Chapter 2: Data Acquisition and Cleaning in Data Science

Don't forget to explore our basket section filled with 15000+ objective type questions.

Introduction to Data Acquisition and Cleaning

Data acquisition and cleaning are essential steps in the data science process. They involve gathering data from various sources, ensuring data quality, handling missing values, dealing with outliers, and transforming the data into a usable format. These steps are crucial because the accuracy and reliability of the data directly impact the results and insights derived from the data analysis.

Data Acquisition

Data acquisition refers to the process of collecting data from different sources. It involves identifying relevant data sources, extracting data, and organizing it for further analysis. Some common data acquisition methods include:

1. Web Scraping: Web scraping is the automated extraction of data from websites. It involves using specialized tools or libraries to scrape data from web pages, APIs, or online databases. Web scraping enables data scientists to gather large amounts of structured or unstructured data for analysis.

2. Public Datasets: Many organizations and institutions provide publicly available datasets for research and analysis purposes. These datasets cover various domains and can be accessed through online repositories, government websites, or data portals.

3. Surveys and Questionnaires: Surveys and questionnaires are effective methods for collecting specific data directly from individuals or target populations. Data scientists design survey questions, distribute them to respondents, and collect the responses for analysis.

4. Sensor Data: Sensors embedded in various devices or systems generate a vast amount of data. This includes data from IoT devices, environmental sensors, medical sensors, and more. Data scientists can acquire and process sensor data to gain insights and support decision-making.

Data Cleaning

Data cleaning, also known as data cleansing or data scrubbing, involves identifying and rectifying errors, inconsistencies, and inaccuracies in the dataset. The goal is to improve data quality and ensure that the data is reliable and ready for analysis. Common data cleaning tasks include:

1. Handling Missing Values: Missing values are a common issue in datasets. Data scientists need to identify missing values and decide how to handle them. This can involve imputing missing values using statistical techniques or making informed decisions based on the data's context.

2. Dealing with Outliers: Outliers are extreme values that deviate significantly from the rest of the data. They can impact the accuracy of statistical models and analysis. Data scientists need to identify outliers and decide whether to remove them, transform them, or handle them in a specific way based on the data and domain knowledge.

3. Standardizing and Normalizing Data: Data often comes in different scales and units. Standardizing and normalizing data ensure that different variables are on a similar scale and have comparable ranges. This step is important for models that rely on distance-based calculations or when comparing variables with different units.

4. Handling Duplicate Data: Duplicate data can occur due to data entry errors, system glitches, or merging multiple datasets. Removing duplicate records ensures data integrity and prevents biases in analysis and modeling.

5. Handling Inconsistent Data: Inconsistent data refers to variations in data formats, spellings, or coding schemes. Data cleaning involves standardizing data formats, resolving inconsistencies, and ensuring uniformity across the dataset.

6. Data Transformation: Data transformation involves converting variables or creating new derived variables to enhance the dataset's usefulness. This can include aggregating data, creating categorical variables, or applying mathematical or statistical transformations.

Data Quality Assessment

Data quality assessment is an important aspect of data cleaning. It involves evaluating the reliability, completeness, accuracy, and consistency of the data. Some common measures of data quality include:

1. Completeness: Assessing the presence of missing values and determining their impact on the dataset.

2. Accuracy: Verifying the correctness and precision of the data by comparing it with trusted sources or expert knowledge.

3. Consistency: Checking for inconsistencies or contradictions within the dataset or across different data sources.

4. Validity: Ensuring that the data conforms to predefined rules, constraints, or expectations.

5. Timeliness: Assessing the relevance and currency of the data in relation to the analysis or decision-making process.


Data acquisition and cleaning are critical steps in the data science workflow. Properly acquiring and cleaning data ensures that the subsequent analysis and modeling are based on reliable, accurate, and consistent data. By addressing issues such as missing values, outliers, and data inconsistencies, data scientists can improve data quality and increase the validity of the insights derived from the data.

If you liked the article, please explore our basket section filled with 15000+ objective type questions.