Top 4 Best Data-Preparation Practices For Data Scientists, Business Analysts, and Researchers

Data scientists and analysts play a critical role in the field of data by using data to inform decisions and drive action. They perform a wide range of tasks to collect, process, analyze, and interpret data to extract insights and generate meaningful recommendations.

Data analysts are important today because they help organizations make informed decisions based on data. With the increasing amount of data generated by businesses, the need for individuals who can analyze and interpret this data is growing. Data analysts use statistical techniques, machine learning algorithms, and data visualization tools to extract insights from data, and to identify patterns, trends, and relationships. These insights are used to inform decision making, drive business strategies, and improve operations. As a result, data analysts play a crucial role in the success of organizations in today’s data-driven world.

Making mistakes in analyzing data could have serious consequences including misleading insights, bias and discrimination, financial and organizational losses, damaged market reputation, and legal ramifications for the starters. Therefore, following a systematic and rigorous approach to data analysis, and to thoroughly validate the results before using them to inform decision making. Therefore, we organized the data-preparation best-practices for improving the performance of machine learning models and ensuring that the models generalize well to unseen data. In this blog, we will discuss the top 5 best practices for these steps in the context of python programming language.

1. Exploratory Data Analysis (EDA)

The first step in the machine learning process is to understand the data that we are working with. This involves exploring the data, identifying its features and their distributions, and identifying any missing or incorrect values. The goal of exploratory data analysis is to gain an understanding of the data that will inform our further steps in the machine learning process.

In python, the most common libraries for exploring data are Pandas and Matplotlib. Pandas is a powerful library for data manipulation, and it provides data structures and functions for efficiently working with tabular data. Matplotlib is a plotting library that can be used to create visualizations of the data.

A typical EDA process. Source: Ghosh et al. 2018

To perform EDA, we can use the following steps in python:

• Load the data into a Pandas DataFrame
• Use Pandas functions to calculate basic statistics and generate descriptive statistics of the data. This can include calculating the mean, median, and standard deviation of each feature, and generating histograms and box plots to visualize the distribution of the data.
• Use Pandas functions to identify missing or incorrect values in the data. This can include finding any NaN values, which are missing values in the data, and correcting or removing them if necessary.
• Use Matplotlib to generate visualizations of the data, including scatter plots and line plots. This can help us identify patterns and relationships in the data that may not be immediately obvious from the descriptive statistics.

Various charts to aid in exploratory data analysis (EDA). Source: Grosser 2018

2. Data Wrangling

Data wrangling is the process of transforming raw data into a format that is usable for machine learning. This involves cleaning and transforming the data so that it can be used to train machine learning models. The goal of data wrangling is to get the data into a format that is suitable for training machine learning models, and to remove any irrelevant or redundant features from the data.

In python, the most common libraries for data wrangling are Pandas, Numpy, and Scipy. Pandas is a powerful library for data manipulation, and it provides functions for cleaning and transforming data. Numpy is a library for numerical computing in python, and it provides functions for performing mathematical operations on arrays. Scipy is a library for scientific computing in python, and it provides functions for transforming and cleaning data.

To perform data wrangling, we can use the following steps in python:

• Use Pandas functions to clean and transform the data. This can include removing any irrelevant or redundant features, imputing missing values, scaling the data, and converting categorical data into numerical data.
• Use Numpy functions to perform mathematical operations on the data, such as scaling and normalization.
• Use Scipy functions to perform data transformations, such as PCA and dimensionality reduction, to reduce the size of the data and make it more manageable.

Howe to perform data wrangling: Steps in data wrangling

3. Data Cleaning — Optional Iteration

Data cleaning is the process of removing any errors, inconsistencies, or outliers from the data. The goal of data cleaning is to ensure that the data is accurate and consistent, and that it is suitable for training machine learning models.

In python, the most common libraries for data cleaning are Pandas, Numpy, and Scipy. Pandas is a powerful library for data manipulation, and it provides functions for cleaning and transforming data. Numpy is a library for numerical computing in python, and it provides functions for performing mathematical operations on arrays. Scipy is a library for scientific computing in python, and it provides functions for identifying and removing outliers from the data.

To perform data cleaning, we can use the following steps in python:

• Use Pandas functions to identify and remove duplicate or irrelevant data, and to correct any errors in the data.
• Use Numpy functions to detect and remove outliers from the data. This can include using functions like z-score or IQR to identify outliers, and removing them if necessary.
• Use Scipy functions to perform data normalization, which can help to ensure that the data is in a suitable format for training machine learning models.

4. Data Preparation for Training and Validation

Data preparation for training and validation is the process of splitting the data into training and validation sets, and preparing the data for use in machine learning models. The goal of data preparation for training and validation is to ensure that the data is in a suitable format for training machine learning models, and that the models are tested on a representative sample of the data.

In python, the most common libraries for data preparation for training and validation are Pandas, Numpy, and Scikit-learn. Pandas is a powerful library for data manipulation, and it provides functions for splitting data into training and validation sets. Numpy is a library for numerical computing in python, and it provides functions for working with arrays and matrices. Scikit-learn is a machine learning library in python, and it provides functions for preparing data for use in machine learning models.

To prepare the data for training and validation, we can use the following steps in python:

• Use Pandas functions to split the data into training and validation sets. This can include using the train_test_split function from Scikit-learn to split the data into training and validation sets, or using the KFold cross-validation function from Scikit-learn to split the data into multiple folds for cross-validation.
• Use Numpy functions to convert the data into arrays and matrices, which are suitable for use in machine learning models.
• Use Scikit-learn functions to prepare the data for use in machine learning models. This can include scaling the data, converting categorical data into numerical data, and performing feature selection to remove irrelevant or redundant features from the data.

5. Conclusion

In this blog, we discussed the top 5 best practices for data exploration, data wrangling, data cleaning, and data preparation for training and validation in python. By following these best practices, we can ensure that the data is in a suitable format for training machine learning models, and that the models are tested on a representative sample of the data. The libraries discussed in this blog, including Pandas, Numpy, Scipy, and Scikit-learn, provide powerful functions for performing these tasks, and are essential tools for any data scientist working in python.

#DataExploration #DataWrangling #DataCleaning #DataPreparation #Training #Validation #BestPractices #PythonTools #PythonLibraries #Pandas #Matplotlib #Seaborn #Numpy #Sklearn #MissingData #Outliers #DataVisualization #DataScientists #DataAnalysts

Keywords: Data exploration, Data wrangling, Data cleaning, Data preparation, Training, Validation, Best practices, Python tools, Python libraries, Pandas, Matplotlib, Seaborn, Numpy, Sklearn, Missing data, Outliers, Data visualization, Data scientists, Data analysts.

References

[1.] ExpressAnalytics — “What is Data Wrangling? What are the Steps in Data Wrangling?”. 2021. Accessed 2023–01–02.

[2.] Ghosh, Aindrila, Mona Nashaat, James Miller, Shaikh Quader, and Chad Marston. 2018. “A comprehensive review of tools for exploratory analysis of tabular industrial datasets.” Visual Informatics, Elsevier B.V., vol. 2, pp. 235–253. Accessed 2023–01–02.

[3.] Grosser, Zach. 2018. “Accessible Colors for Data Visualization. The Data Viz Project by Ferdio.” The Corner, Square’s Technical Blog, January 11. Accessed 2023–01–02.

ICTN is where businesses meet solutions, be it in big-data analytics and business intelligence, or DevOps and cloud migration, we handle it through our team of leading IT engineers and innovators providing you a world-class consulting support.

ICTN was started as a unique passion, and persistence strive for excellence by visionary folks that have worn the hats of engineers, computer scientists, and IT experts. ICTN is a thought leader and technology expert solving the world’s artificial intelligence, cyber security, machine learning, computer vision, cloud computing, software, and application development problems through cutting-edge innovation and emerging technologies.

Visit us at www.ictn.us.