Data Preparation – A Gentle Guide To The Land Of Data


You may have heard that Big Data is a gold mine for businesses, but getting that gold takes some work. If you’ve ever tried to use your data but found it hard to understand or unorganized, there’s no need to worry. In this article, we’ll walk through the process of preparing your data so you can get more out of it and achieve results faster.

Here’s the basic data to get started with

Before we get started, let’s take a look at what data preparation is and why it’s important. Data preparation is the process of ensuring that your data is ready for analysis. This involves cleaning, transforming and enriching your raw data in order to make them usable by your analytics tools.

Data preparation is critical because it allows you to extract maximum value from your existing information assets without having to rebuild them from scratch or create new ones from scratch (which can be costly). It also helps ensure that any conclusions drawn from analyses are valid due to the quality of the underlying data being used for those analyses.

Getting ready for your data.

Data preparation is an important step in the analytics process. It’s where you clean your data and make sure it’s accurate, consistent and ready for analysis.

Before you can analyze data, you need to get it ready for analysis by checking its quality and making sure it’s consistent across datasets.

Create a data dictionary

A data dictionary is a structured way to define the contents of your data. It helps you understand your data, communicate with others about it and identify questions to ask about your data.

The goal of creating a dictionary is not to create content; rather, it’s to give context and meaning so that people can effectively use their information.

Cleaning your data

In this section, we’ll look at the most common types of errors and how to clean them up.

  • Duplicate Records: This is one of the easiest problems to fix. You will want to remove duplicate records from your dataset by using a “remove duplicates” function or command in your data cleaning software.
  • Missing Values: If there are any missing values in your dataset, you can fill them in with an average value or some other reasonable substitute depending on what kind of data it is (e.g., if it’s numeric). If it’s categorical information like gender or ethnicity, then use another column in addition to filling out the missing ones so that any missing values are preserved but not confused with other categories (e.g., male vs female).
  • Outliers: An outlier is simply an observation that does not follow expected patterns within its context; for example, if all observations have similar means but one has an unusually high mean value compared with others then this would be considered an outlier because his/her score deviates significantly from what would normally be expected given his/her own characteristics (e.,g., height) as well as those shared among similar individuals within our sample population.”

Missing Data Imputation and Resampling

Missing data is a common problem and one that can be mitigated by imputation. Imputation is the process of filling in missing values. There are several different methods for imputing missing data, but one of the most popular is mean substitution. This method involves replacing each missing value with the average value for all other observations with similar characteristics (e.g., age).

Another way to fill in missing values is with resampling, which involves drawing samples from a known population and using those samples as estimates when performing statistical analyses on your own dataset—a process known as bootstrapping or jackknifing. For example, if you have 100 customers who have provided feedback about their experience at your business over time but only 80 have provided their age, you might use 20{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} of those 80 customers’ ages as an estimate for each person’s age who did not provide it themselves by drawing 5 new sets of four ages from the 80 customers randomly selected above (thereby creating 4 new groups).

Sampling, Weighting, and Adjustment

Sampling is the process of selecting a subset of records (a sample) from a larger population (the population).

The selection process can be random or non-random. In simple terms, if you’re going to draw conclusions about an entire group based on what you find in your sample, then your results will be more accurate if the sample is truly representative of that group. For example: If I want to know how many people who live in New York City are Democrats and Republicans, but instead just ask 10 random people walking down 5th Avenue if they’re Democrats or Republicans — well then it’s probably not going to give me accurate information about whether most people living in NYC are Democrats or Republicans because those 10 people aren’t necessarily representative!

Checking your data quality

After you’ve collected your data and cleaned it, it’s time to check its quality. You might not think that checking for errors is necessary at this point in the process, but it can save you from spending time cleaning bad data later on.

  • Check for missing values

If any of your columns have missing values (i.e., they contain “N/A” or “-“), then those columns need to be fixed before continuing on with other steps in this tutorial series. Missing values are problematic because they can cause inaccurate analysis results if left unchecked; therefore, we’ll cover how to handle them later in this section of our guide!

  • Compare similar items within each column against each other

Once all of your columns have been checked for missing values, compare them against one another by looking at similar items within each column–for example: comparing age across genders or education level across ethnicities would be appropriate here because both variables represent different pieces of information about someone’s identity (their gender versus their ethnicity). You should also look closely at demographic variables like race/ethnicity and household income level when comparing between groups because these two factors often go hand-in-hand with one another due to racial discrimination policies being put into place throughout history which kept minorities out of certain industries such as healthcare professions like nursing school until recently (and even now!).

You don’t need to be afraid of the land of data.

The land of data is vast and beautiful, but it can be daunting to enter. There are many paths to take, and you may not know where to begin. Don’t worry! I’m going to guide you through the process step by step so that when you’re done, your data will look like this:

  • Image Credit: []


Data preparation is a crucial part of your analysis, and it’s important to understand what you are doing. The good news is that it doesn’t have to be hard or scary – with the right tools and some basic knowledge of statistics, anyone can clean their data!

Ronald Nelder

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