Here’s a table detailing key components of Data Science and the technologies commonly used:

Data Science Component Description Technologies Used
Data Collection Gathering raw data from various sources. SQL, MongoDB, APIs, Web Scraping (BeautifulSoup, Scrapy), Google Sheets
Data Storage Storing data in a structured or unstructured format. MySQL, PostgreSQL, MongoDB, Hadoop HDFS, Amazon S3
Data Cleaning & Preprocessing Preparing data by handling missing values, normalizing, and transforming data. Pandas, NumPy, OpenRefine, Dask, Excel
Exploratory Data Analysis (EDA) Identifying patterns, anomalies, and insights from the data. Pandas, NumPy, Matplotlib, Seaborn, Tableau, Power BI, Excel
Data Visualization Creating visual representations of data. Matplotlib, Seaborn, Plotly, Tableau, Power BI, Excel
Statistical Analysis Applying statistical methods to interpret data. R, Python (SciPy, Statsmodels), SPSS, SAS
Machine Learning Using algorithms to build predictive models. Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost
Deep Learning Training complex models using neural networks. TensorFlow, Keras, PyTorch
Natural Language Processing (NLP) Analyzing and understanding human language data. NLTK, SpaCy, Hugging Face Transformers, Gensim, BERT
Big Data Processing Handling large volumes of data. Hadoop, Apache Spark, Apache Flink
Cloud Computing Utilizing remote servers for computation and storage. AWS (S3, EC2, Lambda), Google Cloud, Microsoft Azure
Model Deployment Making machine learning models available for production use. Flask, Docker, Kubernetes, AWS SageMaker, TensorFlow Serving
Data Ethics and Privacy Ensuring data is used responsibly and in compliance with regulations. GDPR, HIPAA Compliance Tools, Differential Privacy Tools
Collaboration & Version Control Managing code, collaboration, and versioning in data science projects. Git, GitHub, GitLab, Bitbucket

 This table gives an overview of the main components of data science and the corresponding  technologies used at each stage.