Advanced Data Preparation

Contact for pricing

About this service

Summary

The service involves high-level data preparation for both industry and academic datasets. Not only does it include rigorous cleaning that may also include the most advanced techniques for handling missing values and outliers but it also includes data formatting, reshaping, variable which are typically beyond the capabilities of most analysts and researchers. Clients can expect meticulously prepared data, ready for statistical analysis, visualization, or modeling with machine learning algorithms.

Process

Data Cleaning: (e.g., duplicate removal, missing values and outlier treatment)
Data Formatting (e.g., converting data types, string operations, date operations, filtering, sorting, aggregation, discretization)
Reshaping (long to wide, wide to long; if applicable)
Merging sources (joining multiple tables; if applicable)
Variable transformation (e.g., normalization, standardization, Box-Cox, Johnson, etc.; if applicable)
Feature engineering (aggregation, binning, encoding, dimension reduction, etc.; if applicable)
Report Creation: comprehensive presentation (for replicability), recommendations (providing suggestions about how the dataset can be used; if applicable), documentation (documenting methods, assumptions, and decisions made during the preparation).

FAQs

  • Is this service limited to particular fields or industries?

    No, this service is not limited to any particular field or industry. It is adaptable to a wide range of sectors, including healthcare, finance, marketing, technology, social sciences, education, retail, manufacturing, telecommunications, government, and more, ensuring that the approach can be tailored to meet the unique needs of any domain.

  • Does this service involve both cross-sectional and longitudinal datasets?

    Yes, this involves both cross-sectional and longitudinal datasets. Cross-sectional data represents a snapshot of relationships at a given time, Longitudinal data represents time-based trends, repeated measures, or panel data structures.

  • What do you mean by most advanced techniques for handling missing values and outliers?

    The most advanced techniques for handling missing values include predictive modeling methods, multiple imputation, and robust algorithms such as k-Nearest Neighbors (k-NN) and Expectation-Maximization (EM). Additionally, machine learning approaches, including decision trees, random forests, and neural networks, can be employed for imputation in complex scenarios. For managing outliers, advanced methods include clustering algorithms like k-means and DBSCAN, machine learning techniques such as isolation forests, random forests, and one-class Support Vector Machines (SVMs), as well as transformation methods like Box-Cox and Yeo-Johnson.

  • What if my project requirements do not exactly match the offered service?

    I am flexible, so feel welcome to message me and we can discuss the specific requirements of your project.

  • Do I need to provide my own data?

    Yes, I can assist with data collection. This includes providing suggestions and feedback on methods such as survey design, statistical power analysis, and overall research design to ensure that your data collection is effective and appropriate for your needs.

  • How will my data be handled in terms of confidentiality and data security?

    I am committed to data ethics and understand the importance of protecting sensitive information. Your data will be used solely for the purpose of completing your requested analysis. It will not be shared with any third parties and will be deleted upon completion of the task.

  • Which tools do you use for the preparation?

    R is my primary tool due to its comprehensive coverage of various techniques and versatility. However, I am also proficient in other software and can adapt to use the most suitable tool if specific techniques are better supported elsewhere or if you prefer the analysis to be done using other tools.

What's included

  • Report (.html, .docx, etc.)

    The data preparation procedure will be delivered as a comprehensive report. This report will detail every step of the process, from cleaning to handling missing values and outliers. Throughout the report, every decision made during this process will be justified based on best practices in statistics.

  • The Code (RNotebook, Jupyter)

    If needed, the code that was used for the analysis can be delivered along with the report.

  • The Prepared Dataset (.csv, .xlsx, etc.)

    The prepared form of the dataset will be delivered along with the report.

Example projects


Skills and tools

Data Modelling Analyst
Data Scientist
Data Analyst
Data Analysis
Microsoft Excel
pandas
Python
R

Work with me