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How do you ensure accurate data sampling and partitioning for Data Analytics?

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Data sampling and partitioning are essential steps in data analytics, as they help reduce the complexity and size of the data, improve the performance and accuracy of the models, and enable cross-validation and testing. However, if done incorrectly, they can also introduce bias, noise, and overfitting, which can compromise the quality and reliability of the results. In this article, you will learn how to ensure accurate data sampling and partitioning for data analytics, by following some best practices and using some common techniques and tools.

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David Heraty

Senior Commercial Manager | Oracle Advisory | Oracle Cloud Solutions Partner
Kavindu Rathnasiri

Top Voice in Machine Learning | Data Science and AI Enthusiast | Associate Data Analyst at ADA - Asia | Google…
Saugata Deb

Data Scientist @ DPDzero | Ex - AlmaBetter

1 Define the objectives and criteria

Before you start sampling and partitioning your data, you need to have a clear idea of what you want to achieve and how you will measure it. For example, you may want to explore the data, test a hypothesis, build a predictive model, or compare different algorithms. Depending on your objectives, you will need to define the criteria for selecting, splitting, and evaluating your data, such as the sample size, the sampling method, the partition ratio, the validation technique, and the performance metrics.

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David Heraty

Senior Commercial Manager | Oracle Advisory | Oracle Cloud Solutions Partner
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By having a deeper understanding of the underlying structure and variability of the dataset, you can make more informed decisions about the sampling and partitioning strategies that will best suit your objectives. EDA techniques such as visualizations, summary statistics, and correlation analysis can help you identify potential biases, imbalances, or anomalies that may impact the accuracy and reliability of your analysis.

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Kavindu Rathnasiri

Top Voice in Machine Learning | Data Science and AI Enthusiast | Associate Data Analyst at ADA - Asia | Google Certified Data Analyst | Experienced Power BI Developer
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Ensuring accurate data sampling and partitioning is crucial for robust data analytics. From my experience, it's essential to understand the underlying data distribution and characteristics before selecting the sampling and partitioning methods. Additionally, regularly validating the representativeness of the samples and partitions through exploratory data analysis and cross-validation techniques helps maintain accuracy and reliability in analysis results. It's about striking the right balance between sample size, randomness, and stratification to capture the true essence of the data and make informed decisions.

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Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
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Antes de muestrear y particionar datos, es crucial definir tus objetivos y criterios de evaluación. ?Quieres explorar los datos, probar hipótesis, crear modelos predictivos o comparar algoritmos? Basándote en tus objetivos, establece criterios para seleccionar, dividir y evaluar datos, como tama?o de muestra, método de muestreo, proporción de partición, técnica de validación y métricas de rendimiento. Este enfoque te permite dirigir tus esfuerzos de manera efectiva y evaluar el éxito de tu análisis de datos de manera precisa.

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Youssef Fathy

Google Certified | Analytics Lead at Zumra Group | X Swvl | Uber Alumni
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Accurate data sampling and partitioning for Data Analytics begin with a clear definition of objectives and criteria. Understand the specific goals of the analysis, whether it's identifying patterns, making predictions, or deriving insights. Define the criteria for success, such as accuracy, precision, or computational efficiency.

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2 Choose an appropriate sampling method

There are many ways to sample data, but not all of them are suitable for every situation. You need to choose a sampling method that matches the characteristics and distribution of your data, as well as your objectives and criteria. For example, if your data is large and representative, you can use simple random sampling, which selects a subset of data randomly and uniformly. If your data is small or imbalanced, you may need to use stratified sampling, which ensures that the subset of data reflects the proportions of different groups or classes in the population. Other sampling methods include cluster sampling, systematic sampling, and resampling.

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Saugata Deb

Data Scientist @ DPDzero | Ex - AlmaBetter
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hoosing the right sampling method depends on your study goals and data characteristics. For simple, generalizable insights, use random sampling. For specific subgroups, stratified sampling ensures representation. If data is geographically dispersed, cluster sampling might be efficient. Convenience sampling is quick but less reliable, while systematic sampling provides a fixed interval approach. Match your method to your research needs for validity and efficiency.

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Youssef Fathy

Google Certified | Analytics Lead at Zumra Group | X Swvl | Uber Alumni
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Selecting the right sampling method is crucial for obtaining representative data. Depending on the nature of the dataset and analysis goals, methods like random sampling, stratified sampling, or systematic sampling may be employed. Consider factors such as data distribution, size, and potential biases when making this choice.

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3 Apply consistent and reproducible partitioning

Once you have sampled your data, you need to partition it into different sets for training, validation, and testing. This is important to avoid overfitting, which occurs when the model learns too much from the training data and fails to generalize to new data. You need to apply consistent and reproducible partitioning, which means that you use the same criteria and method to split your data every time, and that you document and save your partitioning process and results. For example, you can use a fixed seed for random partitioning, or a predefined index for deterministic partitioning.

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Youssef Fathy

Google Certified | Analytics Lead at Zumra Group | X Swvl | Uber Alumni
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Consistency and reproducibility are essential for reliable results. Establish a robust partitioning strategy to divide the dataset into training, validation, and test sets consistently. Ensure that the partitioning process is deterministic, allowing others to reproduce the results and facilitating the comparison of different models or algorithms

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4 Use appropriate tools and libraries

Data sampling and partitioning can be tedious and error-prone if done manually. Fortunately, there are many tools and libraries that can help you automate and simplify these tasks. For example, if you are using Python, you can use pandas and numpy for data manipulation and random sampling, scikit-learn for stratified sampling and partitioning, and imbalanced-learn for dealing with imbalanced data. If you are using R, you can use dplyr and tidyr for data manipulation and sampling, caret for partitioning and validation, and ROSE for handling imbalanced data.

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Youssef Fathy

Google Certified | Analytics Lead at Zumra Group | X Swvl | Uber Alumni
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Utilize reliable tools and libraries to implement the sampling and partitioning processes efficiently. Popular programming languages like Python offer libraries such as scikit-learn and TensorFlow that provide built-in functions for sampling and partitioning. Leverage these tools to streamline the implementation and maintain code readability.

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5 Evaluate and compare the results

After you have sampled and partitioned your data, you need to evaluate and compare the results to see if they meet your objectives and criteria. For example, you can use descriptive statistics and visualizations to check the distribution and characteristics of your data subsets, and see if they are representative and balanced. You can also use inferential statistics and hypothesis testing to see if there are any significant differences or relationships between your data subsets, and if they are consistent with your expectations. Finally, you can use performance metrics and learning curves to see how well your models fit and perform on your data subsets, and if there are any signs of overfitting or underfitting.

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Youssef Fathy

Google Certified | Analytics Lead at Zumra Group | X Swvl | Uber Alumni
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After sampling and partitioning, rigorously evaluate the performance of your model or analysis. Utilize metrics relevant to the objectives defined earlier, such as accuracy, precision, recall, or F1 score. Compare the results across different sampling methods or partitioning strategies to identify the most effective approach for your specific use case.

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6 Experiment and iterate

Data sampling and partitioning are not one-time activities, but rather iterative processes that require experimentation and refinement. You may need to try different sampling methods, partition ratios, validation techniques, and performance metrics, and see how they affect your data quality and model accuracy. You may also need to adjust your objectives and criteria, as you learn more about your data and your problem. The key is to keep track of your experiments, document your findings, and use feedback and evidence to improve your data sampling and partitioning strategies.

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Youssef Fathy

Google Certified | Analytics Lead at Zumra Group | X Swvl | Uber Alumni
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Data analytics is an iterative process, and refining the sampling and partitioning strategies may be necessary. Conduct experiments by tweaking parameters, trying alternative methods, or adjusting sample sizes. Analyze the impact on model performance and refine the approach iteratively, aiming for continuous improvement.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you ensure accurate data sampling and partitioning for Data Analytics?

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1 Define the objectives and criteria

2 Choose an appropriate sampling method

3 Apply consistent and reproducible partitioning

4 Use appropriate tools and libraries

5 Evaluate and compare the results

6 Experiment and iterate

7 Here’s what else to consider

Data Analytics

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