Data Science and Big Data

[Buela_Vigneswaran]

Data Science and Big Data

1. Introduction to Data Science

• Definition:
  • Data Science involves collecting, processing, analyzing, and visualizing data to extract actionable insights.
• Key Components:
  • Data Collection → Data Cleaning → Data Analysis → Data Visualization → Decision Making.
• Applications:
  • Recommendation Systems, Fraud Detection, Predictive Analytics, Market Analysis.

2. Data Analysis

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• Exploratory Data Analysis (EDA):
  • Techniques to summarize data using statistical measures and visualizations.
  • Tools: Python (Pandas, Matplotlib, Seaborn), R.
• Statistical Methods:
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation.
  • Inferential Statistics: Hypothesis Testing, Confidence Intervals, Regression Analysis.

3. Data Visualization

• Tools:
  • Tableau, Power BI, Google Data Studio.
  • Python Libraries: Matplotlib, Seaborn, Plotly.
• Types of Visualizations:
  • Bar Charts, Line Graphs, Scatter Plots, Heatmaps, Pie Charts, Histograms.
• Best Practices:
  • Keep it simple and clear, choose appropriate visualizations for the data, and ensure accessibility.

4. Big Data

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• Definition:
  • Refers to datasets that are too large or complex to be processed using traditional methods.
• Key Characteristics (3Vs):
  • Volume: Massive amounts of data.
  • Velocity: High-speed data generation.
  • Variety: Different types of data (structured, unstructured, semi-structured).
• Big Data Technologies:
  • Hadoop, Spark, Apache Hive, Apache Kafka.

5. Machine Learning in Data Science

• Supervised Learning:
  • Predicting outcomes based on labeled data (e.g., Linear Regression, Random Forest).
• Unsupervised Learning:
  • Identifying patterns in unlabeled data (e.g., Clustering, PCA).
• Deep Learning:
  • Neural networks for handling large, complex datasets.

6. Data Engineering

• Data Pipelines:
  • Automating data collection, processing, and storage workflows.
• ETL (Extract, Transform, Load):
  • Extracting data from sources, transforming it into usable formats, and loading it into a database or warehouse.
• Data Warehousing:
  • Centralized storage systems for large datasets (e.g., Snowflake, Google BigQuery).

7. Tools and Technologies

• Programming Languages: Python, R, SQL.
• Big Data Frameworks: Hadoop, Spark.
• Cloud Platforms: AWS (Redshift, S3), Google Cloud (BigQuery, Dataflow), Microsoft Azure (Synapse).
• Data Science Libraries: Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch.