Once data has been collected and cleaned/processed, the next step in the data analysis process is to analyse it. For example, survey and test data may need to be transformed from words to numbers.

Then, you can use statistical analysis to answer your research questions. There are various types of data analysis that are used to gain insights from data. Some of these include:

#Statistical analysis:

This is a category of data analysis that involves investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations to draw valid conclusions from data. Statistical analysis can be divided into two main categories: descriptive statistics and inferential statistics.

a) Descriptive statistics: Descriptive statistics is used to describe the basic features of the data in a study, such as the frequency of each value, the averages of the values, and how spread out the values are. It is the first step in statistical analysis and is used to provide a general overview of the data before more complex analyses are performed.

Descriptive analysis is the very first analysis performed in the data analysis process. It generates simple summaries about samples and measurements. It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

b) Inferential statistics: is used to make inferences about a population based on a sample of data. It involves testing hypotheses and making estimates about the population using sample data.

Inferential statistics is used to determine whether the results of a study are statistically significant and can be generalized to the population. For example, a psychological study on the benefits of sleep might have a total of 500 people involved.

When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world and is thus an inference of the larger population.

#Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

The accuracy of the predictions depends on the input variables.

Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not. Using a variable to predict another one doesn’t denote a causal relationship.

For example: The 2020 US election is a popular topic, and many prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction.

Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose. Predictive analysis takes data from the past and present to make predictions about the future.

#Prescriptive Analysis

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses.

Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.

The prescriptive analytics system is built on Microsoft Azure and includes SaaS templates to quickly connect data from across the company.eg Google Self-driving cars, Waymo is a preferred example showing prescriptive analytics. It showcases millions of calculations on every trip.

Most prominent companies like Microsoft and Oracle use prescriptive analysis to optimise processes. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviour’s they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed.