What's your strategy for cleaning data?

Sometimes understanding the data may not be straightforward. That’s when you need to understand the data model to figure out the fact table and the corresponding dimension tables. This will help understand the relationship between various tables/data sources.

In my experience, investing time in deeply understanding the data context is pivotal. It not only helps in the cleaning process but also informs the choice of models and methodologies later. Engaging with domain experts during this phase can provide valuable insights into the subtleties and potential pitfalls of the data you're handling.

It's highly recommended to analyze the data for odd patterns as described, but in addition I suggest starting with improving your understanding: what is the data at hand? Which systems generated it? Are these systems configured properly? Is the data processed and formatted or is the data raw? Are there biases in the data? For example: why are blank fields present? Is that grounds to drop these observations or are there better approaches?

"What am I looking for?"" What do I need this for?" "Who's my target audience?" "How do I get there?" Essentially, these serve as guides when analyzing data for usefulness. Only then will cleaning it up be easier and result in less errors

Domain knowledge is very crucial in understanding your data. What field/sector is your data coming from? What are the ranges of acceptable data in that field? Is zero (or NULL) an acceptable value? If you don't have sufficient domain knowledge, make sure to run your questions through a domain expert. This helps you identify and understand outliers and anomalies better.

Sometimes a pivot table or even conditional formatting in Excel to find out the duplicate and null values can be a good starting point.

Be transparent about transformations you apply, specially if the resulting data will be used by others. In such cases, preserving the raw inputs might be interesting, giving the end user user the opportunity to explore it on its own and to validate your modifications.

Eliminating null values and duplications is a great step ... but it must be done with a level of focus. You must first get a summery of the data to gain prospective.

Addressing errors and inconsistencies within data is pivotal for elevating its reliability. Unveiling typos, omissions, duplications, or inaccuracies is a crucial step. Employ validation rules, regex, and fuzzy matching to uncover and rectify such issues. Caution is paramount to prevent inadvertent errors or skewed outcomes during correction, ensuring data integrity remains intact.

Cross reference is the major thing. Proper spelling and punctuation come later. Up to date and true Data is the mission. Surf between data sets; don’t ignore your own comin sense either.

Normalisation and standardisation are fundamental but must be approached with the model's needs in mind. For instance, normalizing features that are meant to be outliers can dilute critical information. I often emphasise teaching students to question why and how to apply these techniques, rather than accepting them as universal pre-processing steps.

In my experience, on a machine learning project for government using Python for logistic regression, I imported the following library: from sklearn.preprocessing import StandardScaler This allows to standardize the units for numerical variables for a large dataset with up to 50 features. Normalizing the data is important for data pre-processing to ensure that the numerical features are scaled to a common range of units for a classification problem that you are trying to predict an outcome when the dependent variable could include multiple classes or categories e.g. customers If data is not normalized, it could impact the performance, especially if you are comparing against other three other machine learning models e.g. tree models.

Normalizing and standardizing data harmonizes its diverse origins and structures, enhancing comparability and reliability. Normalization scales data into a unified range, promoting equitable assessment. Meanwhile, standardization centers data around a common mean and standard deviation, curbing outlier influence. These techniques mitigate disparities, elevate algorithmic performance, and fortify data's analytical potency.

Data reshaping and restructuring is the creative process that enables you to craft a compelling narrative from a sea of information, much like an author crafting a novel from individual words and sentences. It gives you a new perspective which can be incredibly powerful for analysis and reveal a new angle or story. It empowers you to transform data from mere numbers and facts into a story that enlightens and informs.

The structure of data plays a pivotal role in driving insights and making sure end users can drill down and explore those insights. Taking the time to reshape and restructure ensures that the data aligns with their analysis objectives. A well-organized dataset not only simplifies analysis but also makes it easy to reveal patterns and insights that might have been overlooked otherwise. What do you think?

An area where I have found reshaping and restructuring large datasets beneficial is to optimise their storage and loading times into a working set, and consequently costs which can often go overlooked, compared to making the data more data scientist friendly

Reshaping and restructuring data empowers adaptability to diverse analytical demands. Whether navigating wide or long data, hierarchical complexities, or relational intricacies, strategic reshaping enhances interpretability and manageability. Employ techniques like pivoting, melting, merging, or splitting to tailor data to specific objectives, fostering readability and alignment with analytical goals.

A particularly useful and simple structure when working with panel data is the creation of lead and lag columns. It allows the observation of transition between attributes values.

In my experience, a good approach for beginners is to use notebooks (such as Jupyter, Rmarkdown, Google Collab). This allows for nicely formatted interpretations next to your code. Once this is working, start moving code logic to functions, and then start thinking towards the ultimate solution (e.g. a pipeline or workflow).

Documenting steps to do data cleaning is one the most ignored steps, but has equal importance for success and thus pave a step to do automation. Automation needs good documentation that's its very important first document your steps.

Documenting and automating data cleaning transforms it into a dynamic, repeatable practice. Recognizing its iterative nature, we streamline efficiency and reproducibility. Employ scripts, functions, pipelines, and workflows to orchestrate data-cleaning procedures. Annotations, logs, and metadata capture and elucidate actions, yielding insight into decisions and facilitating future refinements.

Testing and validation should mimic the real-world scenarios where the data will be applied. This requires a multifaceted approach, combining statistical methods with qualitative insights. Collaborating with the data stakeholders for validation has often uncovered hidden biases or errors in my work, reinforcing the need for a diverse perspective in the evaluation process.

I firmly believe that testing the data in the context of the business problem is of utmost significance. By aligning the data with the intricacies of the problem I'm trying to address, I can ensure that my analysis is not only accurate but also relevant and actionable. To put it simply, my approach involves meticulous data cleaning, coupled with rigorous testing and validation, all grounded in a deep understanding of the business problem. This way, I can confidently derive meaningful insights and make well-informed decisions based on the most accurate representation of the data.

Testing and validating data cleaning substantiates its impact and relevance. Viewing it as a means, not an end, we employ descriptive statistics, visualizations, and hypothesis testing to assess pre- and post-cleaning data states. Feedback, peer review, and quality control substantiate, refine, and enhance the data cleaning process, aligning outcomes with objectives.

1.Prioritize Security and Privacy: Be mindful of the legal and ethical considerations, especially if the data includes personal or sensitive information. Following privacy laws and ethical guidelines is crucial. 2.Collaborate with Experts: If the data relates to a specific field or subject, consulting with domain experts can help ensure that the cleaning process aligns with industry standards and best practices. 3. Keep an Eye on Outliers: Outliers are values that stand out from the rest. Sometimes they're errors, but sometimes they're important pieces of information. Decide whether to keep, adjust, or remove them.

1. Ensure key data statistics are identified like the data types (primarily numeric / categorical), missing values, outliers, feature importance etc. 2. Perform data transformation specific to the application / end goal, eg: - forecasting on time-series data might require creation of time-dummies - an NLP solution might require lemmatizing certain similar words & removing other stop words, punctuations etc. 3. Certain features which may seem irrelevant for a particular use-case might become crucial while solving another use-case. Hence always need to think from an end-goal perspective while doing feature selection 4. Sometimes looking beyond the scope of the original data by inculcating some important exogenous features may help as well

Don't overlook the ethical considerations in data cleaning, especially in handling missing or sensitive information. Always question the assumptions behind the cleaning decisions, as they might introduce biases. Engaging in continuous learning and staying abreast of best practices is essential, as the field of data preparation is dynamic and ever-evolving.

Consider the use of the data, in other words when creating a cleaning processing pipeline take into account what the data will be used for down the line. The processing done for creating a report may not be suitable for cases where the data is used to train a machine learning model. What is clean data for some cases, may be dirty in others.

Often more than 80% of the work goes into cleaning and preparing your data. The final analysis is then often the easiest part - so don't underestimate the importance of cleaning your data!