12 Useful Data Analysis Methods
Data needs to be refined before it can be used effectively. To do this, data analysts use various methods to collect, extract, and refine raw data. Below, we’ll detail 12 of the most useful methods that you can use on your next data science project.?
What Is Data Analysis?
Data analysis is the process by which raw data is converted into information that is both relevant and actionable. That information is extremely valuable to businesses because it allows them to make informed decisions based on empirical data and statistical analysis.
12 Data Analysis Methods
The data analysis process isn’t a single technique or step. Rather, it employs several different methods to collect, process, and the data to deduce insights and actionable information.
Here are the 12 most useful data analysis methods:
1. Regression Analysis
Example
Say you plotted the daily sales figures of your business on the y-axis of your graph. On the x-axis, you plotted the amount of rain that fell on the corresponding days. Looking at the data points, you could, with some certainty, predict how the rain (your independent variable) impacts sales (your dependent variable).
2. Dispersion Analysis
Example
Investors often use dispersion analysis to assess the risk of an investment. By looking at the dispersion of returns on a certain investment, investors can gauge its risk. Say you’re looking at a stock that has high dispersion. In other words, its range of possible outcomes (returns) is far apart. One month its growth was 5x the market average; in a different month, its losses were 5x as severe. From this dispersion, you would infer that this is a volatile stock and investing in it is a high-risk proposition.
3. Artificial Neural Network Analysis
How Does It Work??
Artificial neural network analysis works by introducing data into the network in order to train it how to make predictions—it’s similar to how the human brain works. These predictions are tested for accuracy and then refined. As more data is introduced, the network continues to “learn” and update its predictions.?
Example
Neural networks are frequently used in the finance sector to forecast market outcomes over time, analyze transactions, assess risk, and more.
4. Grounded Theory Analysis
Example
An example of grounded theory analysis would be a human resources department conducting research on low employee morale. Following the specific set of procedures required for grounded analysis, researchers would collect data (e.g., conduct interviews, observe behaviors, etc.), then analyze the results to determine the underlying cause of the low morale.?
5. Cluster Analysis
How Does It Work??
Using this technique, analysts collect similar data points from a given set of data and put those points into a group, or cluster. Analysts can then look for patterns within those clusters in order to glean insights and predict future behaviors.
Example
In marketing, cluster analysis is used to sort a large and eclectic customer base into smaller groups of shoppers with similar demographics. This is how we get targeted advertising.
6. Cohort Analysis
Example
An example of cohort analysis would be if your company offered a $100 instant rebate to customers who buy a specific product through your online store. Customers who purchase the product and claim their instant rebate are your cohort. For the next 12 months, you track the purchasing behavior of those customers to see if any patterns arise. Do they instantly spend their rebate? Do they buy accessories related to the original product they purchased? What percentage of the cohort participates in other instant rebate promotions? Analyzing the behavior of your cohort gives you a better understanding of their shopping patterns and allows you to predict what their future behavior might be.
7. Factor Analysis
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Example
An example of factor analysis could be an employee satisfaction survey sent out to 100 people in your office. The surveys are comprehensive, and what you get back is an enormous dataset that tells you hundreds of different things about every person who took the survey. Instead of trying to analyze each survey, you can use factor analysis to group the surveys into manageable groups. For example, you might discover a strong correlation between salaried employees and employees who contribute to the max amount to their 401K—these variables can be grouped.?
8. Text Analysis
How Does It Work??
Text analysis uses an array of algorithms trained to associate certain words with certain thoughts, feelings, or opinions. This information is used to deduce how customers feel about a particular thing.?
Example
For example, if someone inputted the textual data “boring” to describe a newly released film, that data would be tagged as expressing a negative sentiment.
9. Time Series Analysis
How Does It Work??
By measuring the same, specific variable at different points in time, data analysts can pick out trends and patterns that allow them to make informed predictions about future events.
Example
A simple example of time series analysis is if you noticed a surge in stocking cap sales during the month of November every year. Using time series analysis, you could predict that this November stocking cap sales would be high.
10. Monte Carlo Simulation
Example
An example of a Monte Carlo Simulation is if you were trying to calculate the likelihood of rolling a particular value using a standard set of dice. The simulation would assign random values between 1 and 6 to each die, then record the sum. The process would repeat, each time using random values between 1 and 6 for each die. After enough iterations, the simulation will yield the range of all possible outcomes (i.e., the sum of all possible rolls) and the likelihood of each.
11. Discourse Analysis
How Does It Work??
There are multiple approaches and techniques available to conduct discourse analysis. Regardless of which method of discourse analysis you choose, the general outline follows these steps:?
Example
One example of discourse analysis would be if you wanted to know whether your colleagues were more forthcoming about their personal lives outside of work. After establishing context, you could observe how they spoke to each other and the topics they discussed in the company cafeteria versus outside the office in a social situation.
12. Evolutionary Programming
How Does It Work??
Data scientists apply the same population-based variation and selection model originally created for evolutionary programming to real-world data structures and optimization problems. These problems include everything from traffic planning to predicting how likely someone is to default on their mortgage.
Example
Evolutionary algorithms are often used in data mining to generate predictive rules derived from datasets.????