Data science has revolutionized how organizations make decisions regarding their business operations. In the contemporary landscape, data is at the core of every company that leverages digital technology to function. It has proved itself as the ultimate weapon for most businesses looking to expand their customer base and increase their revenue figures. 

Data based decision making is only one aspect of the data science definition; a lot is going behind the scenes when it comes to making sense of seemingly irrelevant information that is stored as data. It requires cleaning and laundering all that insignificant bits of information to obtain an actionable input. It is paramount to understand what is meant by data science before exploring how data cleaning is carried out by data science professionals.

So, what is data science? People are often confused and amazed at the possibilities of data science, but most of them have no idea about what all it entails. Well, the scope of data science is a lot broader than it seems at first glance. Let’s delve deeper into the subject to understand what is data science and does it take to make sense of data.

What is Data Science?

As per the data science definition, it can be explained as a discipline that uses an amalgamation of scientific understanding, algorithms, processes, and systems to obtain valuable and actionable insights from seemingly irrelevant pieces of information. In a layman’s term, it can be understood as a study of data that involves gathering, storing, and structuring information to analyze and obtain useful facts from the set. 

The data science industry is a work in progress, and the data science meaning continues to evolve as more avenues are explored. Today, the role of a data scientist is not just limited to data mining or analyzing bulk data; they are required to know the whole life cycle of data science to uncover the latent potential of this discipline. Now that we’ve understood what is data science, let’s find out how data cleaning is done to make the information gathered usable. 

The ‘What’ and ‘How’ of Data Cleaning 

Let’s use an analogy here to understand what is data cleaning and what all it entails. What do you do before preparing your meal? Do you just throw your vegetables in a hot pan and let it cook? We are guessing that most of us don’t do that. Before preparing our meals, we generally start with washing the vegetables and cutting it into small pieces to make it ready for cooking. Just like that, data scientists also need to prepare their ingredients before they start their analysis. Data is their ingredients, and the data cleansing process is how they prepare their ingredients for the next step. 

Data cleaning is defined as the process of vetting data to identify any anomalies and modify or remove information that is irrelevant, incomplete, or incorrectly formatted. It is the process in which the data collected is prepared for analysis by filtering out the random and unwanted bits. The quality of analysis is directly contingent on how relevant the collected data is. 

Data cleansing helps to improve the accuracy of the data set. The insights obtained after analysis are only as good as the quality of data that is processed to extract that knowledge. This is why it requires careful inspection and filtering of the accumulated data sets. Let’s dig deeper into how the data cleaning or cleansing process is carried out to improve the quality of the analysis.

Treating Irrelevant Data

The relevancy of data is measured by how helpful it is in solving our problem or achieving our objective. Irrelevant data are those that don’t have a place in the context of the problem. Suppose that you want to identify the average height of class 10 students, here their weights or phone numbers would be irrelevant to our objective, and so it won’t be relevant data. You can quickly drop out the information regarding weight and phone number for your analysis. 

Duplicate Information

Duplicates are the repeated data points in your information set. Duplication is very normal when you are combining data sets from multiple sources or due to a technical glitch in collecting survey data. It could lead to the same data input more than once and can result in duplication. It should be removed to avoid any inconsistency in the analysis process.

Data Type Conversions

The information collected should be stored in a standard format that is widely accepted; this is what data type conversion is all about. For example, a data-related height should be stored as a height object, like in centimeters or inches. Qualitative values should be converted into numbers if required. You can spot it by looking at the data types of each column in summary. If any value can’t be converted into the required data type, it should be stored as N/A to produce a warning sign.

Syntax Errors and Standardization

While cleaning your data, you need to work on the syntax errors. For example, extra white spaces at the start or end of a string should be removed. Also, the numeric data should be stored in a way that all values have the same number of digits; the smaller ones can be padded with zeros to achieve the mark. There are multiple ways to add strings, and this might cause a typo that should be fixed, and a uniform representation should be used for a given value.

Scaling and Normalization

Scaling is all about grouping and categorizing data in a particular range to make a uniform representation, for example, grouping marks of students in a range of 0-20, 20-40, 40-60, etc. Also, these scores can be rescaled into percentages rather than absolute values. Scaling helps a great deal when it comes to plotting certain types of data. It helps to reduce the skewness in data sets. It also helps to compare different types of values. 

Normalization helps to rescale the values, the objective here is not just facilitating comparison but also to transform data so that it is normally distributed. Some statistical methods require the normal distribution of data to function; normalization is extremely useful in those cases. In addition to the points mentioned above, the missing values and outliers in data sets should also be taken care of to build a quality data set that helps to achieve valuable insights after analyzing. This article has helped you in understanding what is data science and steps that you need to follow while carrying out data cleansing to build a high-quality data set. If you want to build a career in the field of data science, you can explore Jigsaw Academy’s data science course in detail and evaluate its benefits.