Combines techniques from statistics, computer science, and domain-specific knowledge to analyze and interpret complex data sets.
Data Science
Data Collection:
Gathering data from various sources, such as databases, web scraping, APIs, sensors, or surveys.
Data can be structured (e.g., databases), semi-structured (e.g., JSON, XML), or unstructured (e.g., text, images, videos).
Data Preparation:
Data Cleaning: Removing errors, duplicates, and inconsistencies in the data.
Data Transformation: Converting data into a format suitable for analysis (e.g., normalization, encoding).
Feature Engineering: Creating new features or selecting relevant features to improve model performance.
Exploratory Data Analysis (EDA):
Descriptive Statistics: Summarizing data using measures like mean, median, mode, standard deviation.
Data Visualization: Using graphs and plots (e.g., histograms, scatter plots, box plots) to identify patterns, trends, and anomalies.
Correlation Analysis: Identifying relationships between variables.
Modeling:
Machine Learning: Applying algorithms to learn patterns from data and make predictions or classifications. Common algorithms include linear regression, decision trees, random forests, and neural networks.
Statistical Modeling: Using statistical methods to estimate relationships between variables (e.g., logistic regression, time series analysis).
Deep Learning: A subset of machine learning that uses neural networks with many layers to model complex patterns, particularly in large datasets.
Model Evaluation:
Performance Metrics: Assessing models using metrics such as accuracy, precision, recall, F1-score, RMSE (Root Mean Square Error), etc.
Cross-Validation: Dividing data into training and testing sets to evaluate model generalization.
Deployment:
Model Serving: Integrating the model into production systems, where it can be used to make real-time predictions or inform decisions.
Monitoring: Continuously tracking model performance and making updates as needed.
Data Visualization and Reporting:
Dashboards: Creating interactive dashboards to visualize key metrics and trends.
Reports: Generating reports to communicate findings to stakeholders.
Data Science Tools and Technologies:
Programming Languages: Python, R, SQL are commonly used.
Libraries and Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib.
Big Data Technologies: Hadoop, Spark, Hive, HBase for handling large-scale data.
Databases: SQL databases (MySQL, PostgreSQL) and NoSQL databases (MongoDB, Cassandra).
Cloud Platforms: AWS, Google Cloud, Azure for scalable data storage and processing.
Applications of Data Science
Business Intelligence: Analyzing business data to make informed decisions.
