Data Science Fundamentals

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