Data Collection
Gathering data from various sources such as databases, APIs, web scraping, and sensors.
Sources can include structured data (e.g., spreadsheets) or unstructured data (e.g., text, images).
Data Cleaning and Preprocessing
Ensuring data quality by handling missing values, removing duplicates, and correcting inconsistencies.
This step prepares the raw data for analysis.
Data Analysis
Applying statistical methods and exploratory techniques to uncover patterns and relationships in the data.
Machine Learning and Modeling
Building predictive models using machine learning algorithms like regression, classification, clustering, and deep learning.
Validating and fine-tuning models to improve their accuracy.
Data Visualization
Representing data insights visually using tools like graphs, charts, and dashboards.
Common tools include Tableau, Power BI, and Matplotlib.
Key Components of Data Science