Data Science Fundamentals

Created by

@InspirationalBeryllium

Study Notes

Data Science

Data Visualization

Goal: to effectively communicate insights and patterns in data to stakeholders
Importance:
- Helps in exploratory data analysis and hypothesis generation
- Facilitates communication of results to non-technical stakeholders
- Enhances understanding of complex data
Types of visualizations:
- Univariate (single variable): histograms, box plots
- Bivariate (two variables): scatter plots, heatmaps
- Multivariate (multiple variables): parallel coordinates, radar charts
Best practices:
- Choose the right type of visualization for the data
- Avoid 3D visualizations and unnecessary embellishments
- Use color effectively to convey information
- Consider interactive visualizations for exploration

Big Data

Definition: large amounts of structured and unstructured data that exceed traditional processing capabilities
Characteristics:
- Volume: large amounts of data
- Velocity: high speed of data generation
- Variety: diverse types of data (structured, semi-structured, unstructured)
- Veracity: uncertainty and inconsistencies in data
Challenges:
- Storage and processing requirements
- Data quality and cleaning
- Scalability and parallel processing
Technologies:
- Hadoop ecosystem: HDFS, MapReduce, YARN
- NoSQL databases: HBase, Cassandra, MongoDB
- Distributed processing: Spark, Flink
Applications:
- Predictive analytics and machine learning
- Real-time analytics and streaming data
- Data warehousing and business intelligence

Description

Learn about the basics of data science, including data visualization, big data, and their applications in predictive analytics and business intelligence. Understand the importance of effective data visualization and the challenges of working with big data. Explore the technologies and tools used in data science.