Every day, Data Scientists use several methods and techniques in order to change raw data into actionable insights and help businesses make informed decisions and reach their goals. Here we will see what are the most used data analysis techniques and when they should be used.
With digital transformation, managing data and analysing it has become central to many business activities. Companies are hiring more and more data experts to clean, transform and model data to source the most reliable insights from it. All these processes include a variety of methods and techniques which can be chosen according to the type of data that needs to be analysed.
Finding your way among all the types of data
It is no coincidence that all the data collected by a company or an organization is named ”’big data’ . Data experts are actually facing a very big amount of data, their job is to clean this data, and sort it in different types so they know how to analyse them later on. To learn more about Big Data, Deep Learning or Data Visualization, discover bootcamps in Data. In these bootcamps, you will also learn that Data is categorized in two main types: Quantitative data and Qualitative data
Quantitative data is anything measurable, it can be specific quantities and numbers but also sales figures, email click-through rates, the number of visitors on your website or any kind of percentage and revenue increases.
Qualitative data cannot be measured, it is, therefore, more subjective and is composed of unstructured data. This includes any kind of text such as comments left in response to a survey question, but also written transcripts of oral speech, interviews. Qualitative data also includes images, photographs and videos as well as social media posts or any kind of product reviews.
Recognising whether your data is quantitative or qualitative will be preponderant in choosing the analysis method you will be working with, so make sure you understand these two principles well.
What are the main data analysis techniques?
Choices over data analysis methods depend on the type of data you have and what you want to achieve with it.
Source: Unsplash
The Regression Analysis
If you have two variables and want to see how one impacts the other, you may want to use regression analysis. This kind of analysis is especially useful for making predictions and forecasting future trends. For an e-commerce website measuring the effect of a social media campaign on sales would be a good example of regression analysis.
The Cluster Analysis
Cluster Analysis aims at identifying structures within a dataset by identifying different data points that look similar to each other. This helps the data expert in seeing how data is distributed into a given dataset.
The Time Series Analysis
Time series analysis is a statistical method helping to identify trends and cycles over a period of time. Trends are stable increases or decreases over an extended period of time. Seasonality shows predictable variations in the data due to seasonal factors, such as a peak in ice cream sales during a hot summer. Cyclic patterns show unpredictable fluctuations which are not linked to seasonality but are rather a result of economic or industry-linked conditions.
The Monte Carlo Simulation
The Monte Carlo simulation is an automated technique that is used to calculate possible outcomes and their probability. The Monte Carlo method is used by data analysts to forecast what might happen in the future and make decisions accordingly. For instance, they may determine how much money a company may make if it hires a certain number of sales and hire 5 new people for a certain salary.
When you have a large dataset, grouping data with similarities is sometimes a good way to have a clearer view of the available data. In that aspect, the Factor Analysis is used to reduce a large number of variables to a smaller number of factors by finding correlations between them and grouping them together. This method is particularly useful not only because it condenses large datasets but also because it allows more manageable samples and helps uncover hidden patterns.
The Cohort Analysis and emotion detection models
The cohort analysis is similar to the factor analysis but with humans” Its principle lies in grouping users according to shared aspects in order to better monitor and understand their actions. This allows you to measure aspects that cannot usually be measured in numbers such as happiness, or customer loyalty and satisfaction for instance. Fine-grained sentiment analysis will help focus on opinion polarity (positive, negative, neutral). You can also use emotion detection models to identify words or face expressions associated with sentiments such as happiness, anger, frustration and excitement. This will give you insight into how your customers feel about you.
These are some of the most used data analysis methods but there are many more you can learn about and develop. A good option is to follow a course to master all the data analysis skills. In this course, you will learn how to master data visualization, create dashboards and hypothesis tests, you will also learn how to use spreadsheets for the analysis of simple datasets and SQL for more important analysis.