# Data Analysis Life Cycle in simple terms

**Introduction**

This article is about the data analysis life cycle in simple terms. You will learn about Data preparation, Data exploration, Model building, Model evaluation, and Presentation. If you are interested in learning Data Science, you can check out Data Science courses in Kerala.

The life cycle of a business or scientific problem is typically represented as a linear sequence of activities from question to solution. The data analysis life cycle is an iterative process with an emphasis on conceiving and testing new models. We will discuss how we can conduct our data analysis projects in this way:

**Data preparation**: In this step, we prepare the data by transforming it into workable form for further processing.

**Data exploration**: In this step, we reveal patterns among observations that could represent relationships between variables or observations.

**Model building**: In this step, we select and design models that fit the data and address the problem at hand.

**Model evaluation**: In this step, we assess the quality of model fit, evaluate the assumptions made by statistical modelling, and determine if a proposed model is suitable or needs to be improved.

**Presentation**: In this step, we translate our findings into actionable information.

The data analysis life cycle is a rather general framework, so it can be used in many ways to solve data analysis problems. There are many different approaches to how you can use this framework; however, one approach that you should use is the “Magnum Opus” approach. The Magnum Opus approach consists of the steps outlined above, namely: Data preparation, Data exploration, Model building, Model evaluation, and Presentation.

Through time, data has become more and more useful. There are many reasons for this. Here are a few:

- A lot of information is being collected, so more data is readily available.
- New methods are being developed for storing and managing data efficiently.
- Creators of software have made their code more accessible to the general public.
- There is a greater need for data analysis tools that can easily handle large amounts of data.
- And, more and more scholarly researchers require that their data be available to other scholars and not held hostage.

Whatever the reason for this increase in the availability of data, there should be no doubt that we have the capability to use it in a way to answer our research questions. This means that we must learn how to analyze large, diverse, and unstructured types of data efficiently.

In terms of performance criteria, we should consider two attributes: speed and accuracy — both of which measure different aspects of our ability to extract meaningful information from the information we collect (Fair weather, Burgess).

In terms of speed, it is not uncommon for researchers to work with data that are distributed across many different servers. The more servers there are, the more responses are sent to and from each server. When a response is received, the data and information contained in it must be analyzed and interpreted in order to answer our questions. The quicker we can interact with our data, the faster we can complete our analysis tasks. In addition, as time goes on, richer datasets become ever more complex (Ullman & Daintith). This calls for methods that take longer to compute, but that do a better job of finding patterns and relationships among variables than simpler methods (Burgess).

**Data Analysis Methods**

There are several methods that can be used in a data analysis project. Roughly, there are two categories of approaches to data analysis:

- statistical approaches
- non-statistical approaches.

Each category has different criteria that must be met, but the bottom line is that one way or the other you must use some path to extract information from the large corpus of data that you have collected. A specific approach is determined by a combination of two or more factors:

- the nature of your research question
- your level of familiarity with the topic
- the type of data you collect (Burgess)

**Why is it important to understand the data analysis life cycle?**

Without this knowledge, you cannot work on a data analysis project. You will not be able to get past the first phase of your project. The following factors can help you create a realistic schedule:

- The amount of effort required to obtain access to the data you need (statistical, questions).
- The time it takes to organize and analyze your data (statistical, questions).
- The amount of time required to validate your results (statistical, questions).

The three main types of data analysis methodologies are: (1) statistical, (2) non-statistical, and (3) hybrid. There is no right answer for which one method you should use. Each has its pros and cons, so you should be able to identify what your research asks with each one. With that information, you can then make an informed choice on the most appropriate methodology for you to use.

The following describes each of these methods in more detail:

**Statistical Data Analysis**

Statistical data analysis techniques involve the use of software and computational algorithms to evaluate a large set of data. This method requires that you have an understanding of probability theory and statistics.

You will have to select the appropriate statistical method for your data set, using guidance from statistical methods textbooks and local subject-matter experts. s

Statistical methods can be divided into the following categories:

- Multivariate analysis
- Regression analysis
- Correlation analysis
- Nonparametric statistics

**Non-Statistical Data Analysis**

Many non-statistical approaches to data analysis are based on knowledge of what we do know. This often involves finding relationships in our data, which we believe exist, but that have not been proven or falsified. These relationships form the basis for new inferences and hypotheses. In many cases, non-statistical approaches are more exploratory than statistical methods because they give you a chance to find patterns that you would not otherwise notice.

In developing your data analysis plan, there are a few things you should consider:

- If you have the time, perform a simple statistical analysis using spreadsheets or statistical software. Once you know the magnitude of the relationships in your data or the types of relationships that exist between them, you can decide if deeper statistical analysis is required.
- Remember that non-statistical analyses may not bring about clearly defined answers to your research questions. Your findings may be different from what you originally expected. But that can be good because it may give you an opportunity to discuss any flaws in your original question and possibly improve it (Burgess).

**Hybrid Data Analysis**

When it comes to data analysis, this is probably the most widely used and also most cost-effective of all the methods. Hybrid methods use a combination of statistical and non-statistical techniques. For instance, if we are interested in finding relationships between several variables that define the type of content that is available to users of social networking sites, we may first look at them via regression analysis. If we find that the variables do have an effect on each other and/or on a dependent variable.

**Conclusion**

We have discussed some aspects about data analysis. Data analysis can be done using statistical, non-statistical and hybrid methods. Also, we have to consider the facts like quantity of data, type of data etc. The data analysis life cycle includes the following steps: data preparation, data exploration, model building, model validation, and presentation. There are many things to consider when analyzing large amounts of data. In the upcoming post, we will continue the discussion on data analysis in simple terms. If you want to know more about data science then you can check Data Science training in Kochi.