Data analytics involves a systematic approach to analyzing data, aiming to extract useful information, draw conclusions, and support decision-making. Here’s how data analytics can analyze data effectively:
1. Data Collection
- Gathering Data: Collect data from various sources such as databases, spreadsheets, APIs, and sensors.
- Ensuring Data Quality: Ensure the data collected is accurate, relevant, and complete.
2. Data Cleaning
- Handling Missing Values: Fill in, remove, or estimate missing data points.
- Removing Duplicates: Identify and remove duplicate entries.
- Correcting Errors: Fix errors in data entries, such as typos or inconsistent formats.
3. Data Transformation
- Normalization: Adjust the data to a standard scale without distorting differences in the ranges of values.
- Aggregation: Summarize data to provide a clearer overview (e.g., total sales per month).
- Feature Engineering: Create new features or variables to help in analysis (e.g., extracting year from a date).
4. Exploratory Data Analysis (EDA)
- Descriptive Statistics: Calculate the mean, median, mode, standard deviation, and other statistical metrics.
- Visualization: Use graphs and charts (e.g., histograms, scatter plots, box plots) to understand data distributions and relationships.
- Identifying Patterns: Look for trends, outliers, and patterns in the data.
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