Advanced Data Preparation
Josip Novak
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About this service
Summary
Process
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
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