Advanced Data PreparationJosip Novak
The service involves high-level data preparation for both industry and academic datasets. Not only does it include rigorous cleaning that may also include data formatting, reshaping, and other typical data preparation steps, but it also includes the most advanced techniques for handling missing values and outliers 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.
What's included
Report (.html, .docx, etc.)
A comprehensive, structured report detailing the entire data preparation process. It includes:
1. Problem Definition & Objectives – A clear statement of the purpose and objectives of the data preparation process. This section will outline the specific goals of preparing the dataset (e.g., ensuring quality, consistency, or readiness for a specific purpose) and any key challenges addressed during the process.
2. Data Quality Assessment – A thorough evaluation of the raw dataset, including an assessment of completeness, consistency, and accuracy. This section will identify any potential issues such as entry errors, missing values, duplicates, or data inconsistencies.
3. Data Cleaning – Detailed description of the cleaning process applied to the dataset. This will include actions taken to handle missing values (e.g., imputation methods), removal or correction of duplicates, and the handling of inconsistencies or errors in the data.
4. Outlier Detection & Treatment – Explanation of how outliers were identified and treated. This section will describe the techniques used for detecting outliers (e.g., z-scores, IQR method) and the strategy used to handle them (e.g., removal, transformation, imputation).
5. Data Transformation & Normalization – Overview of any transformations or normalization steps applied to the dataset to ensure consistency and compatibility with analysis methods. This could include scaling features, encoding categorical variables, or applying exponential transformations to skewed variables.
6. Feature Engineering – If applicable, a description of any new features or variables created to improve the dataset’s usability for analysis. This section will also outline the rationale behind the creation of new features (e.g., aggregating variables, creating interaction terms, or extracting key patterns).
7. Data Structuring & Format – Details on how the dataset was structured for easy use and further analysis. This will include the organization of variables, standardization of data formats (e.g., date/time, categorical labels), and ensuring that the dataset is ready for visualization, statistical analysis, or machine learning models.
8. Decision Justification – Throughout the report, every decision made during the preparation process will be justified based on best practices in data science and statistics. This section will provide reasoning for the selected techniques to ensure that the data is prepared for the next steps.
The Prepared Dataset (.csv, .xlsx, etc.)
If required, a cleaned and pre-processed version of the dataset will be delivered alongside the report. The dataset will be provided in the agreed-upon format (e.g., .csv, .xlsx).
FAQs
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.
Yes, this service includes the preparation, integration, and joining of data from multiple sources. Whether it’s merging data from different databases, combining datasets from external APIs, or joining cross-sectional and longitudinal data, I ensure that the datasets are aligned, cleaned, and prepared for further analysis.
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.
Not necessarily. If you already have relevant data, that’s great! However, if you don’t, I can help identify relevant data sources or suggest ways to collect the necessary information.
Yes! If you don’t have the necessary data, I can assist in various ways, including:
* Web Scraping – Collecting publicly available time series data while ensuring compliance with legal and ethical guidelines.
* API Integration – Extracting data from online services, financial markets, social media, or other platforms via APIs.
* Public Databases – Identifying and utilizing open datasets from government sources, research institutions, and industry reports.
* Custom Data Pipelines – Setting up automated processes to continuously collect and structure incoming data.
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 project. It will not be shared with any third parties and will be deleted upon completion of the task.
I primarily use R (my primary tool) and Python for data mining. These languages offer powerful libraries and frameworks that include a variety of techniques for data preparation.
Missing Values:
Predictive Modeling: Using algorithms to predict missing data based on other features.
Single Imputation: Regression Imputation (predicting missing value using regression), k-Nearest Neighbors (k-NN) (replacing missing values with similar observations), Last Observation Carried Forward (LOCF) (using the most recent value for time-series data), Hot Deck Imputation (replacing missing values with observed values from similar cases).
Multiple Imputation: A technique that creates several imputed datasets and combines the results for a more robust estimate.
Robust Algorithms: Methods like k-Nearest Neighbors (k-NN) and Expectation-Maximization (EM) can handle missing values effectively.
Outliers:
Clustering Algorithms: Techniques such as k-means and DBSCAN can help identify and handle outliers based on data grouping.
Machine Learning: Methods like isolation forests, random forests, and one-class Support Vector Machines (SVMs) are effective for detecting outliers.
Transformation Methods: Techniques like Box-Cox and Yeo-Johnson can transform data to reduce the impact of outliers.
The timeline for the project depends on factors such as the data quality, the complexity of the predictive modeling task, and the methods required. Generally, it can take anywhere from a week to a month. A more complex modeling with additional model tuning may take longer.
Example work
Contact for pricing
Tags
Jupyter
Python
R
RStudio
Data Analyst
Data Scientist
Statistician
Service provided by
Josip Novak Vukovar, Croatia
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- Followers
Advanced Data PreparationJosip Novak
Contact for pricing
Tags
Jupyter
Python
R
RStudio
Data Analyst
Data Scientist
Statistician
The service involves high-level data preparation for both industry and academic datasets. Not only does it include rigorous cleaning that may also include data formatting, reshaping, and other typical data preparation steps, but it also includes the most advanced techniques for handling missing values and outliers 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.
What's included
Report (.html, .docx, etc.)
A comprehensive, structured report detailing the entire data preparation process. It includes:
1. Problem Definition & Objectives – A clear statement of the purpose and objectives of the data preparation process. This section will outline the specific goals of preparing the dataset (e.g., ensuring quality, consistency, or readiness for a specific purpose) and any key challenges addressed during the process.
2. Data Quality Assessment – A thorough evaluation of the raw dataset, including an assessment of completeness, consistency, and accuracy. This section will identify any potential issues such as entry errors, missing values, duplicates, or data inconsistencies.
3. Data Cleaning – Detailed description of the cleaning process applied to the dataset. This will include actions taken to handle missing values (e.g., imputation methods), removal or correction of duplicates, and the handling of inconsistencies or errors in the data.
4. Outlier Detection & Treatment – Explanation of how outliers were identified and treated. This section will describe the techniques used for detecting outliers (e.g., z-scores, IQR method) and the strategy used to handle them (e.g., removal, transformation, imputation).
5. Data Transformation & Normalization – Overview of any transformations or normalization steps applied to the dataset to ensure consistency and compatibility with analysis methods. This could include scaling features, encoding categorical variables, or applying exponential transformations to skewed variables.
6. Feature Engineering – If applicable, a description of any new features or variables created to improve the dataset’s usability for analysis. This section will also outline the rationale behind the creation of new features (e.g., aggregating variables, creating interaction terms, or extracting key patterns).
7. Data Structuring & Format – Details on how the dataset was structured for easy use and further analysis. This will include the organization of variables, standardization of data formats (e.g., date/time, categorical labels), and ensuring that the dataset is ready for visualization, statistical analysis, or machine learning models.
8. Decision Justification – Throughout the report, every decision made during the preparation process will be justified based on best practices in data science and statistics. This section will provide reasoning for the selected techniques to ensure that the data is prepared for the next steps.
The Prepared Dataset (.csv, .xlsx, etc.)
If required, a cleaned and pre-processed version of the dataset will be delivered alongside the report. The dataset will be provided in the agreed-upon format (e.g., .csv, .xlsx).
FAQs
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.
Yes, this service includes the preparation, integration, and joining of data from multiple sources. Whether it’s merging data from different databases, combining datasets from external APIs, or joining cross-sectional and longitudinal data, I ensure that the datasets are aligned, cleaned, and prepared for further analysis.
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.
Not necessarily. If you already have relevant data, that’s great! However, if you don’t, I can help identify relevant data sources or suggest ways to collect the necessary information.
Yes! If you don’t have the necessary data, I can assist in various ways, including:
* Web Scraping – Collecting publicly available time series data while ensuring compliance with legal and ethical guidelines.
* API Integration – Extracting data from online services, financial markets, social media, or other platforms via APIs.
* Public Databases – Identifying and utilizing open datasets from government sources, research institutions, and industry reports.
* Custom Data Pipelines – Setting up automated processes to continuously collect and structure incoming data.
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 project. It will not be shared with any third parties and will be deleted upon completion of the task.
I primarily use R (my primary tool) and Python for data mining. These languages offer powerful libraries and frameworks that include a variety of techniques for data preparation.
Missing Values:
Predictive Modeling: Using algorithms to predict missing data based on other features.
Single Imputation: Regression Imputation (predicting missing value using regression), k-Nearest Neighbors (k-NN) (replacing missing values with similar observations), Last Observation Carried Forward (LOCF) (using the most recent value for time-series data), Hot Deck Imputation (replacing missing values with observed values from similar cases).
Multiple Imputation: A technique that creates several imputed datasets and combines the results for a more robust estimate.
Robust Algorithms: Methods like k-Nearest Neighbors (k-NN) and Expectation-Maximization (EM) can handle missing values effectively.
Outliers:
Clustering Algorithms: Techniques such as k-means and DBSCAN can help identify and handle outliers based on data grouping.
Machine Learning: Methods like isolation forests, random forests, and one-class Support Vector Machines (SVMs) are effective for detecting outliers.
Transformation Methods: Techniques like Box-Cox and Yeo-Johnson can transform data to reduce the impact of outliers.
The timeline for the project depends on factors such as the data quality, the complexity of the predictive modeling task, and the methods required. Generally, it can take anywhere from a week to a month. A more complex modeling with additional model tuning may take longer.
Example work
Contact for pricing