DATA HANDLING

[Audio] DATA HANDLING. DATA HANDLING. DATA HANDLING Oata.

[Audio] Data Handling Definition refers to the collection, management, processing, and analysis of information from various sources for purposes such as decision-making, research, or problem-solving. refers to the process of managing, storing, manipulating, and accessing data in various formats..

[Audio] The importance of proper data handling Reliable information for informed decisions Streamlined processes to save time and resources Adherence to standards and protection against breaches.

[Audio] Types of data handling techniques Data cleaning Data transformation Data integration Data reduction Data visualization Data encryption Data archiving.

[Audio] Data Cleaning This involves identifying and correcting errors or inconsistencies in the collected data, such as removing duplicate entries, filling in missing values, and standardizing formats. It ensures that the data is accurate and reliable for analysis. Data cleaning tutorial in pandas.

[Audio] Example of Data Cleaning An example of data cleaning is removing duplicate entries from a dataset. This process involves identifying and eliminating any duplicate records that may exist in the dataset. Duplicate records can occur due to errors or inaccuracies in data entry or data merging processes. By removing duplicate entries, you ensure that each record in the dataset is unique and reliable. This helps prevent errors and inconsistencies in data analysis and reporting..

[Audio] Data Transformation This technique involves converting the collected data into a format that is suitable for analysis. It may include aggregating or summarizing data, normalizing variables, or transforming data into a different scale. Data transformation in python.

[Audio] example of data transformation is converting data from one format to another. This could involve changing the data type of a variable, such as converting a string to a numerical value. For instance, if you have a dataset with a column of dates stored as strings, you may need to transform this data by converting the dates into a date-time format that can be used for analysis or calculations..

[Audio] Data Integration involves combining data from multiple sources into a single, unified data set. It ensures that all relevant data is available for analysis and decision-making..

[Audio] example of data integration is combining data from multiple sources into a single, unified dataset. For example, imagine you have a company that operates multiple stores and has separate databases for each store. Each database contains information such as sales data, customer data, and inventory data. To get a comprehensive view of the company's performance, you would need to integrate the data from all the databases into a single dataset..

[Audio] Data Reduction Data reduction techniques aim to reduce the complexity of the data set while preserving its integrity and information content. This may involve sampling, dimensionality reduction, or feature selection to focus on the most relevant variables..

[Audio] One example of data reduction is summarizing a large dataset into key statistics or aggregates. For instance, instead of analyzing individual sales transactions, a company might summarize the total sales revenue for each month or year. This reduces the amount of data to be analyzed while still providing an overview of the sales performance over time..

[Audio] Data Visualization Data visualization techniques involve representing data graphically to facilitate understanding and interpretation. Graphical representations such as charts, graphs, and maps help identify patterns, trends, and relationships in the data..

[Audio] One example of data visualization is creating a bar chart to display the sales performance of different products. The x-axis represents the products, and the y-axis represents the sales revenue. Each product is represented by a bar whose height corresponds to the sales revenue. This visualization allows viewers to quickly compare the sales performance of different products and identify any patterns or trends..

[Audio] Data Encryption Data encryption techniques involve encoding data to prevent unauthorized access or interception. It is particularly important for handling sensitive data and ensuring compliance with data protection regulations..

[Audio] One example of data encryption is using the Advanced Encryption Standard (AES) algorithm to encrypt sensitive information such as personal data or financial transactions. AES is a symmetric encryption algorithm that uses a secret key to encrypt and decrypt data. The data is transformed into ciphertext, which is a scrambled version of the original data, making it unreadable to anyone who does not have the secret key. This ensures that even if the encrypted data is intercepted or accessed without authorization, it cannot be understood or used without the correct decryption key..

[Audio] Data Archiving Data archiving involves storing data for long-term retention and future reference. It ensures that historical data is preserved and accessible for analysis and decision-making purposes..

[Audio] One example of data archiving is when an organization stores old or inactive data in a secure and separate storage system. This is done to free up space on primary storage systems and maintain long-term access to historical or reference data..

[Audio] For instance, a company may archive customer data that is no longer actively used but still needs to be retained for regulatory or legal purposes. The archived data is typically compressed and stored on tape drives, cloud storage, or dedicated archival servers. It is organized and indexed to facilitate easy retrieval when needed. This approach helps optimize storage resources, improve system performance, and ensure data integrity and accessibility for future reference or compliance requirements..

[Audio] Best practices for efficient data handling Establish Establish clear data handling policies: Ensure Ensure data quality Use Use relevant data sources Optimize Optimize data representation Balance Balance quantitative and qualitative data Secure Secure sensitive data Support Support machine learning models.

[Audio] Summary Data handling involves collecting, organizing, analyzing, and interpreting data to make informed decisions and draw meaningful insights. The goal of data handling is to extract useful information from raw data and convert it into a format that can be easily understood and utilized. Proper data handling is crucial for businesses and organizations to make informed decisions, identify patterns and trends, and improve operations and outcomes..

[Audio] References https://mailchimp.com/resources/data-handling/#:~:text=Data%20handling%20refers%20to%20the,accessing%20data%20in%20various%20formats..