Data analysis is an aspect of data science that is all about analyzing data for different kinds of purposes. It involves inspecting, cleaning, transforming and modeling data to draw useful insights from it. Show
What Are the Different Types of Data Analysis?
With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including — business, science and social science, among others. As businesses thrive under the influence of many technological advancements, data analysis plays a huge role in decision making, providing a better, faster and more efficacious system that minimizes risks and reduces human biases. That said, there are different kinds of analysis catered with different goals. We’ll examine each one below. Two Camps of Data AnalysisData analysis can be divided into two camps, according to the book R for Data Science:
Data analysis can be separated and organized into six types, arranged in an increasing order of complexity.
1. Descriptive AnalysisThe goal of descriptive analysis isto describe or summarize a set of data. Here’s what you need to know:
Descriptive Analysis ExampleTake the COVID-19 statistics page on Google for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus. Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data. More on Data Analysis: Data Scientist vs Data Analyst: Similarities and Differences Explained 2. Exploratory Analysis (EDA)Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:
Exploratory Analysis ExampleClimate change is an increasingly important topic as the global temperature is gradually rising over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature. Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses. 3. Inferential AnalysisInferential analysis involves using a small sample of data to infer information about a larger population of data. The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:
Inferential Analysis ExampleThe idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. 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. Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions. 4. Predictive AnalysisPredictive analysis involvesusing historical or current data to find patterns and make predictions about the future. Here’s what you need to know:
Predictive Analysis ExampleThe 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. More on Data: Explaining the Empirical for Normal Distribution 5. Causal AnalysisCausal analysislooks at the cause and effectof relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:
Causal Analysis ExampleSay you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome. Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another. 6. Mechanistic AnalysisMechanistic analysis is used tounderstand exactchanges in variables that lead to other changes in other variables. Here’s what you need to know:
Mechanistic AnalysisExampleMany graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society. Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science. A tutorial on the different types of data analysis. | Video: Shiram VasudevanWhen to Use the Different Types of Data Analysis
A few important tips to remember include:
Which of the following best describes the data analysis?Which of the following best describes data analysis? A. It is the process of studying the valuable insights and drawing the necessary data for it.
Which of the following best describes the goal of data visualization?The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets. The term is often used interchangeably with others, including information graphics, information visualization and statistical graphics.
What is the regression approach How might the regression approach be used in auditing?The use of regression analysis builds a model based on the relationship of variables from past data. Current data is used to predict current account balances, and the auditor relies on confidence intervals resulting from this model to determine if the recorded amount for an account is deemed rea- sonable.
What is predictive analytics quizlet?Predictive Analytics. The use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on. historical data.
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