One of the most sought-after skills in the modern data science industry is advanced knowledge of artificial intelligence, automation, and machine learning algorithms. A common way to utilize machine learning models is completing tasks that are more rote, mundane, or repetitive. A top use of automation in the realm of data science is for the purpose of data cleaning and organization. Through the mobilization of machine learning models, any Data Scientist can learn how to speed up the initial stages of processing and preparing a dataset with automation and algorithms.
What is Data Cleaning?
Data cleaning (sometimes called data cleansing), is the process of getting data ready for analysis after it has been collected. When working with a dataset, it is common to receive “messy” (not yet cleaned) data. Messy data often contains errors, missing values, or other inconsistencies that would make it difficult to work with.
Cleaning data ensures the data is easy to access, search, and understand. By querying the data, examining descriptive statistics, and even exploratory analysis of a dataset, Data Scientists can begin to determine how best to clean a dataset. They can manually or automatically search through the dataset for errors, then correct them to ensure the analytics stage is not unduly influenced.
Why Use Automation for Data Cleaning and Organization?
There are many reasons you would use automation and machine learning to clean and organize a dataset. When working with larger datasets, the process of data cleaning can become a difficult and time-consuming task. There are studies showing that Data Scientists spend anywhere from 50-80% of their time cleaning and organizing datasets. It’s inefficient to spend that much time completing mundane instead of analyzing and interpreting the data.
Data cleaning does not usually require much human oversight. Training a machine learning model on data cleaning and organization frees up the time spent cleaning data so Data Scientists can work on more complex data analysis. Data Scientists can use automation and machine learning to enable a machine to identify missing values, remove data that doesn’t belong within a dataset, and organize a dataset for ease of accessibility and efficiency.
Identifying and Fixing Missing Values
One issue that can appear within a dataset, either before or after data is collected and stored, is missing values. Missing values in a dataset correspond to numerical data that has not been logged into a dataset or that was never included in the dataset. For data housed within a database management system, missing values are usually identified within a database by writing queries to search for “NULL values.” NULL value is just another name for data that is either missing or unknown within a dataset.
Identifying NULL values is time-consuming because missing values are not always readily apparent to a Data Scientist or a machine learning algorithm. Many data science tools include automated features that identify missing values for the Data Scientist to make this process quicker and easier. Programs like SAS, SPSS, and Stata will all “automatically remove such cases from any analysis”. Removing the missing values ensures statistical models and algorithms (like linear regression) can run.
Editing or Removing Errors
It is common to have values in a dataset that are incorrect or do not belong within the dataset. Since many datasets include information connected or related to other parts of the dataset, editing or removing errors can be difficult. Simple errors within a dataset such as grammatical errors, are commonly repeated throughout a dataset or across entries. This means that editing that error requires changing a multitude of data entries.
Editing or removing errors is a repetitive process that a machine can do through “find and replace” or “search and replace” algorithms. This feature is usually programmed into data science tools. It allows Data Scientists to search a dataset for a specific word and replace that word with something else. In other words, they don’t have to go through the entire dataset updating every entry where the mistake was made.
Want to Learn More About Automation and Machine Learning?
Automation and machine learning make it easier to manage datasets that lack organization or structure. Both automation and machine learning are hot topics within the world of data science, especially when it comes to the process of cleaning and organizing data, as well as testing products and software.
For data science students and professionals interested in learning more about automation and machine learning, Noble Desktop offers relevant data science classes. The Data Science Certificate provides training in using Python and SQL to create machine learning models and to organize databases. This certificate is helpful for anyone that wants to become a Data Scientist or analyst.
The Python Data Science & Machine Learning Bootcamp teaches students and professionals how to use popular Python data science libraries to manage and manipulate a dataset. More advanced students can look to the Python Machine Learning Bootcamp for training on processing data via algorithms and statistical models. Anyone who is interested in using automation and machine learning can look to Noble Desktop’s data science classes and certificate programs, as well as the Python bootcamps.