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Data Wrangling: Uncover the Power of Refining Raw Data for Informed Insights

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Data wrangling involves preparing data so that it can be analysed. This process is an essential step in Data Science, and requires specific skills and tools. Find out everything you need to know!

Today’s businesses collect a great deal of data, particularly on the web. By using this data to make strategic decisions, they can gain a major competitive advantage.

However, if the data is incorrect, there is a risk that decisions will be wrong. Before thinking about analysing the data or creating visualisations, it is essential to transform the raw information.

It needs to be converted to the right format, cleaned up and structured so that it can be used. The process encompassing these stages is known as data wrangling.

What is Data Wrangling?

Data wrangling is the process of transforming data. It is an essential step in Data Science, preceding analysis or machine learning tasks.

This method can involve a wide variety of tasks, including data collection, exploratory analysis, data cleansing, structure creation and storage.

In total, data wrangling can take up 80% of the time of a data analyst or data scientist. This is because the process is iterative and has no clearly defined stages.

The tasks involved depend on a number of factors, such as the data sources, their quality, the organisation’s data architecture and the intended use cases.

Why is this so important?

Data wrangling is simply crucial, because it’s the only way to make raw data usable. The information extracted from the data during this process can be extremely valuable.

On the contrary, skipping this stage can result in poor data models that risk having a negative impact on decision-making and the organisation’s reputation.

The data used in a company often comes from different departments. It may be stored on different computers, and spread across different spreadsheets.

This can lead to duplicate, incorrect or untraceable data. It is preferable to centralise data so that it can be used optimally.

It is therefore a very important methodology. However, due to a lack of understanding, data wrangling is very often neglected within companies. Decision-makers generally prefer quick results, and formatting data can be time-consuming…

Good data wrangling involves assembling raw data and understanding its context. This is what allows the data to be interpreted, cleansed and transformed into valuable information.

Data Wrangling vs Data Cleaning

The terms “Data Wrangling” and “Data Cleaning” are often mistakenly confused and used interchangeably. This is because both techniques convert data into a usable format.

However, there are important differences between the two.

Data wrangling refers to the process of collecting raw data, cleaning it up, mapping it and storing it in a useful format.

In fact, data cleaning is only one aspect of data wrangling. This process consists of cleaning up a dataset by removing unwanted, duplicate or incorrect elements, correcting structural errors and other typos, and standardising units of measurement.

In general, Data Cleaning follows more precise steps than Data Wrangling. However, the order of these steps may vary.

The stages of Data Wrangling

The various data wrangling tasks depend on the transformation to be carried out on the dataset. For example, if the data is already in a database, the structuring steps are no longer essential.

The first step is generally data extraction. Logically, it is impossible to transform data without first collecting it.

This step requires planning, to decide what data is needed and where to collect it. The data is then extracted from its source in a raw format.

Data is generally collected in an unstructured format. This means that it has no existing model and is totally disorganised. It is therefore necessary to structure the dataset, in particular by extracting relevant information. For example, parsing HTML code from a website involves extracting only the elements required.

Exploratory analysis (EDA) then consists of determining the structure of a dataset and summarising its main characteristics. This task can be carried out directly after extraction, or later in the process. It all depends on the state of the dataset and the work required. The aim is to familiarise yourself with the data so that you know how to proceed afterwards.

Once the dataset has been structured and explored, you can start applying algorithms to clean it.

The Python and R languages can be used to automate many algorithmic tasks. The aim may be to identify erroneous or duplicate data, or to standardise measurement systems.

The data can then be enriched. This involves combining the dataset with data from other sources.

This may be internal systems or third-party data, for example. The aim is to accumulate more data points to increase the accuracy of the analysis, or simply to fill in missing information.

The data validation stage checks the consistency, quality and accuracy of the data.

This task can be carried out using pre-programmed scripts, capable of comparing data attributes with defined rules. In the event of a problem, this stage must be repeated several times.

The final stage in data wrangling is data publication. The aim is to make the data accessible by depositing it in a new database or other storage system.

End users such as Data Analysts, Data Engineers and Data Scientists can finally access the data. They can exploit the data to create reports or visualisations, and discover relevant information that can be used to make strategic decisions!

The benefits of Data Wrangling

Data wrangling offers a number of advantages. First and foremost, it enables even the most complex data to be analysed quickly, simply and efficiently.

The process transforms raw, unstructured data into usable data, neatly arranged in rows and columns. The data can also be enriched to make it even more useful.

After Wrangling, analysts can process massive volumes of data and share their work with ease. Combining multiple sources of data also enables a better understanding of the audience, and therefore better targeting of advertising campaigns.

What are the Data Wrangling tools?

Data wrangling uses the same tools as data cleaning. These include programming languages such as Python and R, software such as Microsoft Excel, and open source data analysis platforms such as KNIME.

This is one of the reasons why mastery of Python is essential for data analysts. This language allows you to write scripts for very specific tasks.

There are also various tools specially dedicated to data wrangling, enabling non-programmers to carry out this process. These include OpenRefine. However, intuitive visual tools are often less flexible. They are less effective on large, unstructured datasets.

How do you master Data Wrangling?

Data wrangling is an essential stage in the data analysis process. Before data can be analysed, it has to be converted into a usable format.

To become an expert in Data Wrangling, you can turn to DataScientest.

Our various Data Analyst, Data Engineer and Data Scientist courses enable you to learn how to handle the Python language, data extraction, web scraping, data cleaning and text mining.

All our courses can be taken entirely remotely via the web, in BootCamp or Continuing Education. Our innovative Blended Learning approach combines asynchronous learning on a coached online platform and Masterclasses.

Our courses lead to a certificate issued by Mines ParisTech PSL Executive Education, validation of block 3 of the state-recognised RNCP 36129 “Artificial Intelligence Project Manager” certification, and Microsoft Azure or Amazon Web Services cloud certification.

As far as funding is concerned, our organisation is eligible for the Compte Personnel de Formation (Personal Training Account) in France or the Bildungsgutschein in Germany  and many others. Don’t wait any longer, and discover DataScientest to become an expert in Data Wrangling and data analysis!

Now you know all about Data Wrangling. For more information on the same subject, see our dossier on Data Cleaning and our dossier on the Python language.


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