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Check out Springboard’s Data Analytics Career Track to see if you qualify.