Category : Data

One of the most important aspects of statistics is the data that you have. The reality is that when you get a bunch of data, you need to take the time o prepare it. While this may seem a very simple and fast task, the truth is that it isn’t. Preparing data can be incredibly slow but is extremely important before you start with its analysis. 

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Something that most people tend to assume is that preparing data can b very fast. So, if you are working with a client, it’s important that you explain to him that this is a slow process. Nevertheless, even if you say it will take you one hour, your client will still have unrealistic expectations that it will be a lot faster. 

The time-consuming part is preparing the data. Weeks or months is a realistic time frame. Hours is not. Why? There are three parts to preparing data: cleaning it, creating necessary variables, and formatting all variables.

Preparing Data For Analysis Is Crucial

#1: Data Cleaning:

Data cleaning means finding and eliminating errors in the data. How you approach it depends on how large the data set is, but the kinds of things you’re looking for are:

• Impossible or otherwise incorrect values for specific variables
• Cases in the data who met exclusion criteria and shouldn’t be in the study
• Duplicate cases
• Missing data and outliers
• Skip-pattern or logic breakdowns
• Making sure that the same value of string variables is always written the same way (male ≠ Male in most statistical software).

You can’t avoid data cleaning and it always takes a while, but there are ways to make it more efficient. For example, one way to find impossible values for a variable is to print out data for cases outside a normal range.

This is where learning how to code in your statistical software of choice really helps. You’ll need to subset your data using IF statements to find those impossible values. But if your data set is anything but small, you can also save yourself a lot of time, code, and errors by incorporating efficiencies like loops and macros so that you can perform some of these checks on many variables at once.

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#2: Creating New Variables:

Once the data are free of errors, you need to set up the variables that will directly answer your research questions.

It’s a rare data set in which every variable you need is measured directly.

So you may need to do a lot of recoding and computing of variables.

Examples include:

• Creating change scores
• Creating indices from scales
• Combining too-small-to-use categories of nominal variables
• Centering variables
• Restructuring data from wide format to long (or the reverse)

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#3: Formatting Variables:

Both original and newly created variables need to be formatted correctly for two reasons:

• So your software works with them correctly. Failing to format a missing value code or a dummy variable correctly will have major consequences for your data analysis.
• It’s much faster to run the analyses and interpret results if you don’t have to keep looking up which variable Q156 is.

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Examples include:

• Setting all missing data codes so missing data are treated as such
• Formatting date variables as dates, numerical variables as numbers, etc.
• Labeling all variables and categorical values so you don’t have to keep looking them up.

While we can definitely state that the Internet changed a lot of our lives (for both people and businesses), we can’t exclude cloud computing. 

When you are studying statistics, you know that technology is crucial. After all, dealing with so many different types of data and running advanced models can’t be done manually. 

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The reality is that cloud computing enhanced, even more, the use of technologies especially in what is related to data science. After all, it offers numerous benefits without paying any additional cost or making new investments in hardware and infrastructure. 

The reality is that companies today generate vast volumes of data and to drive insights out of this data, they need to inevitably leverage data analytics. Simply put, data analytics helps in driving business growth by enhancing their services and gaining a competitive edge. Larger companies can successfully analyze and gain data insights internally, but smaller companies often rely on external third-party sources to obtain data insights. So, to eliminate this external dependency for data insights, cloud computing service providers can be of great help.

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What Is Cloud Computing?

Ultimately, with cloud computing, companies can easily access a wide range of services such as servers, databases, and even software. So, companies can set up and run their applications with data center providers without a lot of costs. 

With cloud computing combined with data science, companies can handle complex projects at very minimum prices, which would have else been a costly affair. 

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Why You Should Consider Cloud Computing For Data Science

When you have a company, you know that establishing your own servers just to handle your data can be extremely expensive. While this isn’t usually an obstacle for bigger companies, it is a huge barrier for smaller companies. Besides, servers can take a lot of space. And this is something most smaller companies don’t have as well. Then, you still need to consider timely backups and regular maintenance to ensure that your servers are always up and running. 

So, instead of the high costs, a lot of space, and even many special human resources, you can do the same tasks by using cloud computing. Ultimately, companies can host their data without being worried about servers. Besides, they can access the server architecture present in the cloud as per their needs and pay only for the data/service that they require on the cloud. 

Cloud has made data accessible to everyone in unique ways. Companies can perform data analytics with ease and compete on the global level without being concerned about the extra costs related to Data Science. Due to its growing popularity, Cloud computing and Data Science have given rise to Data as a Service or DaaS.

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What If You Don’t Use Cloud Computing For Data Science?

Well, when you can’t use cloud computing, you will need to rely on local servers to store your data. So, every time you need to use this data, you need to extract the data required from the servers to your systems. 

Notice that the simple process of transferring data from traditional servers to systems can bring some problems since companies are dealing with a huge volume of data. 

There’s no question that in statistics, you need to ensure that the data that you collect has good quality. However, unlike what you may think, this isn’t always an easy task. The truth is that a company may experience quality issues when integrating data sets from various applications or departments or when entering data manually. So, we decided to share with you the steps you need to proceed when you want to collect high-quality data. 

5 Steps To Collect High-Quality Data

#1: Data Governance Plan:

When you are looking to collect high-quality data, you need to ensure that you begin with a data governance plan. Simply put, this plan shouldn’t only talk about ownership but also about classification, sharing, and sensitivity levels. But above all, it’s important that it follows in detail with procedural details that outline your data quality goals. 

So, you need to ensure that it has the details of all the personnel involved in the process and each of their roles and more importantly a process to resolve/work through issues.

Ultimately, you can see data governance as the process of ensuring that there are data curators who are looking at the information being ingested into the organization and that there are processes in place to keep that data internally consistent, making it easier for consumers of that data to get access to it in the forms that they need.

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#2: Data Quality Guidance:

When you collect high-quality data, you know you need to separate good data from bad data. This means you need to have a clear guide to use. 

Overall speaking, you will need to calibrate your automated data quality system with this information, so you need to have it laid out beforehand. Notice that this step also includes the validation of the data before it can be further processed. This ensures that data meets minimal standards.

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#3: Data Cleansing Process:

While you may have a good process in place to set apart good data from bad data, you still need to use a data cleansing process to look for flaws in your datasets. 

You need to make sure that you provide guidance on what to do with specific forms of bad data and identifying what’s critical and common across all organizational data silos. 

One of the things to keep in mind is that implementing data cleansing manually is cumbersome as the business shifts, strategies dictate the change in data and the underlying process. 

#4: Clear Data Lineage:

When you want to collect high-quality data, you know that this data comes from different departments and digital systems. So, it’s imperative that you have a clear understanding of data lineage. This means knowing how an attribute is transformed from system to system interactions and provide the ability to build trust and confidence. 

Simply put, data lineage is metadata that indicates where the data was from, how it has been transformed over time, and who, ultimately, is responsible for that data.

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#5: Data Catalog And Documentation:

The last step of how to collect high-quality data is related to data catalog and documentation. 

Improving data quality is a long-term process that you can streamline using both anticipations and past findings. 

So, when you document every problem that is detected and associated data quality score to the data catalog, you reduce the risk of mistake repetition and solidify your data quality enhancement regime with time. 

When you are learning statistics and studying simple models, you are probably not aware that missing data is something very common in statistics. The reality is that date from experiments, surveys, and other sources are often missing some data. 

One of the most important things to keep in mind about missing data in statistics is that the impact of this missing data on the results depends on the mechanism that caused the data to be missing. 

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Data Are Missing For Many Reasons

• Subjects in longitudinal studies tend to drop out even before the study is complete. The reason is that they may have either died, moved to another area, or they simply don’t see a reason to participate. 
• In what concerns surveys, these usually suffer from missing data when participants skip a question, don’t know, or don’t want to answer. 
• In the case of experimental studies, missing data occurs when a researcher is unable to collect an observation. The researcher may become sick, the equipment may fail, bad weather conditions may prevent observation in field experiments, among others. 

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Why Missing Data Is Important In Statistics

Missing data is a very important problem in statistics since most statistical procedures require a value for each variable. Ultimately, when a data set is incomplete, the data analyst needs to decide how to deal with it.

In most cases, researchers usually tend to use complete case analysis (also called listwise deletion). This means that they will be analyzing only the cases with complete data. Individuals with data missing on any variables are dropped from the analysis.

While this is a simple and easy to use approach, it has limitations. The most important limitation, in our opinion, is the fact that it can substantially lower the sample size, leading to a severe lack of power. This is especially true if there are many variables involved in the analysis, each with data missing for a few cases. Besides, it can also lead to biased results, depending on why the data are missing.

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Missing Data Mechanisms

As we already mentioned above, the effects on your model will depend on the missing data mechanism that you decide to use. 

Overall speaking, these mechanisms can be divided into 4 classes that are based on the relationship between the missing data mechanism and the missing and observed values. 

These 3 designs look like repeated measures.

#1: Missing Completely At Random (MCAR): 

MCAR means that the missing data mechanism is unrelated to the values of any variables, whether missing or observed.

Data that are missing because a researcher dropped the test tubes or survey participants accidentally skipped questions are likely to be MCAR. Unfortunately, most missing data are not MCAR.

#2: Non-Ignorable (NI):

NI means that the missing data mechanism is related to the missing values.

It commonly occurs when people do not want to reveal something very personal or unpopular about themselves. For example, if individuals with higher incomes are less likely to reveal them on a survey than are individuals with lower incomes, the missing data mechanism for income is non-ignorable. Whether income is missing or observed is related to its value.

#3: Missing At Random (MAR):

MAR requires that the cause of the missing data is unrelated to the missing values but may be related to the observed values of other variables.

MAR means that the missing values are related to observed values on other variables. As an example of CD missing data, missing income data may be unrelated to the actual income values but are related to education. Perhaps people with more education are less likely to reveal their income than those with less education.

While you may think that by only knowing statistics better you have all that it takes to do data analysis, this isn’t quite true. The reality is that you should keep in mind that statistical knowledge is only a part of the equation. The second part os developing data analysis skills. 

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One of the things that you should keep in mind about data analysis skills us that they apply to all analyses no matter the software or statistical method you are using. 

In order to start developing these data analysis skills, you need to have some statistical knowledge. However, as you learn these skills, you’ll notice how statistics make more sense. 

6 Data Analysis Skills Every Analyst Needs

#1: Planning The Data Analysis:

When you have a data analysis project, you want to ensure that you have a plan. The truth is that it will allow you to think ahead on critical decisions that may take you a lot of time if you have to redo them again. 

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#2: Managing The Data Analysis Project:

When you are working on a data analysis project, no matter if you are doing it alone or with others, you need to manage it. This includes keeping track of the times, dedicating enough time to each step, and even find the resources that you need. 

#3: Cleaning, Coding, Formatting, And Structuring Data:

When you are working on a data analysis project, you always want to ensure that your data is cleaned before you even start. But your work doesn’t stop there. After all, you will need to code and format the variables and then structure them according to your plan. 

Notice that this is probably the step that takes longer. 

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#4: Running Analysis In An Efficient Order: 

One of the things that is important to keep in mind is that there is a specific order that you need to obey when you are running the steps of your analysis. Besides, you will need to make decisions at every step. When you don’t do this, your analysis will not only be slower but frustrating as well. Besides, you’re likely to make mistakes. 

#5: Checking Assumptions And Dealing With Violations:

Unlike what you may have heard, all statistical test and model has its own assumptions. The truth is that there is a lot of skill in reading uncertain situations and drawing conclusions. 

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#6: Recognizing And Dealing With Data Issues:

One of the things that you probably already know is that real data is messy data. This means that real data has issues that make the analysis hard. From small sample sizes to outliers, and even truncated distributions can happen in all types of data sets. So, you need to ensure that you recognize when a data issue is happening as well as you need to determine if it will cause problems or what you need to do about it. 

When you think about data, it is important that you keep in mind that there are two different approaches that you can adopt: the generative and the analytical approach. 

So, let’s take a look at each one of these models for data analysis.

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Generative Model For Data Analysis

Simply put, when you use the generative model for data analysis, the process will focus on the process by which the analysis is created. This means that you need to develop an understanding of the decisions that you make from one step to the other so that you can recreate or reconstruct a data analysis. 

One of the things that you need to keep in mind about this model is the fact that it tends to take place inside the data analyst’s head which means that it can’t b observed directly. So, when you need to take measurements, you will need to ask the analyst directly. However, the main problem is that this is subjected to a wide range of measurement errors. Notice that on some occasions, you may have access to partial information when the analyst writes down the thinking process through a series of reports or if a team is involved and there is a record of communication about the process. 

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This model tends to be quite useful for understanding the “biological process”, i.e. the underlying mechanisms for how data analyses are created, sometimes referred to as “statistical thinking”. 

Analytic Model For Data Analysis

With this approach, you will ignore the underlying processes that serve to generate the data analysis and you will focus on the observable outputs of the analysis. These outputs may be an R markdown document, a PDF report, or even a slide deck. 

The main advantage of using this approach is that the analytic outputs are real and can be directly observed. However, it’s worth noting that the elements placed in the report are the cumulative result of all the decisions made through the course of a data analysis.

Many people tend to refer to the analytical model for data analysis as the physician approach since it basically mirrors the problem that a physician confronts. 

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What Is Still Missing?

After analyzing both models – the generative and the analytical models for data analysis, it is worth to state that we believe that something is still missing. 

The reality is that when you are gathering new data, you need to think about the answers that you’re trying to get. This ensures that you need to achieve a balance between matching the principles of both the analyst and the audience. So, summing up, for both the generative model and the analytical model of data analysis, the missing ingredient is a clear definition of what makes a data analysis successful. The other side of that coin, of course, is knowing when a data analysis has failed. 

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While the analytical approach is useful because it allows you to separate the analysis from the analyst and to categorize analyses according to their observed features, the categorization is unordered unless we have some notion of success. 

On the other hand, the generative approach is useful because it reveals potential targets of intervention, especially from a teaching perspective, in order to improve data analysis. However, without a concrete definition of success, you don’t have a target to strive for and you do not know how to intervene in order to make genuine improvement.

As you already know, data is crucial. And when you are doing data science, you need to do research. Ultimately, you want to ensure that the data that you collect can answer a question, improve a current product, come up with a new one or identify a pattern. So, as you can easily understand, the common factor to all these is that you want to make sure that you use the data to answer a question that you haven’t answered before. 

Getting High-Quality Data

When you are trying to answer a question, the first thing you will do is to collect and then store it. However, you need to be careful about the storage process. After all, the state and quality of the data that you have can make a huge amount of difference in both how fast and how accurately you can get your answers. The truth is that if you structure the data for analysis, then you will be able to get your answers a lot faster. 

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The truth is that you can get your data from many different sources and you will need to store it depending on the questions that you want to answer. 

Creating research quality data is the way that you refine and structure data to make it conducive to doing science. It means that the data is no longer as general purpose, but it means you can use it much, much more efficiently for the purpose you care about – getting answers to your questions.

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When we talk about research quality, we are referring to data that is easy to manipulate and use, is formatted to work with the tools that you are going to use, is summarized the right amount, has potential biases clearly documented, is valid and accurately reflects the underlying data collection, and combines all the relevant data types you need to answer questions. 

One of the things that you need to pay attention to is when you are summarizing the data. The truth is that you need to know what are the most common types of questions that you want to answer as well as the resolution that you need to answer them. With this in mind, you may consider summarizing things at the finest unit of analysis you think you will need – it is always easier to aggregate than disaggregate at the analysis level. Besides, you should also need to ensure that you know what to quantify. 

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Organizing Data The Right Way

The reality is that one of the main difficulties many people have is related to the organization of the data after they collect it. 

Ultimately, you just want to ensure that you can organize your data in a way that allows you to complete frequent tasks quickly and without large amounts of data processing and reformatting. 

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One of the things that you need to know about high-quality data and the ways you have to store it is that each data analytic tool tends to have different requirements on the type of data you need to input. For example, many statistical modeling tools use “tidy data” so you might store the summarized data in a single tidy data set or a set of tidy data tables linked by a common set of indicators. Some software (for example in the analysis of human genomic data) require inputs in different formats – say as a set of objects in the R programming language. Others, like software to fit a convolutional neural network to a set of images, might require a set of image files organized in a directory in a particular way along with a metadata file providing information about each set of images.