Data Analysis
- Involves examining, cleaning, transforming, and modeling data to extract insights and support decisions.
- Ranges from simple visualization to complex statistical and machine learning techniques.
- Common steps include exploratory data analysis (EDA) and predictive modeling.
Definition
Section titled “Definition”Data analysis is the process of examining, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Explanation
Section titled “Explanation”Data analysis covers a spectrum of activities and techniques aimed at turning raw data into actionable information. It can be as simple as creating visualizations to reveal patterns or as advanced as applying statistical and machine learning methods to build predictive models. Exploratory data analysis (EDA) is typically the first step: it helps summarize and reveal the overall structure and patterns in a dataset, which guides further, more advanced analyses. Predictive modeling uses past data to forecast future outcomes and inform decisions. Across these activities, common tasks include visualizing distributions, assessing relationships between variables, and preparing data through cleaning and transformation.
Examples
Section titled “Examples”Exploratory Data Analysis (EDA)
Section titled “Exploratory Data Analysis (EDA)”Exploratory data analysis involves exploring and summarizing a dataset to better understand its overall structure and patterns. EDA is typically the first step in the data analysis process, as it helps to identify trends and relationships within the data that can be further explored using more advanced techniques. For instance, an EDA might involve creating scatter plots or histograms to visualize the distribution of different variables in a dataset, or performing a correlation analysis to identify the strength of the relationship between two variables.
Predictive Modeling
Section titled “Predictive Modeling”Predictive modeling involves using statistical and machine learning techniques to develop models that can predict future outcomes based on past data. Predictive modeling is often used in a variety of industries, including finance, healthcare, and retail, to help make more informed decisions and improve operational efficiency. For example, a predictive model might be used to forecast sales for a retail business, or to identify patients at risk of developing a particular disease.
Use cases
Section titled “Use cases”- Finance, healthcare, and retail industries commonly apply data analysis to make more informed decisions and improve operational efficiency.
- Examples include forecasting sales and identifying patients at risk of developing a particular disease.
Related terms
Section titled “Related terms”- Exploratory Data Analysis (EDA)
- Predictive modeling
- Data visualization
- Statistical analysis
- Correlation analysis
- Scatter plot
- Histogram
- Machine learning
- Data science (data science process)