Imagine constructing a puzzle with mismatched pieces. Sounds impossible, right?
For
data analysts, working with raw, incorrect datasets often feels the same. Data filled with inconsistencies and errors could only lead to faulty analysis, and eventually, disastrous results.
A recent study revealed that organisations lose an average of $15 million annually due to data inaccuracies. Thus,
data cleaning in data analytics is more than important to ensure authentic data-driven business decisions.
So,
what is data cleaning?
Data cleaning, data cleansing, data scrubbing or data wrangling, is the first step to
data analysis. In simple terms, think of it as cleaning or tidying up your data before you start the actual data analysis. In fact, data cleaning takes up to 60% of the overall analysis time. Thus, the cleansing part is always an indispensable chapter in a comprehensive
data science course online.
Data Cleaning is the process of eliminating errors and discrepancies from the raw dataset to make the data fit for accurate analysis. It enhances accuracy, reliability, and clarity by eradicating inconsistencies and refining data quality. These could be things like missing, incomplete, incorrectly formatted, or duplicate data.
Flawed or incomplete data could lead to erroneous analysis if not cleaned on time . The cleansing procedure helps to remove the messy bits in raw data to get a trimmed, tidy picture. By
cleaning data in data analysis, you ensure that the results are credible and accurate.
Data cleaning aims to save as much data as possible while improving reliability. Some of the popular
data cleaning tools are - Microsoft Excel, Python, Ruby, SQL, etc.
Cleaning and sorting data has its own set of benefits. In the next section, you will know about some essential benefits of data cleaning.
The
benefits of data cleaning cannot be overstated. It's like laying a strong foundation for a building – do it right, and you will have a sturdy, lasting building; mess it up, and everything collapses. That's why experts spend 60-80% of their time cleaning data.
Here is a brief on the
importance of data cleaning in data analytics.
Data cleansing or data scrubbing is the backbone of quality analysis. Here is a breakdown of
data cleaning steps from start till the final step.
Data cleansing services are akin to constructing a solid foundation for your insights. It transforms messy, incomplete, duplicate data into a valuable resource for accurate decision-making. By following the steps outlined – from eliminating unwanted observations to validating your dataset – you ensure to land up with clean, tailored data that is ready to help you for data-driven decisions.
As mentioned above, data cleaning is a vital part of the data analytics process. If you are aspiring to build a career in data science, you have to receive training on data cleaning. Do you want to learn
data cleaning in data science? When it comes to choosing a leading
data analytics course in Kolkata or India, DataSpace Academy could be a credible name. Enrol today to unlock the secrets of data and embark on a journey of transforming raw information into valuable insights. Your data-driven future awaits!